{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine Assay Matrix Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When employing TReNA, we are applying a solver to an assay matrix to infer relationships between transcription factors and target genes. Both the solver type and the assay matrix are mutable entities; there are currently 6 different solvers built into TReNA. The assay matrix can also be transformed in various ways. In this notebook, we will examine these two variables in an attempt to answer the following questions:\n",
    "\n",
    "1. How do the different solvers compare to one another on the same data?\n",
    "2. What is the effect of transforming the assay matrix in different ways?\n",
    "\n",
    "To this end, we will be using the ampAD data of 154 transcription factors and 278 samples, with MEF2C as the target gene. Our three solvers will be LASSO, Random Forest, and Bayes Spike. The 4 different data transformations we will apply here are: as-is (unchanged), log2-transformed, asinh-transformed, and voom-transformed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details on the Data\n",
    "\n",
    "For this exercise, we will be using RNAseq data gathered for Alzheimer's disease (AD) and related diseases [Original Paper Here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5058336/). These expression data are from samples taken during brain autopsies from 4 groups of subjects:\n",
    "1. Normal/control group, with no discernable cognitive impairment and no diagnosis of any neurodegnerative disease\n",
    "2. AD group, with cognitive impairment and a diagnosis of AD\n",
    "3. Progressive supranuclear palsy (PSP) group, with cognitive impairment and a diagnosis of PSP\n",
    "4. Pathelogical aging, with the presence of amyloid plaques associated with a diseased state, but with no discernible cognitive impairment. \n",
    "\n",
    "MEF2C is a gene identified through genome-wide association studies to be associated with protection from AD. To better understand which transcription factors are associated with MEF2C, and thus may be associated with AD, we will use TReNA to identify meaningful relationships. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation Characteristics\n",
    "\n",
    "Let us first get a handle on what our data look like under the 4 different transformations. \n",
    "\n",
    "1. The \"as-is\" data are fairly straightforward; we have RNAseq data in units of RPKM with expression levels concentrated near 0, but the range of the data is quite large. This skewed dataset may not be ideal (though we don't exactly have a standard to compare to), so one way to adjust that skewed nature is to do a log2 transformation.\n",
    "\n",
    "2. For each value in the matrix, the log2 transformation subtracts the minimum expression value in the matrix, adds a very small number (0.001) to prevent any attempt at taking log2(0), and then takes log2() of the value. As a result, the log2-transformed data have a much smaller range and appear much more Gaussian than the as-is data. They also cross over 0 into the negative range, a characteristic that differs from the original data.  \n",
    "\n",
    "3. The third data transformation is using asinh, the hyperbolic arcsine function. The actual math here is to simply take asinh() of every value in the matrix; the resulting distribution is scaled down, much like the log2-transformed data, but all values are positive. \n",
    "\n",
    "4. The fourth and final transformation is VOOM transformation, a technique that estimates the mean-variance relationship in the data and uses it to weigh each observation. The transformation is performed using the *voom()* function in the *limma* package from Biocondutor, which is documented [here](http://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf). The manuscript for the VOOM transformation can be found [here](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). \n",
    "\n",
    "For a better understanding of what these distributions look like, we can look at histograms of all 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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g2knXFYkMxH0kDK7XX3kTxoIPVAkekD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\nFFBAAQUUUEABBRRQQAEFFKiPwP8D+nFq+vFcakcAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Voom-Transformed Data”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the assay matrix (mtx.sub) and transform it 3 ways\n",
    "load(\"../extdata/ampAD.154genes.mef2cTFs.278samples.RData\")\n",
    "\n",
    "mtx.tmp <- mtx.sub - min(mtx.sub) + 0.001\n",
    "mtx.log2 <- log2(mtx.tmp)\n",
    "\n",
    "mtx.asinh <- asinh(mtx.sub)\n",
    "\n",
    "suppressMessages(library(limma))\n",
    "mtx.voom <- voom(mtx.sub)$E\n",
    "\n",
    "# Plot them all\n",
    "par(mfrow = c(4,1))\n",
    "par(family = \"sans\")\n",
    "hist(mtx.sub, main = \"As-is Data\")\n",
    "hist(mtx.log2, main = \"Log2-Transformed Data\")\n",
    "hist(mtx.asinh, main = \"Asinh-Transformed Data\")\n",
    "hist(mtx.voom, main = \"Voom-Transformed Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data distributions are quite dissimilar, so it follows that we might expect different results when using them, even if we're using the same solver. To explicitly quantify these distributions, we can also take Tukey's five number summary (min,1st quartile, median, 3rd quartile, max) of each one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As-is : 0 1.753137 12.34697 43.24747 1027.766 \n",
      " Log2 : -9.965784 0.8107618 3.626201 5.434577 10.0053 \n",
      "Asinh : 0 1.327453 3.208193 4.460219 7.62829 \n",
      " Voom : 6.379003 8.825687 11.33609 13.10218 17.47137 \n"
     ]
    }
   ],
   "source": [
    "cat(\"As-is :\",fivenum(mtx.sub),\"\\n\")\n",
    "cat(\" Log2 :\",fivenum(mtx.log2),\"\\n\")\n",
    "cat(\"Asinh :\",fivenum(mtx.asinh),\"\\n\")\n",
    "cat(\" Voom :\",fivenum(mtx.voom),\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we expected, the as-is data have a much wider distribution but are concentrated around small values. The log2 and asinh data both scale down the distribution in different ways, with the log2 transformation incoporating negative values and the asinh transformation finding its minimum at 0. \n",
    "\n",
    "Now that we've examined the distributions fairly thoroughly, let us move to using the solvers themselves to infer relationships between transcription factors and target genes. This will give us a better idea of how our distributions affect the solutions we infer and what we might want to be careful of in the future"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Distributions and Solvers in TReNA\n",
    "\n",
    "First things first, we'll go ahead and load our function; this function will do several things:\n",
    "\n",
    "1. Load the \"as-is\" data and create the 3 different matrices we've examined above\n",
    "2. For each of those 3 matrices, create and solve a TReNA object for each of 4 solvers (LASSO, Bayes Spike, Random Forest, Square Root LASSO) with MEF2C as the target gene. This means we'll have 12 total solutions. \n",
    "3. For each of the 12 solutions, pull out the top 10 genes, then take the union of those 9 sets of 10. This will give us a list of genes representing the best hits for each combination of distribution and solver\n",
    "3. Compile the scores of those genes from the 12 solutions into a unified table (tbl.all), along with the gene names and their Pearson correlations to MEF2C.\n",
    "\n",
    "Let's do that and produce our table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] --- Testing LASSO\n",
      "[1] --- Testing Bayes Spike\n",
      "[1] --- Testing Random Forest\n",
      "[1] --- Testing Square Root LASSO\n"
     ]
    }
   ],
   "source": [
    "# Source the desired function and generate the table\n",
    "suppressMessages(source(\"../utils/evaluateAllSolvers.R\"))\n",
    "tbl.all <- assess_ampAD154AllSolversAndDistributions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Just to get an idea of what we're looking at, here's the table. We can browse through this to see what the data look like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>lasso.as.is</th><th scope=col>lasso.log2</th><th scope=col>lasso.asinh</th><th scope=col>lasso.voom</th><th scope=col>bs.as.is</th><th scope=col>bs.log2</th><th scope=col>bs.asinh</th><th scope=col>bs.voom</th><th scope=col>rf.as.is</th><th scope=col>rf.log2</th><th scope=col>rf.asinh</th><th scope=col>rf.voom</th><th scope=col>sqrt.lasso.as.is</th><th scope=col>sqrt.lasso.log2</th><th scope=col>sqrt.lasso.asinh</th><th scope=col>sqrt.lasso.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF          </td><td>  0.77458827 </td><td> 0.134199069 </td><td> 0.209422903 </td><td> 0.24105628  </td><td> 5.930056e-01</td><td> 3.228768e-01</td><td> 8.583764e-05</td><td> 3.468772e-01</td><td>1266161.9489 </td><td>57.37132247  </td><td>25.91603826  </td><td>53.05247084  </td><td> 0.69519133  </td><td> 0.140776460 </td><td> 0.142896092 </td><td> 0.15897283  </td><td> 0.92323940  </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4        </td><td>  2.60534321 </td><td> 0.157190023 </td><td> 0.076155038 </td><td> 0.09551869  </td><td> 1.708536e+00</td><td> 1.832979e-01</td><td> 8.946250e-05</td><td> 1.708533e-01</td><td> 649234.9844 </td><td>46.37406567  </td><td>22.47867569  </td><td>48.94942356  </td><td> 3.47937884  </td><td> 0.190961358 </td><td> 0.158960329 </td><td> 0.17336532  </td><td> 0.90475987  </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1        </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-5.683068e-01</td><td>-1.444623e-06</td><td> 1.154825e-05</td><td>-3.633166e-05</td><td> 577058.4385 </td><td>21.19406994  </td><td>11.98698783  </td><td>27.98823933  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 0.86282704  </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2        </td><td>  0.13675630 </td><td> 0.216133895 </td><td> 0.228754703 </td><td> 0.26140384  </td><td> 1.035786e+00</td><td> 6.942847e-05</td><td> 3.566778e-01</td><td> 2.853092e-01</td><td> 424329.6976 </td><td>26.35567531  </td><td>14.22518618  </td><td>30.46469273  </td><td> 0.02139235  </td><td> 0.181298914 </td><td> 0.226526723 </td><td> 0.23814460  </td><td> 0.84232989  </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2         </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.009811994 </td><td> 0.00000000  </td><td> 8.928837e-01</td><td> 5.648769e-05</td><td> 7.409788e-05</td><td>-5.996884e-06</td><td> 137474.9788 </td><td> 5.41129360  </td><td> 2.34014879  </td><td> 7.35197201  </td><td> 0.08809962  </td><td> 0.008485763 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 0.82786857  </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2        </td><td>  2.61508513 </td><td> 0.106667287 </td><td> 0.120449138 </td><td> 0.13231032  </td><td> 3.972476e+00</td><td> 8.526235e-05</td><td> 2.309917e-01</td><td> 4.508482e-05</td><td> 112640.2790 </td><td> 3.91445249  </td><td> 2.97052657  </td><td> 3.80445761  </td><td> 2.44496515  </td><td> 0.101105760 </td><td> 0.106358127 </td><td> 0.10469561  </td><td> 0.81961721  </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2        </td><td>  0.69243601 </td><td> 0.104562826 </td><td> 0.115311122 </td><td> 0.09676352  </td><td> 1.086813e+00</td><td> 2.277099e-01</td><td> 1.056974e-01</td><td> 1.235520e-01</td><td>  84319.1960 </td><td> 5.12252917  </td><td> 2.25117903  </td><td> 4.60443070  </td><td> 1.00207756  </td><td> 0.100506784 </td><td> 0.094864545 </td><td> 0.08923052  </td><td> 0.77043615  </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX         </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-4.072208e-03</td><td> 1.835087e-07</td><td> 7.377633e-06</td><td> 1.398560e-05</td><td>  88685.5631 </td><td>10.12862043  </td><td> 3.22875065  </td><td> 8.62751278  </td><td> 1.18904754  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 0.73094500  </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX          </td><td> 11.26492428 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 3.241756e-01</td><td>-2.498712e-05</td><td> 6.947728e-05</td><td> 5.735278e-05</td><td>  53018.0228 </td><td> 1.43641155  </td><td> 0.71578023  </td><td> 0.86613053  </td><td>13.37342127  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 0.72419056  </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6         </td><td>  1.27950800 </td><td> 0.066352215 </td><td> 0.090529696 </td><td> 0.09315715  </td><td> 2.312552e+00</td><td> 6.697943e-05</td><td> 1.139972e-01</td><td> 1.365092e-01</td><td>  33707.1603 </td><td> 2.90733938  </td><td> 1.70273480  </td><td> 2.28153347  </td><td> 2.69414714  </td><td> 0.058348346 </td><td> 0.086557473 </td><td> 0.09288872  </td><td> 0.71461154  </td></tr>\n",
       "\t<tr><th scope=row>12</th><td>ESRRG        </td><td>  0.66926539 </td><td> 0.019431010 </td><td> 0.046640155 </td><td> 0.01141497  </td><td> 3.927342e-01</td><td> 3.936057e-05</td><td> 2.318925e-04</td><td> 7.762502e-06</td><td>  66430.6662 </td><td> 4.29280477  </td><td> 1.61084504  </td><td> 3.77395550  </td><td> 0.45898430  </td><td> 0.004540240 </td><td> 0.010904356 </td><td> 0.00000000  </td><td> 0.70903181  </td></tr>\n",
       "\t<tr><th scope=row>19</th><td>TSHZ3        </td><td>  0.00000000 </td><td> 0.101236743 </td><td> 0.064662784 </td><td> 0.04451921  </td><td>-1.089643e-02</td><td> 8.721712e-05</td><td> 1.673391e-01</td><td> 2.551630e-05</td><td>  23971.7861 </td><td> 6.25117359  </td><td> 3.70888147  </td><td> 9.28067022  </td><td> 0.00000000  </td><td> 0.103275043 </td><td> 0.119569865 </td><td> 0.09920060  </td><td> 0.70562897  </td></tr>\n",
       "\t<tr><th scope=row>38</th><td>POU6F2       </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-3.743652e-02</td><td> 1.548796e-01</td><td> 1.126459e-05</td><td>-6.461413e-06</td><td>   7735.8553 </td><td> 1.54216776  </td><td> 0.61268809  </td><td> 1.62421935  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 0.68969765  </td></tr>\n",
       "\t<tr><th scope=row>14</th><td>NFE2L2       </td><td> -0.32653069 </td><td>-0.015204118 </td><td>-0.057125918 </td><td>-0.04777479  </td><td>-7.803000e-01</td><td>-1.304394e-04</td><td>-8.658551e-05</td><td>-1.444340e-01</td><td>  16250.1818 </td><td> 0.62406681  </td><td> 0.26788921  </td><td> 1.05073252  </td><td>-0.01666623  </td><td>-0.010311681 </td><td>-0.001796439 </td><td>-0.01195379  </td><td>-0.63838196  </td></tr>\n",
       "\t<tr><th scope=row>17</th><td>CUX2         </td><td>  0.03676364 </td><td> 0.074610279 </td><td> 0.134597803 </td><td> 0.11559182  </td><td> 1.240175e+00</td><td> 1.096828e-01</td><td> 2.066298e-01</td><td> 6.543902e-05</td><td>  13627.7613 </td><td> 3.79983083  </td><td> 1.69602385  </td><td> 3.33980250  </td><td> 0.00000000  </td><td> 0.059819887 </td><td> 0.070841315 </td><td> 0.05577775  </td><td> 0.62765043  </td></tr>\n",
       "\t<tr><th scope=row>18</th><td>SOX12        </td><td> -0.02284905 </td><td>-0.065376926 </td><td>-0.009410002 </td><td> 0.00000000  </td><td> 2.385374e-02</td><td>-4.011697e-05</td><td>-2.834563e-01</td><td>-2.815619e-05</td><td>  75863.7094 </td><td> 1.74741861  </td><td> 0.71539459  </td><td> 1.06994890  </td><td>-0.05066787  </td><td>-0.062815144 </td><td>-0.063477372 </td><td>-0.09422693  </td><td>-0.62404571  </td></tr>\n",
       "\t<tr><th scope=row>10</th><td>HOMEZ        </td><td> -0.75952764 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-2.571221e-01</td><td>-2.773147e-05</td><td>-1.190609e-04</td><td>-2.070808e-04</td><td>   2075.6167 </td><td> 0.23303020  </td><td> 0.11305491  </td><td> 0.13354243  </td><td>-0.05711910  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.58326686  </td></tr>\n",
       "\t<tr><th scope=row>34</th><td>STAT5A       </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-5.317519e+00</td><td> 1.619035e-05</td><td> 5.634046e-05</td><td> 6.416900e-05</td><td>   2473.5968 </td><td> 0.70519199  </td><td> 0.07501966  </td><td> 0.34651510  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.57464748  </td></tr>\n",
       "\t<tr><th scope=row>21</th><td>FOXO4        </td><td>  0.00000000 </td><td>-0.059932873 </td><td>-0.038120414 </td><td>-0.09406223  </td><td>-7.525646e-02</td><td>-1.089069e-01</td><td>-7.967480e-05</td><td>-1.649238e-01</td><td> 100624.4622 </td><td> 2.53966391  </td><td> 1.35220598  </td><td> 4.10752152  </td><td> 0.00000000  </td><td>-0.060840837 </td><td>-0.054144051 </td><td>-0.09392238  </td><td>-0.55645498  </td></tr>\n",
       "\t<tr><th scope=row>16</th><td>STAT1        </td><td>  0.10044635 </td><td> 0.058977896 </td><td> 0.039676356 </td><td> 0.01249171  </td><td> 1.452155e-02</td><td> 1.550252e-04</td><td> 5.356998e-04</td><td>-7.697100e-06</td><td>  49426.5314 </td><td> 1.67592402  </td><td> 0.76834548  </td><td> 1.64999002  </td><td> 0.14023646  </td><td> 0.061726178 </td><td> 0.056424616 </td><td> 0.04170972  </td><td> 0.44791101  </td></tr>\n",
       "\t<tr><th scope=row>28</th><td>ZHX3         </td><td>  0.00000000 </td><td> 0.000000000 </td><td>-0.085656799 </td><td>-0.04603107  </td><td> 8.553841e-04</td><td>-3.468578e-01</td><td>-8.150376e-05</td><td>-1.474584e-04</td><td>   2775.8698 </td><td> 0.12460750  </td><td> 0.05507819  </td><td> 0.06762929  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.43410051  </td></tr>\n",
       "\t<tr><th scope=row>23</th><td>FOXD1        </td><td>  0.00000000 </td><td>-0.020514333 </td><td>-0.043780174 </td><td>-0.04197126  </td><td>-1.447640e-01</td><td>-8.077647e-05</td><td>-1.312632e-01</td><td>-1.355494e-04</td><td>   3940.9648 </td><td> 0.17796819  </td><td> 0.07402906  </td><td> 0.17825892  </td><td> 0.00000000  </td><td>-0.018612142 </td><td>-0.030152933 </td><td>-0.02430439  </td><td>-0.41483052  </td></tr>\n",
       "\t<tr><th scope=row>20</th><td>STAT5B       </td><td>  0.00000000 </td><td>-0.072874603 </td><td>-0.124676757 </td><td>-0.24554706  </td><td>-8.615242e-03</td><td>-1.028715e-04</td><td>-6.899009e-05</td><td>-5.172639e-03</td><td>   1962.9467 </td><td> 0.14380142  </td><td> 0.09304024  </td><td> 0.14962969  </td><td>-0.03142925  </td><td>-0.044682367 </td><td>-0.088535616 </td><td>-0.20937298  </td><td>-0.37452622  </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>NFE2L3       </td><td> -2.44526737 </td><td>-0.042077199 </td><td>-0.012117383 </td><td> 0.00000000  </td><td>-3.535628e+00</td><td>-6.050182e-04</td><td>-1.180878e-03</td><td> 8.473173e-06</td><td>   1780.2860 </td><td> 0.90328191  </td><td> 0.43550208  </td><td> 0.85687215  </td><td>-0.26084529  </td><td>-0.038226731 </td><td>-0.036547702 </td><td>-0.02745371  </td><td>-0.33931986  </td></tr>\n",
       "\t<tr><th scope=row>29</th><td>FOXL1        </td><td>  0.00000000 </td><td> 0.000000000 </td><td>-0.055243544 </td><td>-0.02501204  </td><td>-2.179699e+01</td><td>-1.023130e-05</td><td>-1.109603e-05</td><td>-3.033104e-04</td><td>    745.0412 </td><td> 0.04530632  </td><td> 0.01952745  </td><td> 0.06490894  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.28785265  </td></tr>\n",
       "\t<tr><th scope=row>30</th><td>RAX2         </td><td>  0.00000000 </td><td> 0.000000000 </td><td>-0.054872150 </td><td>-0.07062620  </td><td> 2.104629e+01</td><td>-2.984847e-05</td><td>-3.858662e-03</td><td>-1.693215e-03</td><td>  10191.6573 </td><td> 0.15838869  </td><td> 0.08666600  </td><td> 0.13254073  </td><td>-3.68593268  </td><td> 0.000000000 </td><td>-0.079070690 </td><td>-0.05256948  </td><td>-0.28106544  </td></tr>\n",
       "\t<tr><th scope=row>32</th><td>FOXQ1        </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.012481789 </td><td> 0.01484751  </td><td> 1.497996e-01</td><td> 1.024722e-04</td><td> 9.058601e-02</td><td> 1.950611e-05</td><td>   1912.3582 </td><td> 0.07622154  </td><td> 0.02708449  </td><td> 0.04248428  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.26418329  </td></tr>\n",
       "\t<tr><th scope=row>7</th><td>SOX15        </td><td> -2.00952281 </td><td>-0.047167519 </td><td>-0.081356107 </td><td>-0.04556199  </td><td>-5.808046e+00</td><td>-1.649264e-01</td><td>-2.072168e-04</td><td>-1.738663e-04</td><td>   3073.3194 </td><td> 0.09674146  </td><td> 0.04575365  </td><td> 0.17657099  </td><td>-0.20871913  </td><td>-0.034963116 </td><td>-0.033217226 </td><td> 0.00000000  </td><td>-0.26062515  </td></tr>\n",
       "\t<tr><th scope=row>13</th><td>ZFHX2        </td><td> -0.44426232 </td><td>-0.064360137 </td><td>-0.076382960 </td><td>-0.03506666  </td><td>-9.485212e-01</td><td>-1.449449e-01</td><td>-1.972800e-01</td><td>-5.449061e-05</td><td>   4242.0609 </td><td> 0.10108451  </td><td> 0.05349746  </td><td> 0.11198257  </td><td>-0.05434429  </td><td>-0.044781408 </td><td>-0.056811985 </td><td>-0.02709282  </td><td>-0.24922722  </td></tr>\n",
       "\t<tr><th scope=row>36</th><td>FOXD4L6      </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 2.001216e+01</td><td> 5.598696e-05</td><td>-3.493371e-05</td><td> 3.952855e-05</td><td>   2138.4440 </td><td> 0.38560704  </td><td> 0.08815874  </td><td> 0.26016141  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 0.24249449  </td></tr>\n",
       "\t<tr><th scope=row>39</th><td>SOX6         </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 1.117300e-02</td><td> 1.410110e-01</td><td> 2.635437e-04</td><td> 6.368482e-05</td><td>   1286.4206 </td><td> 0.02964059  </td><td> 0.02545302  </td><td> 0.07550225  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.18744712  </td></tr>\n",
       "\t<tr><th scope=row>31</th><td>FOXE3        </td><td>  0.00000000 </td><td> 0.000000000 </td><td>-0.023970663 </td><td> 0.00000000  </td><td>-3.409200e+01</td><td>-7.539808e-07</td><td>-2.472980e-04</td><td> 1.070491e-05</td><td>   1436.7861 </td><td> 0.09033598  </td><td> 0.02945399  </td><td> 0.08047401  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.15567610  </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>FOXD4        </td><td> -6.12834538 </td><td> 0.000000000 </td><td>-0.018794219 </td><td>-0.01121346  </td><td>-9.572871e-01</td><td>-2.277717e-04</td><td>-2.120234e-04</td><td>-1.566068e-04</td><td>   7525.4338 </td><td> 0.13565668  </td><td> 0.05591150  </td><td> 0.14653822  </td><td>-1.39091255  </td><td> 0.000000000 </td><td>-0.005870526 </td><td> 0.00000000  </td><td>-0.11598769  </td></tr>\n",
       "\t<tr><th scope=row>26</th><td>OTX2         </td><td>  0.00000000 </td><td> 0.000000000 </td><td>-0.104669744 </td><td>-0.06159170  </td><td>-8.116979e-02</td><td>-6.058279e-06</td><td> 1.893926e-05</td><td>-2.229342e-01</td><td>   1023.9351 </td><td> 0.11237214  </td><td> 0.05414230  </td><td> 0.15764033  </td><td> 0.00000000  </td><td> 0.000000000 </td><td>-0.035312214 </td><td>-0.01896358  </td><td>-0.11114757  </td></tr>\n",
       "\t<tr><th scope=row>25</th><td>FOXP3        </td><td>  0.00000000 </td><td> 0.004366561 </td><td> 0.061610938 </td><td> 0.02631893  </td><td> 8.865602e+00</td><td> 7.248455e-04</td><td> 1.555273e-02</td><td> 1.446537e-03</td><td>   1611.4674 </td><td> 0.06884909  </td><td> 0.02423162  </td><td> 0.08531106  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.10724617  </td></tr>\n",
       "\t<tr><th scope=row>37</th><td>ELF3         </td><td>  0.00000000 </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td> 7.590501e+00</td><td>-1.672171e-04</td><td> 3.211889e-04</td><td> 4.752853e-05</td><td>   3326.0816 </td><td> 0.14770484  </td><td> 0.06169517  </td><td> 0.18053208  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.10576117  </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>FOXD4L1      </td><td>-10.64033243 </td><td> 0.000000000 </td><td>-0.051396874 </td><td>-0.05015705  </td><td>-7.279581e+00</td><td>-5.366559e-05</td><td>-2.090862e-03</td><td>-1.683918e-03</td><td>   4436.9676 </td><td> 0.19584650  </td><td> 0.07824253  </td><td> 0.21936956  </td><td>-2.38450203  </td><td> 0.000000000 </td><td>-0.014643134 </td><td>-0.01698541  </td><td>-0.10509003  </td></tr>\n",
       "\t<tr><th scope=row>22</th><td>ZBTB16       </td><td>  0.00000000 </td><td>-0.020936732 </td><td>-0.035129792 </td><td>-0.07343806  </td><td>-1.758344e-01</td><td>-2.965382e-04</td><td>-1.843154e-04</td><td>-2.008173e-01</td><td>   1771.1880 </td><td> 0.07772086  </td><td> 0.03064144  </td><td> 0.06150837  </td><td>-0.02690810  </td><td>-0.011124576 </td><td>-0.014971105 </td><td>-0.04979472  </td><td> 0.06296197  </td></tr>\n",
       "\t<tr><th scope=row>24</th><td>ESR2         </td><td>  0.00000000 </td><td>-0.012150312 </td><td>-0.093702601 </td><td>-0.05702639  </td><td> 5.680673e-02</td><td>-1.076067e-01</td><td>-1.918502e-03</td><td>-1.278182e-01</td><td>   1254.3418 </td><td> 0.06494721  </td><td> 0.02666922  </td><td> 0.10061373  </td><td> 0.00000000  </td><td>-0.002660900 </td><td>-0.008303913 </td><td> 0.00000000  </td><td>-0.03964059  </td></tr>\n",
       "\t<tr><th scope=row>27</th><td>ESRRB        </td><td>  0.00000000 </td><td> 0.000000000 </td><td>-0.091401413 </td><td>-0.05857560  </td><td>-3.068364e+01</td><td>-1.117221e-03</td><td>-7.120074e-04</td><td>-1.932052e-04</td><td>   1860.4361 </td><td> 0.08006934  </td><td> 0.04726370  </td><td> 0.07232247  </td><td> 0.00000000  </td><td> 0.000000000 </td><td> 0.000000000 </td><td> 0.00000000  </td><td>-0.03485913  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & gene & lasso.as.is & lasso.log2 & lasso.asinh & lasso.voom & bs.as.is & bs.log2 & bs.asinh & bs.voom & rf.as.is & rf.log2 & rf.asinh & rf.voom & sqrt.lasso.as.is & sqrt.lasso.log2 & sqrt.lasso.asinh & sqrt.lasso.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF           &   0.77458827  &  0.134199069  &  0.209422903  &  0.24105628   &  5.930056e-01 &  3.228768e-01 &  8.583764e-05 &  3.468772e-01 & 1266161.9489  & 57.37132247   & 25.91603826   & 53.05247084   &  0.69519133   &  0.140776460  &  0.142896092  &  0.15897283   &  0.92323940  \\\\\n",
       "\t5 & STAT4         &   2.60534321  &  0.157190023  &  0.076155038  &  0.09551869   &  1.708536e+00 &  1.832979e-01 &  8.946250e-05 &  1.708533e-01 &  649234.9844  & 46.37406567   & 22.47867569   & 48.94942356   &  3.47937884   &  0.190961358  &  0.158960329  &  0.17336532   &  0.90475987  \\\\\n",
       "\t35 & SATB1         &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   & -5.683068e-01 & -1.444623e-06 &  1.154825e-05 & -3.633166e-05 &  577058.4385  & 21.19406994   & 11.98698783   & 27.98823933   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   &  0.86282704  \\\\\n",
       "\t15 & SATB2         &   0.13675630  &  0.216133895  &  0.228754703  &  0.26140384   &  1.035786e+00 &  6.942847e-05 &  3.566778e-01 &  2.853092e-01 &  424329.6976  & 26.35567531   & 14.22518618   & 30.46469273   &  0.02139235   &  0.181298914  &  0.226526723  &  0.23814460   &  0.84232989  \\\\\n",
       "\t33 & ATF2          &   0.00000000  &  0.000000000  &  0.009811994  &  0.00000000   &  8.928837e-01 &  5.648769e-05 &  7.409788e-05 & -5.996884e-06 &  137474.9788  &  5.41129360   &  2.34014879   &  7.35197201   &  0.08809962   &  0.008485763  &  0.000000000  &  0.00000000   &  0.82786857  \\\\\n",
       "\t4 & FOXP2         &   2.61508513  &  0.106667287  &  0.120449138  &  0.13231032   &  3.972476e+00 &  8.526235e-05 &  2.309917e-01 &  4.508482e-05 &  112640.2790  &  3.91445249   &  2.97052657   &  3.80445761   &  2.44496515   &  0.101105760  &  0.106358127  &  0.10469561   &  0.81961721  \\\\\n",
       "\t11 & TSHZ2         &   0.69243601  &  0.104562826  &  0.115311122  &  0.09676352   &  1.086813e+00 &  2.277099e-01 &  1.056974e-01 &  1.235520e-01 &   84319.1960  &  5.12252917   &  2.25117903   &  4.60443070   &  1.00207756   &  0.100506784  &  0.094864545  &  0.08923052   &  0.77043615  \\\\\n",
       "\t40 & DRGX          &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   & -4.072208e-03 &  1.835087e-07 &  7.377633e-06 &  1.398560e-05 &   88685.5631  & 10.12862043   &  3.22875065   &  8.62751278   &  1.18904754   &  0.000000000  &  0.000000000  &  0.00000000   &  0.73094500  \\\\\n",
       "\t1 & HDX           &  11.26492428  &  0.000000000  &  0.000000000  &  0.00000000   &  3.241756e-01 & -2.498712e-05 &  6.947728e-05 &  5.735278e-05 &   53018.0228  &  1.43641155   &  0.71578023   &  0.86613053   & 13.37342127   &  0.000000000  &  0.000000000  &  0.00000000   &  0.72419056  \\\\\n",
       "\t8 & LHX6          &   1.27950800  &  0.066352215  &  0.090529696  &  0.09315715   &  2.312552e+00 &  6.697943e-05 &  1.139972e-01 &  1.365092e-01 &   33707.1603  &  2.90733938   &  1.70273480   &  2.28153347   &  2.69414714   &  0.058348346  &  0.086557473  &  0.09288872   &  0.71461154  \\\\\n",
       "\t12 & ESRRG         &   0.66926539  &  0.019431010  &  0.046640155  &  0.01141497   &  3.927342e-01 &  3.936057e-05 &  2.318925e-04 &  7.762502e-06 &   66430.6662  &  4.29280477   &  1.61084504   &  3.77395550   &  0.45898430   &  0.004540240  &  0.010904356  &  0.00000000   &  0.70903181  \\\\\n",
       "\t19 & TSHZ3         &   0.00000000  &  0.101236743  &  0.064662784  &  0.04451921   & -1.089643e-02 &  8.721712e-05 &  1.673391e-01 &  2.551630e-05 &   23971.7861  &  6.25117359   &  3.70888147   &  9.28067022   &  0.00000000   &  0.103275043  &  0.119569865  &  0.09920060   &  0.70562897  \\\\\n",
       "\t38 & POU6F2        &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   & -3.743652e-02 &  1.548796e-01 &  1.126459e-05 & -6.461413e-06 &    7735.8553  &  1.54216776   &  0.61268809   &  1.62421935   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   &  0.68969765  \\\\\n",
       "\t14 & NFE2L2        &  -0.32653069  & -0.015204118  & -0.057125918  & -0.04777479   & -7.803000e-01 & -1.304394e-04 & -8.658551e-05 & -1.444340e-01 &   16250.1818  &  0.62406681   &  0.26788921   &  1.05073252   & -0.01666623   & -0.010311681  & -0.001796439  & -0.01195379   & -0.63838196  \\\\\n",
       "\t17 & CUX2          &   0.03676364  &  0.074610279  &  0.134597803  &  0.11559182   &  1.240175e+00 &  1.096828e-01 &  2.066298e-01 &  6.543902e-05 &   13627.7613  &  3.79983083   &  1.69602385   &  3.33980250   &  0.00000000   &  0.059819887  &  0.070841315  &  0.05577775   &  0.62765043  \\\\\n",
       "\t18 & SOX12         &  -0.02284905  & -0.065376926  & -0.009410002  &  0.00000000   &  2.385374e-02 & -4.011697e-05 & -2.834563e-01 & -2.815619e-05 &   75863.7094  &  1.74741861   &  0.71539459   &  1.06994890   & -0.05066787   & -0.062815144  & -0.063477372  & -0.09422693   & -0.62404571  \\\\\n",
       "\t10 & HOMEZ         &  -0.75952764  &  0.000000000  &  0.000000000  &  0.00000000   & -2.571221e-01 & -2.773147e-05 & -1.190609e-04 & -2.070808e-04 &    2075.6167  &  0.23303020   &  0.11305491   &  0.13354243   & -0.05711910   &  0.000000000  &  0.000000000  &  0.00000000   & -0.58326686  \\\\\n",
       "\t34 & STAT5A        &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   & -5.317519e+00 &  1.619035e-05 &  5.634046e-05 &  6.416900e-05 &    2473.5968  &  0.70519199   &  0.07501966   &  0.34651510   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.57464748  \\\\\n",
       "\t21 & FOXO4         &   0.00000000  & -0.059932873  & -0.038120414  & -0.09406223   & -7.525646e-02 & -1.089069e-01 & -7.967480e-05 & -1.649238e-01 &  100624.4622  &  2.53966391   &  1.35220598   &  4.10752152   &  0.00000000   & -0.060840837  & -0.054144051  & -0.09392238   & -0.55645498  \\\\\n",
       "\t16 & STAT1         &   0.10044635  &  0.058977896  &  0.039676356  &  0.01249171   &  1.452155e-02 &  1.550252e-04 &  5.356998e-04 & -7.697100e-06 &   49426.5314  &  1.67592402   &  0.76834548   &  1.64999002   &  0.14023646   &  0.061726178  &  0.056424616  &  0.04170972   &  0.44791101  \\\\\n",
       "\t28 & ZHX3          &   0.00000000  &  0.000000000  & -0.085656799  & -0.04603107   &  8.553841e-04 & -3.468578e-01 & -8.150376e-05 & -1.474584e-04 &    2775.8698  &  0.12460750   &  0.05507819   &  0.06762929   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.43410051  \\\\\n",
       "\t23 & FOXD1         &   0.00000000  & -0.020514333  & -0.043780174  & -0.04197126   & -1.447640e-01 & -8.077647e-05 & -1.312632e-01 & -1.355494e-04 &    3940.9648  &  0.17796819   &  0.07402906   &  0.17825892   &  0.00000000   & -0.018612142  & -0.030152933  & -0.02430439   & -0.41483052  \\\\\n",
       "\t20 & STAT5B        &   0.00000000  & -0.072874603  & -0.124676757  & -0.24554706   & -8.615242e-03 & -1.028715e-04 & -6.899009e-05 & -5.172639e-03 &    1962.9467  &  0.14380142   &  0.09304024   &  0.14962969   & -0.03142925   & -0.044682367  & -0.088535616  & -0.20937298   & -0.37452622  \\\\\n",
       "\t6 & NFE2L3        &  -2.44526737  & -0.042077199  & -0.012117383  &  0.00000000   & -3.535628e+00 & -6.050182e-04 & -1.180878e-03 &  8.473173e-06 &    1780.2860  &  0.90328191   &  0.43550208   &  0.85687215   & -0.26084529   & -0.038226731  & -0.036547702  & -0.02745371   & -0.33931986  \\\\\n",
       "\t29 & FOXL1         &   0.00000000  &  0.000000000  & -0.055243544  & -0.02501204   & -2.179699e+01 & -1.023130e-05 & -1.109603e-05 & -3.033104e-04 &     745.0412  &  0.04530632   &  0.01952745   &  0.06490894   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.28785265  \\\\\n",
       "\t30 & RAX2          &   0.00000000  &  0.000000000  & -0.054872150  & -0.07062620   &  2.104629e+01 & -2.984847e-05 & -3.858662e-03 & -1.693215e-03 &   10191.6573  &  0.15838869   &  0.08666600   &  0.13254073   & -3.68593268   &  0.000000000  & -0.079070690  & -0.05256948   & -0.28106544  \\\\\n",
       "\t32 & FOXQ1         &   0.00000000  &  0.000000000  &  0.012481789  &  0.01484751   &  1.497996e-01 &  1.024722e-04 &  9.058601e-02 &  1.950611e-05 &    1912.3582  &  0.07622154   &  0.02708449   &  0.04248428   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.26418329  \\\\\n",
       "\t7 & SOX15         &  -2.00952281  & -0.047167519  & -0.081356107  & -0.04556199   & -5.808046e+00 & -1.649264e-01 & -2.072168e-04 & -1.738663e-04 &    3073.3194  &  0.09674146   &  0.04575365   &  0.17657099   & -0.20871913   & -0.034963116  & -0.033217226  &  0.00000000   & -0.26062515  \\\\\n",
       "\t13 & ZFHX2         &  -0.44426232  & -0.064360137  & -0.076382960  & -0.03506666   & -9.485212e-01 & -1.449449e-01 & -1.972800e-01 & -5.449061e-05 &    4242.0609  &  0.10108451   &  0.05349746   &  0.11198257   & -0.05434429   & -0.044781408  & -0.056811985  & -0.02709282   & -0.24922722  \\\\\n",
       "\t36 & FOXD4L6       &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   &  2.001216e+01 &  5.598696e-05 & -3.493371e-05 &  3.952855e-05 &    2138.4440  &  0.38560704   &  0.08815874   &  0.26016141   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   &  0.24249449  \\\\\n",
       "\t39 & SOX6          &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   &  1.117300e-02 &  1.410110e-01 &  2.635437e-04 &  6.368482e-05 &    1286.4206  &  0.02964059   &  0.02545302   &  0.07550225   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.18744712  \\\\\n",
       "\t31 & FOXE3         &   0.00000000  &  0.000000000  & -0.023970663  &  0.00000000   & -3.409200e+01 & -7.539808e-07 & -2.472980e-04 &  1.070491e-05 &    1436.7861  &  0.09033598   &  0.02945399   &  0.08047401   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.15567610  \\\\\n",
       "\t3 & FOXD4         &  -6.12834538  &  0.000000000  & -0.018794219  & -0.01121346   & -9.572871e-01 & -2.277717e-04 & -2.120234e-04 & -1.566068e-04 &    7525.4338  &  0.13565668   &  0.05591150   &  0.14653822   & -1.39091255   &  0.000000000  & -0.005870526  &  0.00000000   & -0.11598769  \\\\\n",
       "\t26 & OTX2          &   0.00000000  &  0.000000000  & -0.104669744  & -0.06159170   & -8.116979e-02 & -6.058279e-06 &  1.893926e-05 & -2.229342e-01 &    1023.9351  &  0.11237214   &  0.05414230   &  0.15764033   &  0.00000000   &  0.000000000  & -0.035312214  & -0.01896358   & -0.11114757  \\\\\n",
       "\t25 & FOXP3         &   0.00000000  &  0.004366561  &  0.061610938  &  0.02631893   &  8.865602e+00 &  7.248455e-04 &  1.555273e-02 &  1.446537e-03 &    1611.4674  &  0.06884909   &  0.02423162   &  0.08531106   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.10724617  \\\\\n",
       "\t37 & ELF3          &   0.00000000  &  0.000000000  &  0.000000000  &  0.00000000   &  7.590501e+00 & -1.672171e-04 &  3.211889e-04 &  4.752853e-05 &    3326.0816  &  0.14770484   &  0.06169517   &  0.18053208   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.10576117  \\\\\n",
       "\t2 & FOXD4L1       & -10.64033243  &  0.000000000  & -0.051396874  & -0.05015705   & -7.279581e+00 & -5.366559e-05 & -2.090862e-03 & -1.683918e-03 &    4436.9676  &  0.19584650   &  0.07824253   &  0.21936956   & -2.38450203   &  0.000000000  & -0.014643134  & -0.01698541   & -0.10509003  \\\\\n",
       "\t22 & ZBTB16        &   0.00000000  & -0.020936732  & -0.035129792  & -0.07343806   & -1.758344e-01 & -2.965382e-04 & -1.843154e-04 & -2.008173e-01 &    1771.1880  &  0.07772086   &  0.03064144   &  0.06150837   & -0.02690810   & -0.011124576  & -0.014971105  & -0.04979472   &  0.06296197  \\\\\n",
       "\t24 & ESR2          &   0.00000000  & -0.012150312  & -0.093702601  & -0.05702639   &  5.680673e-02 & -1.076067e-01 & -1.918502e-03 & -1.278182e-01 &    1254.3418  &  0.06494721   &  0.02666922   &  0.10061373   &  0.00000000   & -0.002660900  & -0.008303913  &  0.00000000   & -0.03964059  \\\\\n",
       "\t27 & ESRRB         &   0.00000000  &  0.000000000  & -0.091401413  & -0.05857560   & -3.068364e+01 & -1.117221e-03 & -7.120074e-04 & -1.932052e-04 &    1860.4361  &  0.08006934   &  0.04726370   &  0.07232247   &  0.00000000   &  0.000000000  &  0.000000000  &  0.00000000   & -0.03485913  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | bs.as.is | bs.log2 | bs.asinh | bs.voom | rf.as.is | rf.log2 | rf.asinh | rf.voom | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF           |   0.77458827  |  0.134199069  |  0.209422903  |  0.24105628   |  5.930056e-01 |  3.228768e-01 |  8.583764e-05 |  3.468772e-01 | 1266161.9489  | 57.37132247   | 25.91603826   | 53.05247084   |  0.69519133   |  0.140776460  |  0.142896092  |  0.15897283   |  0.92323940   | \n",
       "| 5 | STAT4         |   2.60534321  |  0.157190023  |  0.076155038  |  0.09551869   |  1.708536e+00 |  1.832979e-01 |  8.946250e-05 |  1.708533e-01 |  649234.9844  | 46.37406567   | 22.47867569   | 48.94942356   |  3.47937884   |  0.190961358  |  0.158960329  |  0.17336532   |  0.90475987   | \n",
       "| 35 | SATB1         |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   | -5.683068e-01 | -1.444623e-06 |  1.154825e-05 | -3.633166e-05 |  577058.4385  | 21.19406994   | 11.98698783   | 27.98823933   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   |  0.86282704   | \n",
       "| 15 | SATB2         |   0.13675630  |  0.216133895  |  0.228754703  |  0.26140384   |  1.035786e+00 |  6.942847e-05 |  3.566778e-01 |  2.853092e-01 |  424329.6976  | 26.35567531   | 14.22518618   | 30.46469273   |  0.02139235   |  0.181298914  |  0.226526723  |  0.23814460   |  0.84232989   | \n",
       "| 33 | ATF2          |   0.00000000  |  0.000000000  |  0.009811994  |  0.00000000   |  8.928837e-01 |  5.648769e-05 |  7.409788e-05 | -5.996884e-06 |  137474.9788  |  5.41129360   |  2.34014879   |  7.35197201   |  0.08809962   |  0.008485763  |  0.000000000  |  0.00000000   |  0.82786857   | \n",
       "| 4 | FOXP2         |   2.61508513  |  0.106667287  |  0.120449138  |  0.13231032   |  3.972476e+00 |  8.526235e-05 |  2.309917e-01 |  4.508482e-05 |  112640.2790  |  3.91445249   |  2.97052657   |  3.80445761   |  2.44496515   |  0.101105760  |  0.106358127  |  0.10469561   |  0.81961721   | \n",
       "| 11 | TSHZ2         |   0.69243601  |  0.104562826  |  0.115311122  |  0.09676352   |  1.086813e+00 |  2.277099e-01 |  1.056974e-01 |  1.235520e-01 |   84319.1960  |  5.12252917   |  2.25117903   |  4.60443070   |  1.00207756   |  0.100506784  |  0.094864545  |  0.08923052   |  0.77043615   | \n",
       "| 40 | DRGX          |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   | -4.072208e-03 |  1.835087e-07 |  7.377633e-06 |  1.398560e-05 |   88685.5631  | 10.12862043   |  3.22875065   |  8.62751278   |  1.18904754   |  0.000000000  |  0.000000000  |  0.00000000   |  0.73094500   | \n",
       "| 1 | HDX           |  11.26492428  |  0.000000000  |  0.000000000  |  0.00000000   |  3.241756e-01 | -2.498712e-05 |  6.947728e-05 |  5.735278e-05 |   53018.0228  |  1.43641155   |  0.71578023   |  0.86613053   | 13.37342127   |  0.000000000  |  0.000000000  |  0.00000000   |  0.72419056   | \n",
       "| 8 | LHX6          |   1.27950800  |  0.066352215  |  0.090529696  |  0.09315715   |  2.312552e+00 |  6.697943e-05 |  1.139972e-01 |  1.365092e-01 |   33707.1603  |  2.90733938   |  1.70273480   |  2.28153347   |  2.69414714   |  0.058348346  |  0.086557473  |  0.09288872   |  0.71461154   | \n",
       "| 12 | ESRRG         |   0.66926539  |  0.019431010  |  0.046640155  |  0.01141497   |  3.927342e-01 |  3.936057e-05 |  2.318925e-04 |  7.762502e-06 |   66430.6662  |  4.29280477   |  1.61084504   |  3.77395550   |  0.45898430   |  0.004540240  |  0.010904356  |  0.00000000   |  0.70903181   | \n",
       "| 19 | TSHZ3         |   0.00000000  |  0.101236743  |  0.064662784  |  0.04451921   | -1.089643e-02 |  8.721712e-05 |  1.673391e-01 |  2.551630e-05 |   23971.7861  |  6.25117359   |  3.70888147   |  9.28067022   |  0.00000000   |  0.103275043  |  0.119569865  |  0.09920060   |  0.70562897   | \n",
       "| 38 | POU6F2        |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   | -3.743652e-02 |  1.548796e-01 |  1.126459e-05 | -6.461413e-06 |    7735.8553  |  1.54216776   |  0.61268809   |  1.62421935   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   |  0.68969765   | \n",
       "| 14 | NFE2L2        |  -0.32653069  | -0.015204118  | -0.057125918  | -0.04777479   | -7.803000e-01 | -1.304394e-04 | -8.658551e-05 | -1.444340e-01 |   16250.1818  |  0.62406681   |  0.26788921   |  1.05073252   | -0.01666623   | -0.010311681  | -0.001796439  | -0.01195379   | -0.63838196   | \n",
       "| 17 | CUX2          |   0.03676364  |  0.074610279  |  0.134597803  |  0.11559182   |  1.240175e+00 |  1.096828e-01 |  2.066298e-01 |  6.543902e-05 |   13627.7613  |  3.79983083   |  1.69602385   |  3.33980250   |  0.00000000   |  0.059819887  |  0.070841315  |  0.05577775   |  0.62765043   | \n",
       "| 18 | SOX12         |  -0.02284905  | -0.065376926  | -0.009410002  |  0.00000000   |  2.385374e-02 | -4.011697e-05 | -2.834563e-01 | -2.815619e-05 |   75863.7094  |  1.74741861   |  0.71539459   |  1.06994890   | -0.05066787   | -0.062815144  | -0.063477372  | -0.09422693   | -0.62404571   | \n",
       "| 10 | HOMEZ         |  -0.75952764  |  0.000000000  |  0.000000000  |  0.00000000   | -2.571221e-01 | -2.773147e-05 | -1.190609e-04 | -2.070808e-04 |    2075.6167  |  0.23303020   |  0.11305491   |  0.13354243   | -0.05711910   |  0.000000000  |  0.000000000  |  0.00000000   | -0.58326686   | \n",
       "| 34 | STAT5A        |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   | -5.317519e+00 |  1.619035e-05 |  5.634046e-05 |  6.416900e-05 |    2473.5968  |  0.70519199   |  0.07501966   |  0.34651510   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.57464748   | \n",
       "| 21 | FOXO4         |   0.00000000  | -0.059932873  | -0.038120414  | -0.09406223   | -7.525646e-02 | -1.089069e-01 | -7.967480e-05 | -1.649238e-01 |  100624.4622  |  2.53966391   |  1.35220598   |  4.10752152   |  0.00000000   | -0.060840837  | -0.054144051  | -0.09392238   | -0.55645498   | \n",
       "| 16 | STAT1         |   0.10044635  |  0.058977896  |  0.039676356  |  0.01249171   |  1.452155e-02 |  1.550252e-04 |  5.356998e-04 | -7.697100e-06 |   49426.5314  |  1.67592402   |  0.76834548   |  1.64999002   |  0.14023646   |  0.061726178  |  0.056424616  |  0.04170972   |  0.44791101   | \n",
       "| 28 | ZHX3          |   0.00000000  |  0.000000000  | -0.085656799  | -0.04603107   |  8.553841e-04 | -3.468578e-01 | -8.150376e-05 | -1.474584e-04 |    2775.8698  |  0.12460750   |  0.05507819   |  0.06762929   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.43410051   | \n",
       "| 23 | FOXD1         |   0.00000000  | -0.020514333  | -0.043780174  | -0.04197126   | -1.447640e-01 | -8.077647e-05 | -1.312632e-01 | -1.355494e-04 |    3940.9648  |  0.17796819   |  0.07402906   |  0.17825892   |  0.00000000   | -0.018612142  | -0.030152933  | -0.02430439   | -0.41483052   | \n",
       "| 20 | STAT5B        |   0.00000000  | -0.072874603  | -0.124676757  | -0.24554706   | -8.615242e-03 | -1.028715e-04 | -6.899009e-05 | -5.172639e-03 |    1962.9467  |  0.14380142   |  0.09304024   |  0.14962969   | -0.03142925   | -0.044682367  | -0.088535616  | -0.20937298   | -0.37452622   | \n",
       "| 6 | NFE2L3        |  -2.44526737  | -0.042077199  | -0.012117383  |  0.00000000   | -3.535628e+00 | -6.050182e-04 | -1.180878e-03 |  8.473173e-06 |    1780.2860  |  0.90328191   |  0.43550208   |  0.85687215   | -0.26084529   | -0.038226731  | -0.036547702  | -0.02745371   | -0.33931986   | \n",
       "| 29 | FOXL1         |   0.00000000  |  0.000000000  | -0.055243544  | -0.02501204   | -2.179699e+01 | -1.023130e-05 | -1.109603e-05 | -3.033104e-04 |     745.0412  |  0.04530632   |  0.01952745   |  0.06490894   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.28785265   | \n",
       "| 30 | RAX2          |   0.00000000  |  0.000000000  | -0.054872150  | -0.07062620   |  2.104629e+01 | -2.984847e-05 | -3.858662e-03 | -1.693215e-03 |   10191.6573  |  0.15838869   |  0.08666600   |  0.13254073   | -3.68593268   |  0.000000000  | -0.079070690  | -0.05256948   | -0.28106544   | \n",
       "| 32 | FOXQ1         |   0.00000000  |  0.000000000  |  0.012481789  |  0.01484751   |  1.497996e-01 |  1.024722e-04 |  9.058601e-02 |  1.950611e-05 |    1912.3582  |  0.07622154   |  0.02708449   |  0.04248428   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.26418329   | \n",
       "| 7 | SOX15         |  -2.00952281  | -0.047167519  | -0.081356107  | -0.04556199   | -5.808046e+00 | -1.649264e-01 | -2.072168e-04 | -1.738663e-04 |    3073.3194  |  0.09674146   |  0.04575365   |  0.17657099   | -0.20871913   | -0.034963116  | -0.033217226  |  0.00000000   | -0.26062515   | \n",
       "| 13 | ZFHX2         |  -0.44426232  | -0.064360137  | -0.076382960  | -0.03506666   | -9.485212e-01 | -1.449449e-01 | -1.972800e-01 | -5.449061e-05 |    4242.0609  |  0.10108451   |  0.05349746   |  0.11198257   | -0.05434429   | -0.044781408  | -0.056811985  | -0.02709282   | -0.24922722   | \n",
       "| 36 | FOXD4L6       |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   |  2.001216e+01 |  5.598696e-05 | -3.493371e-05 |  3.952855e-05 |    2138.4440  |  0.38560704   |  0.08815874   |  0.26016141   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   |  0.24249449   | \n",
       "| 39 | SOX6          |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   |  1.117300e-02 |  1.410110e-01 |  2.635437e-04 |  6.368482e-05 |    1286.4206  |  0.02964059   |  0.02545302   |  0.07550225   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.18744712   | \n",
       "| 31 | FOXE3         |   0.00000000  |  0.000000000  | -0.023970663  |  0.00000000   | -3.409200e+01 | -7.539808e-07 | -2.472980e-04 |  1.070491e-05 |    1436.7861  |  0.09033598   |  0.02945399   |  0.08047401   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.15567610   | \n",
       "| 3 | FOXD4         |  -6.12834538  |  0.000000000  | -0.018794219  | -0.01121346   | -9.572871e-01 | -2.277717e-04 | -2.120234e-04 | -1.566068e-04 |    7525.4338  |  0.13565668   |  0.05591150   |  0.14653822   | -1.39091255   |  0.000000000  | -0.005870526  |  0.00000000   | -0.11598769   | \n",
       "| 26 | OTX2          |   0.00000000  |  0.000000000  | -0.104669744  | -0.06159170   | -8.116979e-02 | -6.058279e-06 |  1.893926e-05 | -2.229342e-01 |    1023.9351  |  0.11237214   |  0.05414230   |  0.15764033   |  0.00000000   |  0.000000000  | -0.035312214  | -0.01896358   | -0.11114757   | \n",
       "| 25 | FOXP3         |   0.00000000  |  0.004366561  |  0.061610938  |  0.02631893   |  8.865602e+00 |  7.248455e-04 |  1.555273e-02 |  1.446537e-03 |    1611.4674  |  0.06884909   |  0.02423162   |  0.08531106   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.10724617   | \n",
       "| 37 | ELF3          |   0.00000000  |  0.000000000  |  0.000000000  |  0.00000000   |  7.590501e+00 | -1.672171e-04 |  3.211889e-04 |  4.752853e-05 |    3326.0816  |  0.14770484   |  0.06169517   |  0.18053208   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.10576117   | \n",
       "| 2 | FOXD4L1       | -10.64033243  |  0.000000000  | -0.051396874  | -0.05015705   | -7.279581e+00 | -5.366559e-05 | -2.090862e-03 | -1.683918e-03 |    4436.9676  |  0.19584650   |  0.07824253   |  0.21936956   | -2.38450203   |  0.000000000  | -0.014643134  | -0.01698541   | -0.10509003   | \n",
       "| 22 | ZBTB16        |   0.00000000  | -0.020936732  | -0.035129792  | -0.07343806   | -1.758344e-01 | -2.965382e-04 | -1.843154e-04 | -2.008173e-01 |    1771.1880  |  0.07772086   |  0.03064144   |  0.06150837   | -0.02690810   | -0.011124576  | -0.014971105  | -0.04979472   |  0.06296197   | \n",
       "| 24 | ESR2          |   0.00000000  | -0.012150312  | -0.093702601  | -0.05702639   |  5.680673e-02 | -1.076067e-01 | -1.918502e-03 | -1.278182e-01 |    1254.3418  |  0.06494721   |  0.02666922   |  0.10061373   |  0.00000000   | -0.002660900  | -0.008303913  |  0.00000000   | -0.03964059   | \n",
       "| 27 | ESRRB         |   0.00000000  |  0.000000000  | -0.091401413  | -0.05857560   | -3.068364e+01 | -1.117221e-03 | -7.120074e-04 | -1.932052e-04 |    1860.4361  |  0.08006934   |  0.04726370   |  0.07232247   |  0.00000000   |  0.000000000  |  0.000000000  |  0.00000000   | -0.03485913   | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene    lasso.as.is  lasso.log2   lasso.asinh  lasso.voom  bs.as.is     \n",
       "9  HLF       0.77458827  0.134199069  0.209422903  0.24105628  5.930056e-01\n",
       "5  STAT4     2.60534321  0.157190023  0.076155038  0.09551869  1.708536e+00\n",
       "35 SATB1     0.00000000  0.000000000  0.000000000  0.00000000 -5.683068e-01\n",
       "15 SATB2     0.13675630  0.216133895  0.228754703  0.26140384  1.035786e+00\n",
       "33 ATF2      0.00000000  0.000000000  0.009811994  0.00000000  8.928837e-01\n",
       "4  FOXP2     2.61508513  0.106667287  0.120449138  0.13231032  3.972476e+00\n",
       "11 TSHZ2     0.69243601  0.104562826  0.115311122  0.09676352  1.086813e+00\n",
       "40 DRGX      0.00000000  0.000000000  0.000000000  0.00000000 -4.072208e-03\n",
       "1  HDX      11.26492428  0.000000000  0.000000000  0.00000000  3.241756e-01\n",
       "8  LHX6      1.27950800  0.066352215  0.090529696  0.09315715  2.312552e+00\n",
       "12 ESRRG     0.66926539  0.019431010  0.046640155  0.01141497  3.927342e-01\n",
       "19 TSHZ3     0.00000000  0.101236743  0.064662784  0.04451921 -1.089643e-02\n",
       "38 POU6F2    0.00000000  0.000000000  0.000000000  0.00000000 -3.743652e-02\n",
       "14 NFE2L2   -0.32653069 -0.015204118 -0.057125918 -0.04777479 -7.803000e-01\n",
       "17 CUX2      0.03676364  0.074610279  0.134597803  0.11559182  1.240175e+00\n",
       "18 SOX12    -0.02284905 -0.065376926 -0.009410002  0.00000000  2.385374e-02\n",
       "10 HOMEZ    -0.75952764  0.000000000  0.000000000  0.00000000 -2.571221e-01\n",
       "34 STAT5A    0.00000000  0.000000000  0.000000000  0.00000000 -5.317519e+00\n",
       "21 FOXO4     0.00000000 -0.059932873 -0.038120414 -0.09406223 -7.525646e-02\n",
       "16 STAT1     0.10044635  0.058977896  0.039676356  0.01249171  1.452155e-02\n",
       "28 ZHX3      0.00000000  0.000000000 -0.085656799 -0.04603107  8.553841e-04\n",
       "23 FOXD1     0.00000000 -0.020514333 -0.043780174 -0.04197126 -1.447640e-01\n",
       "20 STAT5B    0.00000000 -0.072874603 -0.124676757 -0.24554706 -8.615242e-03\n",
       "6  NFE2L3   -2.44526737 -0.042077199 -0.012117383  0.00000000 -3.535628e+00\n",
       "29 FOXL1     0.00000000  0.000000000 -0.055243544 -0.02501204 -2.179699e+01\n",
       "30 RAX2      0.00000000  0.000000000 -0.054872150 -0.07062620  2.104629e+01\n",
       "32 FOXQ1     0.00000000  0.000000000  0.012481789  0.01484751  1.497996e-01\n",
       "7  SOX15    -2.00952281 -0.047167519 -0.081356107 -0.04556199 -5.808046e+00\n",
       "13 ZFHX2    -0.44426232 -0.064360137 -0.076382960 -0.03506666 -9.485212e-01\n",
       "36 FOXD4L6   0.00000000  0.000000000  0.000000000  0.00000000  2.001216e+01\n",
       "39 SOX6      0.00000000  0.000000000  0.000000000  0.00000000  1.117300e-02\n",
       "31 FOXE3     0.00000000  0.000000000 -0.023970663  0.00000000 -3.409200e+01\n",
       "3  FOXD4    -6.12834538  0.000000000 -0.018794219 -0.01121346 -9.572871e-01\n",
       "26 OTX2      0.00000000  0.000000000 -0.104669744 -0.06159170 -8.116979e-02\n",
       "25 FOXP3     0.00000000  0.004366561  0.061610938  0.02631893  8.865602e+00\n",
       "37 ELF3      0.00000000  0.000000000  0.000000000  0.00000000  7.590501e+00\n",
       "2  FOXD4L1 -10.64033243  0.000000000 -0.051396874 -0.05015705 -7.279581e+00\n",
       "22 ZBTB16    0.00000000 -0.020936732 -0.035129792 -0.07343806 -1.758344e-01\n",
       "24 ESR2      0.00000000 -0.012150312 -0.093702601 -0.05702639  5.680673e-02\n",
       "27 ESRRB     0.00000000  0.000000000 -0.091401413 -0.05857560 -3.068364e+01\n",
       "   bs.log2       bs.asinh      bs.voom       rf.as.is     rf.log2    \n",
       "9   3.228768e-01  8.583764e-05  3.468772e-01 1266161.9489 57.37132247\n",
       "5   1.832979e-01  8.946250e-05  1.708533e-01  649234.9844 46.37406567\n",
       "35 -1.444623e-06  1.154825e-05 -3.633166e-05  577058.4385 21.19406994\n",
       "15  6.942847e-05  3.566778e-01  2.853092e-01  424329.6976 26.35567531\n",
       "33  5.648769e-05  7.409788e-05 -5.996884e-06  137474.9788  5.41129360\n",
       "4   8.526235e-05  2.309917e-01  4.508482e-05  112640.2790  3.91445249\n",
       "11  2.277099e-01  1.056974e-01  1.235520e-01   84319.1960  5.12252917\n",
       "40  1.835087e-07  7.377633e-06  1.398560e-05   88685.5631 10.12862043\n",
       "1  -2.498712e-05  6.947728e-05  5.735278e-05   53018.0228  1.43641155\n",
       "8   6.697943e-05  1.139972e-01  1.365092e-01   33707.1603  2.90733938\n",
       "12  3.936057e-05  2.318925e-04  7.762502e-06   66430.6662  4.29280477\n",
       "19  8.721712e-05  1.673391e-01  2.551630e-05   23971.7861  6.25117359\n",
       "38  1.548796e-01  1.126459e-05 -6.461413e-06    7735.8553  1.54216776\n",
       "14 -1.304394e-04 -8.658551e-05 -1.444340e-01   16250.1818  0.62406681\n",
       "17  1.096828e-01  2.066298e-01  6.543902e-05   13627.7613  3.79983083\n",
       "18 -4.011697e-05 -2.834563e-01 -2.815619e-05   75863.7094  1.74741861\n",
       "10 -2.773147e-05 -1.190609e-04 -2.070808e-04    2075.6167  0.23303020\n",
       "34  1.619035e-05  5.634046e-05  6.416900e-05    2473.5968  0.70519199\n",
       "21 -1.089069e-01 -7.967480e-05 -1.649238e-01  100624.4622  2.53966391\n",
       "16  1.550252e-04  5.356998e-04 -7.697100e-06   49426.5314  1.67592402\n",
       "28 -3.468578e-01 -8.150376e-05 -1.474584e-04    2775.8698  0.12460750\n",
       "23 -8.077647e-05 -1.312632e-01 -1.355494e-04    3940.9648  0.17796819\n",
       "20 -1.028715e-04 -6.899009e-05 -5.172639e-03    1962.9467  0.14380142\n",
       "6  -6.050182e-04 -1.180878e-03  8.473173e-06    1780.2860  0.90328191\n",
       "29 -1.023130e-05 -1.109603e-05 -3.033104e-04     745.0412  0.04530632\n",
       "30 -2.984847e-05 -3.858662e-03 -1.693215e-03   10191.6573  0.15838869\n",
       "32  1.024722e-04  9.058601e-02  1.950611e-05    1912.3582  0.07622154\n",
       "7  -1.649264e-01 -2.072168e-04 -1.738663e-04    3073.3194  0.09674146\n",
       "13 -1.449449e-01 -1.972800e-01 -5.449061e-05    4242.0609  0.10108451\n",
       "36  5.598696e-05 -3.493371e-05  3.952855e-05    2138.4440  0.38560704\n",
       "39  1.410110e-01  2.635437e-04  6.368482e-05    1286.4206  0.02964059\n",
       "31 -7.539808e-07 -2.472980e-04  1.070491e-05    1436.7861  0.09033598\n",
       "3  -2.277717e-04 -2.120234e-04 -1.566068e-04    7525.4338  0.13565668\n",
       "26 -6.058279e-06  1.893926e-05 -2.229342e-01    1023.9351  0.11237214\n",
       "25  7.248455e-04  1.555273e-02  1.446537e-03    1611.4674  0.06884909\n",
       "37 -1.672171e-04  3.211889e-04  4.752853e-05    3326.0816  0.14770484\n",
       "2  -5.366559e-05 -2.090862e-03 -1.683918e-03    4436.9676  0.19584650\n",
       "22 -2.965382e-04 -1.843154e-04 -2.008173e-01    1771.1880  0.07772086\n",
       "24 -1.076067e-01 -1.918502e-03 -1.278182e-01    1254.3418  0.06494721\n",
       "27 -1.117221e-03 -7.120074e-04 -1.932052e-04    1860.4361  0.08006934\n",
       "   rf.asinh    rf.voom     sqrt.lasso.as.is sqrt.lasso.log2 sqrt.lasso.asinh\n",
       "9  25.91603826 53.05247084  0.69519133       0.140776460     0.142896092    \n",
       "5  22.47867569 48.94942356  3.47937884       0.190961358     0.158960329    \n",
       "35 11.98698783 27.98823933  0.00000000       0.000000000     0.000000000    \n",
       "15 14.22518618 30.46469273  0.02139235       0.181298914     0.226526723    \n",
       "33  2.34014879  7.35197201  0.08809962       0.008485763     0.000000000    \n",
       "4   2.97052657  3.80445761  2.44496515       0.101105760     0.106358127    \n",
       "11  2.25117903  4.60443070  1.00207756       0.100506784     0.094864545    \n",
       "40  3.22875065  8.62751278  1.18904754       0.000000000     0.000000000    \n",
       "1   0.71578023  0.86613053 13.37342127       0.000000000     0.000000000    \n",
       "8   1.70273480  2.28153347  2.69414714       0.058348346     0.086557473    \n",
       "12  1.61084504  3.77395550  0.45898430       0.004540240     0.010904356    \n",
       "19  3.70888147  9.28067022  0.00000000       0.103275043     0.119569865    \n",
       "38  0.61268809  1.62421935  0.00000000       0.000000000     0.000000000    \n",
       "14  0.26788921  1.05073252 -0.01666623      -0.010311681    -0.001796439    \n",
       "17  1.69602385  3.33980250  0.00000000       0.059819887     0.070841315    \n",
       "18  0.71539459  1.06994890 -0.05066787      -0.062815144    -0.063477372    \n",
       "10  0.11305491  0.13354243 -0.05711910       0.000000000     0.000000000    \n",
       "34  0.07501966  0.34651510  0.00000000       0.000000000     0.000000000    \n",
       "21  1.35220598  4.10752152  0.00000000      -0.060840837    -0.054144051    \n",
       "16  0.76834548  1.64999002  0.14023646       0.061726178     0.056424616    \n",
       "28  0.05507819  0.06762929  0.00000000       0.000000000     0.000000000    \n",
       "23  0.07402906  0.17825892  0.00000000      -0.018612142    -0.030152933    \n",
       "20  0.09304024  0.14962969 -0.03142925      -0.044682367    -0.088535616    \n",
       "6   0.43550208  0.85687215 -0.26084529      -0.038226731    -0.036547702    \n",
       "29  0.01952745  0.06490894  0.00000000       0.000000000     0.000000000    \n",
       "30  0.08666600  0.13254073 -3.68593268       0.000000000    -0.079070690    \n",
       "32  0.02708449  0.04248428  0.00000000       0.000000000     0.000000000    \n",
       "7   0.04575365  0.17657099 -0.20871913      -0.034963116    -0.033217226    \n",
       "13  0.05349746  0.11198257 -0.05434429      -0.044781408    -0.056811985    \n",
       "36  0.08815874  0.26016141  0.00000000       0.000000000     0.000000000    \n",
       "39  0.02545302  0.07550225  0.00000000       0.000000000     0.000000000    \n",
       "31  0.02945399  0.08047401  0.00000000       0.000000000     0.000000000    \n",
       "3   0.05591150  0.14653822 -1.39091255       0.000000000    -0.005870526    \n",
       "26  0.05414230  0.15764033  0.00000000       0.000000000    -0.035312214    \n",
       "25  0.02423162  0.08531106  0.00000000       0.000000000     0.000000000    \n",
       "37  0.06169517  0.18053208  0.00000000       0.000000000     0.000000000    \n",
       "2   0.07824253  0.21936956 -2.38450203       0.000000000    -0.014643134    \n",
       "22  0.03064144  0.06150837 -0.02690810      -0.011124576    -0.014971105    \n",
       "24  0.02666922  0.10061373  0.00000000      -0.002660900    -0.008303913    \n",
       "27  0.04726370  0.07232247  0.00000000       0.000000000     0.000000000    \n",
       "   sqrt.lasso.voom gene.cor   \n",
       "9   0.15897283      0.92323940\n",
       "5   0.17336532      0.90475987\n",
       "35  0.00000000      0.86282704\n",
       "15  0.23814460      0.84232989\n",
       "33  0.00000000      0.82786857\n",
       "4   0.10469561      0.81961721\n",
       "11  0.08923052      0.77043615\n",
       "40  0.00000000      0.73094500\n",
       "1   0.00000000      0.72419056\n",
       "8   0.09288872      0.71461154\n",
       "12  0.00000000      0.70903181\n",
       "19  0.09920060      0.70562897\n",
       "38  0.00000000      0.68969765\n",
       "14 -0.01195379     -0.63838196\n",
       "17  0.05577775      0.62765043\n",
       "18 -0.09422693     -0.62404571\n",
       "10  0.00000000     -0.58326686\n",
       "34  0.00000000     -0.57464748\n",
       "21 -0.09392238     -0.55645498\n",
       "16  0.04170972      0.44791101\n",
       "28  0.00000000     -0.43410051\n",
       "23 -0.02430439     -0.41483052\n",
       "20 -0.20937298     -0.37452622\n",
       "6  -0.02745371     -0.33931986\n",
       "29  0.00000000     -0.28785265\n",
       "30 -0.05256948     -0.28106544\n",
       "32  0.00000000     -0.26418329\n",
       "7   0.00000000     -0.26062515\n",
       "13 -0.02709282     -0.24922722\n",
       "36  0.00000000      0.24249449\n",
       "39  0.00000000     -0.18744712\n",
       "31  0.00000000     -0.15567610\n",
       "3   0.00000000     -0.11598769\n",
       "26 -0.01896358     -0.11114757\n",
       "25  0.00000000     -0.10724617\n",
       "37  0.00000000     -0.10576117\n",
       "2  -0.01698541     -0.10509003\n",
       "22 -0.04979472      0.06296197\n",
       "24  0.00000000     -0.03964059\n",
       "27  0.00000000     -0.03485913"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tbl.all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>40</li>\n",
       "\t<li>18</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 40\n",
       "\\item 18\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 40\n",
       "2. 18\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 40 18"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(tbl.all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see here, the union of top 10 genes from all solutions results in 40 genes. We can see that both LASSO methods are behaving much as we'd expect them, with some coefficients being shrunken to 0. We can see a little of what's going on with the scores in general--Random Forest looks to line up with itself regardless of distribution, whereas the others do not--but rather than draw conclusions based on a cursory look, let's examine the correlations more closely. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Broad view\n",
    "\n",
    "We'll take a look at the correlations between all different columns to give us a broad look at our table. We'll output these correlations as a plot of pairwise correlations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z3Np5xgMnAu/P/HFhNlDz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\n9xy2kUlhX3BhV6t6cYLPwWc9L9iP9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eBcc52HcW5dvgYhgFyoWpXtIfH8At4G/c\nO4M+dG4pbbkWnFvnQPApxbrJTYe8BS9kvX7BUVvmzM5bw+FrjHCO+MuyOzKD9G+XrNxaDtrp\nXHgV7syMcrzrMS+y7io6+BFsbtHO+8HxDvuMOSi3Zl2Kce9kjOWo7c6tlpZ27AqrZIa4vlg3\nBrSvpT+OMKEhR2tTPzAunUP3ZUcODfaaq8yZLS3z0CPgR4M531zqev4AaLdrgO9iXrgawhq0\nNOVnoV7aio7Mn+5HtMf1+gI4FeYD13XtvRxehPNAG4dBUjN4wC/U34sW40UM+NOqeCEDv5zc\nZLSD7mVuOJn6s8DANSH4sWQA3w7fgPWqXPsJVyf9p0zhnsaObjQb66exZzR2ffnGbqjgun7S\nRyHpzEzZj6Dgq8MozwBuRkeD16p9r2Vp0xSZcNpD6NeF0mSkTcfC42Cs+R4/gTb+A3yH2cCP\nJ4+hPcWSCr8JLHmxwspZuK+xfnzUtXCiBeT7HQVtYTD0AX3m+5R61uLUN1Wz84D42W6cVgDz\nz2mwF3QDbdMOfTkdrA5uAtQa0LGhVPpHl9LVVdXap2wLPu8j+BSczyNhJpgKDgcX0alBm7XX\nTX/8jpyW1EIla6urNO6+g/9mzRbmuB98DtrhvHKMfwDfR/tUJfZNuLPpG0PHdhpYFCxrg/4y\nB7SFamzh9rKaVEyUbRRd0Bbn0fTg5iLI8W0uG33mPGAuKSrtnBXMj8tlD9GnX0Fz2jkXzzPe\ni8qPoRlhCYjtNBY7gL5uDplTPmzig8xt+s75pH/3hXhec9pwrSn+bY73HY4d64PjYr5xX6KO\ngUUyPG+KXPOaQ2/zkNXA+bQRhNzzAmXVFF/a3uc2Rfpv+ewB5nPPzfUbZ3Wu3yFvOt92g6/B\n9/AdjO9K3mFa7muqOvGA+eE+6AnmTvO8R+U+RLv6Q5izf6ZcjVzPkpIHqvbA2rTwy318I5hc\nnVQS7s2fh/pyRzdiTrwicmK7eS/37OasH1zEwKzN1pmd+iv2WShrZ7gWykVtfyvrs8hhZxrF\n/WpTOA/lYLPjHNdZDuehzaSOYdGgWdVyMZ/Us0td07ZfILaxklh9vGrr/n+Dv1MsZ0voO9ij\nbdbF93stXxdfj8sPcG9RHUdDn2V/gfDsYF+oL3UM91ZyvL2okbQ7A2I78+XYp8FO68I7VGJf\nuOeaJth5MW1jW+JyeH5zHS9qgp03Z3YGX5WKweay07ErqntpGMZRW2tp57FFjaTdI5mdwdZw\nbC4fxs8xtxSVOS1+VsiLIQ6027r4nqLlfYoaSTvXsjB3tSkQ6oraVKqda19RvUvDeKy1rzl9\nGOx1r+NeoqgG0zA8y2OwM/iz3HncppKydoaPrmptdS/4cWab9gSbgn9DXThWYk+5e9zjutdN\nSh5IHkgeSB5IHkgeSB5IHkgeSB5IHkgeSB4o7wH/eHFy02oY7B83LgDTgL+5eBOeggGQlDyQ\nPJA8kDyQPJA8kDyQPJA8kDyQPFDIA+G/ZSzUuAUaHUqfV8OvMAz8MPK/b/ej6VT4BF6DpOSB\n5IHkgeSB5IHkgeSB5IHkgeSB5IHftQf8H3y/A/+n31Laikqvp//prJR3Ul3yQPJA8kDyQPJA\n8kDyQPJA8kDywO/KA34YjWjkjfwf25Zp5J50OXkgeSB5IHkgeSB5IHkgeSB5IHkgeWCy94D/\nv9RI6A2l/sa33aj/FsJfgUkxKXkgeSB5IHkgeSB5IHkgeSB5IHkgeeD364FuvNpQ8P81egL8\n++FfgM/Aj6P1ISl5IHkgeSB5IHkgeSB5IHkgeSB5IHmgkAcmx7/Fzn8orCv4D6R1Bv+BrEFw\nP/gPeiUlDyQPJA8kDyQPJA8kDyQPJA8kDyQP/CE9sBJv/fAf8s3TSycPJA8kDyQPJA8kDyQP\nJA8kDyQPNLsHSv2/PM3eSQ0f2J5nr1DD56dHJw8kDyQPJA8kDyQPJA8kDyQPJA/8gTwwuX8g\n/YGGKr1q8kDyQPJA8kDyQPJA8kDyQPJA8kCtPTC5/UOxeX/8QsUoeCl/IZ0nDyQPJA8kDyQP\nJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQP\nJA8kDyQPJA8kDzTNA5PjX/PdtDeuvPXM3NoL6vGfIfrvON1SuWkT3Tk7Zz2hHv8/2Wj6uXOi\n3is/mZtbt4J6xNxw+rm3ctMmurMjZz2gHnYOpp8HJuq98pMFuHVjqIed79NPn8pNm+jOLpz9\nBeph59v08/hEvVd+sgS3rgP1sPN1+nmyctMmunNZztacqKZ2J/6ny88VfPyKtPOfY6iHnqWT\nlwt2tCrt/lywbbXNHHPHvogcc8e+1vo/OnAOvVOwI+fQkgXbVtNMO81JA6tpFN3rv5lobqq1\ntPNBGFywo01ot0DBttU0007/Xcnh1TSK7t2McsfovFZF7bwLRhfsYGvazV2wbTXNfuXm2+HT\nahpF97r3nC06r1XR/03FvecXtepgcn5uPTYDk6t//Oi4Cd5r5AVm4Prs2T3fcvwK/KiaB5zM\n/htNk9K0XFwQ2sFPk7qxzLXdqL8U3i9zvbFq/ybAWeFz+AGmBiem7+FHoglzPEwPvtNUUET7\n0+hM+KBI4yrazMi9vpO2F9ERNDoWPizSeBJtTMraNBJ+BH38M8wJRXQijQ6GIVU0dr67KXA8\n/ceWjTfHdRZQpcbGa99B0c3E2bTdA4ZBY1qAG1xQ/LfNgozHecENhotOrA6cTAmfgeM+BpaG\nIrqMRj1hxCQaT8M1+/wSnOttwbnjeNp3KenfOUD7lWX9vIYnBXQjbTYC/9/L5pC5R9sd4/my\nB/puc0E/6A5FdDeN1oSPco2Np+kg72fH+HsYC42pIzcYC+Ys51F/cINWRP4zEctDufHzmY7x\n1JD3uXYYq6U2F841rzvHfSfH/X7YEYroKRotDM7bxmT+mwnyc84cpD3xpm0KzrXTnKCdjrsb\npr2hiF6lkc9wTlYi+/4anFNBzulOENYe67VzfhgH+tt3uQIOhSJ6n0bGoe9sTDpP20B4vuPn\nfPgVPgTX8iLqTKNz4dgijWnjeqENsX+KPEo/+xznzIwwA/wCzkNjYkE4Ec6AInJMvgLHsoic\nY9pl3JhPpwXrXKM8/xiUc+BwuNCTAjLO9ak5rilyfhk3+tN9k+uCeyPnp75wvdwHroFq1ZYG\n2mncmRObS46x9upXx+obWAB2gNshKXmgYg9sy52jK7jbSWCwPQsm1aBrKZiANggVZY5dqTf5\nOimKaHcaDSrSMGvjBsaEE2tPTkwg2jVfdqE7RydtUR1AwzeLNq6iXQ/ubcpiciTtn6uiv0pu\nnYebXJyMBxO/6gUh6TdUVPnjJO7vW2WbFbnfMd0t1+6RrD7YFl/uzcnAuKLK8jncf18FbdwA\nuVBvlLt3cc612QU+1sqcWL9IVnkQx9eycpHD5TS6pZGG+umG3D1dOdcOF59S2pXKOI8czfnT\npW6ssO4m7ruywnsbu20abtD2brApuDmcC/4M1veBorqHhueVaHw2dY+WqLevc0vU56v8iHUT\n5iKv7MO+isp+T22k8SVcv7/EPU9Ooq1zzM2emz7lmDl2RWXMGDuV6AhueqPEjTdSd3Oufn/O\nR0GY+84B50JROQedi5XKvl3DYs3MifHnHA8yLw+GdlmFOcXcUlTmNHObMoY+yGhrRabHObpB\ndSyLyhxtri4q/dOraOOs3TYc3QyHOeP4Omdcj08H5Zqnj4vqSxr2KNjYD1Vzz3a59vrNfYjj\nE6TN7iWKajwNuxdtnLVzn+fafUj0nKcoPwRfQBtwT7Y7FJExaPy7tjSXzEM+sx849kGfUNg2\nnKTjxB6YYuLTdFbAAy6SM4CbIANQzQEbwlhYEFqr3CDNDc/mDPTcpOXk2Td3LZ1W7wF/i2hS\ndbHdK2ruR1M95QJpjK4RdapdM4ELlPa1lPSFi4z+ifPSPpwPgREQawFOPgc3OvWSc9mNRKwX\nOPHDt9w8953MB5tBa9MPGPQi+KcF+tMN6JjsPP5TBqqaTf15kvG3ZPTEJSivCV5rTPrZMXfs\n6yXtWhcWiTpcjvIqUM5m7XwHvoZ6S5v0bzzP5+N8E8jbq51+TLXU3Nee3aEdBLnmuOHWriDt\nfAXc4Da3+vHATmBc/QRqKegKH4JzY3KW9sdzph/nfiS8B+Hd3Au0lOah46mg1D5Eu7aG2aC1\naHoMMafH9vbjfGlwLQ0fohRbjcxDr4M2Brv/RLk9+GF6bBmOot6Pqz+kpvxDvnXzvrSL4Gvg\nBugG8DcI24AbjcXB30y1VrlB8jdUbk78DWWQC+tX4EJ1GyyfnTuhkqr3gIusHyZXwb+gGzj3\n4k0BpzWXv9VyDE2IC4Gbfcfazd5L0NI6CAP6gx8d/cA/8XKT8lfQf7F8Fxci59i78YUalp3L\n+uvSqA/tawPl5rl2ngp3wh3QCeo97nRZVgdw5TFYFhYGfe/m8N/gJqC59R8e+F94Fm7JHt6L\nYx+4Lzuf1EE/d4HZoVYfcfn+Hbud4UXQZueuNt8N2l1K2rkPzARflrqhhnXP82xzzaOgvd+B\nc/5NuBZiaWdPcNPnb+vrraPo0DzkGvoQLAobgv72lzZB2ul6NDXE9eF6U47G3UDYAK6AX2F7\n0H+rgXN4cpb2O2ecz5+AeWgX2BJehyvB9aClNJKOHVP3ITdGRniu7cbvG+A8nBNaWt9gwBjQ\nvuczY1zXdwdjx5ieGVqb9sOgfnAkmD+Nbf+0agUwT5XSz1TeDC3xi55S9tS1boq69vb77Wwv\nXs2J0QtM4t/DtPAq9IXWpM0xxsVIG98FN0fHwhGwChwEZ8EZ4CapK3wEC0J+k0pVUs4DfoAc\nAh+CPn4WFoVrwPjQt1PDSlBvadNt8AMsA4fCsmAeMGm2tFwElwMXHe3S3qPhTNCXb8MOoF6C\nR+A/sA10BpN9LXU6D7evc8F5sSPoTzehQ0D1BDei2vsWOObHwNbQDlyYXHRqKfPR+6ANL8PG\nUE5+EOnzfuBGxI3SYaD8ECgqY2oP+AYcIzdoyhyiL/4OHWAesL8toZL8ch/3DYOHwfeyfVPX\nMT8I3wX9ZW7cHGKZ2/1IPwo6gpu0A8GxLafbufA5PAQbwNzQVK3MAwaCdr4I60M5GQP6f2Yw\nd58Mf4GfINYNnIyHB8HrvltT1Z0HDIXvoB+4rpTTUC4Yf/a/NIyFNeAmiHU1J1OBsbQuzAbN\nJePOD8hfwE3ubjAcHLPPwLGcHOQG+AMwPpzXjqdyzgwG58wmoH9/zPiC4xwwCuqlBejobnDT\n/TGY3y+GC2FfMF6OhUPhJNBery0Krq/11nx0aAx8CZ/CleAacAIcDtprzLSHR2ExaC2aGkP+\nBR+B4+/apJ3G+ydgbnOf160MzjXzbVLywEQe2Jaz0RPVlD5pR/UbYAJy8phsDTr5CpxMBmQ5\ndeWCbYpu7kzog8o9PFe/Neduzs6DreA90E4Xhp9AO3yHwyCfiFz0XEiL6gAaOjlrrR50YCIr\nqiNp+FzRxrS7BFx89KNHfSu7gonKRUE/j4FK4ovbSsqFo2/JK5OudNy1xziIY7Uf5ybTvHpT\n4aasqM6hoQt0pdqGG98FbTSBa+fZ4LieDsagm6cb4FLw2T+A72K7orqchrdU0NgNxgCwv2/B\nhf3voE0PgfPoMXDhD3nAe4KOpvB0OClwtB9zSjkdzgV95BzQh27S9aFzP9h5FuVFIa/ZqHAj\nYHvf73Eoqvtp+Dy4uGqHvvB580NTNRcPcIOlr32ufRXVozTUvm/AZ43Lzo3Dpmo+HvAf0P8+\n27ErKnOS+eRZMO58nlwCTZUb1odBP+hT50JRvU1D7TTPaZ9ln7k8NFVdeMBj4HMdJ3NLUZnT\nzG1qBvgQtP0DCL7VH/IOOH+KqC+NzNVFNYqGvSpofBz3fAfHwGZwJWj79+D7jADzjmPh3La8\nDAQZX0eGkwJH8405ujHNwQ2ue0/CDtAfwvwwZnwH7dNe18y83qTCvURRmdvcy1Sq9tw4FF6A\nnaAfBHt9Z23WXtcqP+iCBlHYPZxUeWzL/T6za5XtSt3+AJXG0D6gfe5JQ3zbh+XLYJYy+P5J\nyQO/8cC21DiRG9OO3OBEeR3cjLhZGwsG3jNgInZRmwJKyUlgoDopishJ6GSsRNpyRnajZW10\n8owHJ73Jcwkope5Uel9RmdRMbrWWSdrxKCoXCReLItJH+tQYMHF+DMbCGNB384Fx4ILcC/R9\nUZ1Ew74FGr9Pm3HgXGG3hAAAQABJREFU4jkUhkM4/zflvHpTMTBfWcX5Odxb6QdST+41Ds+E\n9UHf6bfDQU0HYcN9M+UnwPfYG7znNSgqN4WVfCCF5zuGs8O7YJ64EYIfPR4FvsNTYExsBepo\ncHNSVG6y3fyU0tRU2rccCfav//WRfhwJN8CL4D0bQym1pdIx6FPqYoV193Cf/ToX3AS5UXZs\n9dVM0Bxqx0MuBPsqKm3TTm17BhxPx+t7aC5NxYOuAceuqF6ioeuLtg0AN2yWtd2Yag4ZP7eB\nc6GozBXaZHz1g0GgnaOguaSd94OxXVTa2TtrfCDHj8Bffjjull0Ltdu15DIwPop8JPWlnbm6\nqPRbr0YaT89159nO0X2PUtZ+6x8H38vzjWBayOs5Ko7MV1Zxrp96VHD/idzzPpg7B4M2GSPG\njGhv2KNQ/I3cQxzwm9rKK8yD3Su/veGXxa6Rc8GHoL3GjvEQ4tzx/RPE8p12jyuqKLflXveE\nXatoU+rWVajU3iVhXdDmcfANGA8hJuxrUizF9T+kpvhDvnXzvrQfFF/ALPAaOFGmBINvJdgS\nVgA3Ky0pNxMLw6rwbVZ+jKObJCeki4PxcDEkFfPAJTRz/N0QqfAbST+UrD8ITFgmqJaQNhgD\n9j8G3Gi4WLrxcuy3gKWhpXQCHd8Da8GdMDO40LsBNH4PAW3W1oNh7ex4Lkev11P68Dhw7vSF\nDcBxd6FxIdKmPuCY6/eTodZahA604Rg4Hexfn40Ac9It0A28bxRcB9qfl5tD36Ep8rm+t8+Z\nCcw1Y8Ex3QOaQ2523KQ0RcFON3iLwhvwCEwDm0Jz6Ece0lQ72/AMffcVdMiedxtHfez8mBqa\nKjdP5qemKMxDx6Yz3A6vw9ywMDSHtNN51lxyDX8GXPtuhBngVDBeLe8AL8IJ0Bq1IEY5/ovD\nEDA3rQsvgfNvHegI7kkuyo4cWkRu1p+Ao0Cb/CAyrr+GJ8FxPRD0e2uQsfEU+FE2L3wAs4Dz\neTSogyGs+Q0VreSHvh4J78Jd4LppPBgHxslboL9vBtf9UixGvff9IaXDkprmAQPQBet9WAuG\nwQBw49kGroRBYPJqSc1K5y5+LlQGvBPc4L8VXHD6wxhYDUxaLmpbQFJlHtCvC2W36lsT5rKw\nHJioXGz1d0vKmPwTzASzgQuT52Ex+pJyS9loLuoCm4OxdxwYl3+BGaEz7JWVTerXg3PKBd+k\n7yahHupKJ0+Ci8yeMCcsD0eA88Y5r5+9Ty0D3uvmUF/XUm2zh3/O0UV8KZgeHGu1M9wPzvP5\nwfrLoRZ2Tclz9cex8BisD6NhKnAhjuXYu2EaCvpK/wb/Uayp9Jl50f6PBDdEoe8NKVejvbnZ\ndcD3fhk2huZSGNv/8sDdYAj0BGN/GnCzWY2MjYfhWxgFp4Nj01T5DN/fuXEO7AEdwBjbFIrI\n+X8pfAJfw79hamguuYY7h/Vhf3DOuOn1+Dk4n1eGRWEGaG36CIPMicaf+fA+0N+uP5/BUmCc\nPALzQJA5817Qp8Z9PaSvnf+9wXl/AxwB+lV724Ex5Fh/CIdALfITj61I2qv/NgDnoDafAuOz\nc+2dDr4AY2dVaC3S9rngnzATfAPHgP50nTfmLX8O7glLMYD6P6wc7KSmeeAWmuvHdcHN0SKw\nAvwIJi2DsAsMg3pqLTp7FlysXEy102Q5BzhRtNUNskl/angN5gavbQv94A7YBiY31TOu2+Mc\nF6QRkZP07Y3gZmZKMAmZQIdDS8oPNzeD2tcuQ/vcCBirJtHtsiOHukq7foKxsDu4uXKhdCy1\n7RIwdj3/D+jTp2EBcOFyA1BrrUgHj4Nj/h0E37mQv5HVcWiw7U6OR8GZ8BKMAN+jlvqAh+tH\nN5PO9Tfha3CDp7+0x03U87AvqB3gjIZS8/7QN27cHccDskd35qgP8vPgAuoOAzd324O+egL+\nDLWW8STacBm4GXI+6EfHuFIdyY3nwq1g/nwRzAubQHPIGFdbwNnQC7RbP2vraKhUC3Kjc8c5\nszOcmh1dI5pDxtuFsAZo35ygHONqx9R3fBi6gz7uDT5vFWguXcuDXPvUzRMODTGgz50/X0F/\ncA551KaW1Jp0/gy4tg+FI0Dbvoe3wfmttL8jvAmfwmrgvkTpQ59hzt8Dqokfbq9Y5vIBMA5e\nh0FgHjVGvoQ2EOzXFn1rjtgfroITwPist+anw7vgQFgSlgDtMp7NaeYI7VXOv91gFDwBK0At\ntTQPfwQc74/A/D0V5NWXioFg3lczgLbPBrYLsbAB5VvLcAP1c0E1Ms7Mpw/AY2Au6A2LwmSl\nlp7ok5WzyhjrZkk/mqCCLBuwJjATgNerWWy5vUkyQB+FEfACzA7dQDtMnuuAcgOjXdfBwqCO\nBxf2g8CF+GSY3GQiq4ccWxekTcExt9/xoHaDHRpKE+o7Ub4oO2+pg0k9KPjIpKm0/1tYHPqA\n71ZPGZsuPrPBTxkmcO0SYzbYZExuDIPhTnChcOGttY6hg7Ggj5z3v4B+3AQeh/7gom/dzKCd\n74GbQudSreVm7hNoW6IjF/Fd4S7YG7rD56APDwb93pz6mYc5bsqxtWysWX8pBM1DYR/YHv4F\n92TlhzgeC7WWGzfVDrQzzAvtnRUq0dTcdDS4mToezJ/7wmVwEjSHQl5xXekI2jk6e7C2dsvK\nlRyO4KZ3YVP4N1wMzqctIGz6KBaSc1EZT1uC80BbHXfXInPLnFCpnFvLgfP/Grgd1oMwThSb\nrOl4gn7NP1O/Wv8GrAy+25KwIbSUVqXjvqBfXbtnB+POnGn9f8ENaXiXTymvktV773BQ+4O5\n6i9wB5jXmlvaZWyNBJ/vBvk8+BB+Bd+hNywA1pvvle+1LpwGe8Ch4L310ix09CysCfpRe8yp\n5odOYI713UKeHUL5VugFj8CxUCstxIOfBn21A5wMO8EtEMuY7gkPgTGsjGffxzh2DnqP8llf\nlcE1xXWuUjlWfWAZGA39wef/FV6C3WGy0ZSTjaWt11AXdRUSkkcDUQzAH2AUdAGDtR46nk7u\nh5XgM3AD4kLdEdYGJ7ybFTUt7GIBafsrDaUJP7T3cHDz0Fz6Mw/aFt4Dk4q2uME4GOaH28BJ\nFfxJsUEmUROCifJBcDFQ+edNqK3PTzcXbvC09VOYE0yawfYQB1Q1JIi3LbSg3ITG0k5t9Pgr\n9ANj5X0wod0D9ZL9Bxlv2hXyk/a5KC0GbjqfhddhYXCOuYly8a21jLW5QHvcaHSANqCmh9XA\n9wgxrf22OQHctNRaM9GBMVhK+rMrDIZh4Pkb8CWYm8Rc0Vz6mQfpJ2VfynPjy3wY5JgqNxax\nzD1HxBU1Kgcb84//ggo/GPaAODbz93neCRz/hyGW77BnXNGEcogzfanNHo1FNx+3wwHwAFSi\nJbhJf8fv/irnzrH2MAKKKjwz2Bjs/Q8P7AkDYUf4F1QibX0HnG9B4yh8Hk6a4XgOzwj2BvvD\nY+el4Lxy/roWDoIl4UFoCR1Lp8amee8YcAN7MnQCc43xMBRCP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nM/oIKsc40rJfcCH5e68EeoM5kllfeAi85C8C/oDeuDm/mPwCB1AY0V\nAjXU/RIK0dGNQR84F/4a1Tel6GRdARYHAzqMq/bktU++ogXOS004bW0smY+IbI03x6WeF93a\n5KK/cemZPcUF3MSU9+3M1Iky4dRTxtm3sBH44fs8rAU3g8lf+/P2GiOhzvcxhpaH+WAGMN7d\nQJhUe0AtdAQP9QNvAVgm14GL/VRR3Z8pu6nRtjtB213UaqEuPHQN0CdHw91gf9tBkDnAWDAP\nKDdST8H1cDvUUnPycH3gGF0Ou4L+6gxB2uNHsD5rCTl2Ib7i/q27Iqu4hePT4HtcCfryVPCD\n7x6oh9yEhE1w6E8bx4LjuSd8AGoa0N/m8CegnirlT+30Q8JxfhQGgHPWeXUmqMPBOdzLkzpo\navrQn8q8orTTHPIlmBtfA++5DU4Gc4A+7gjafR+Yz2qpKXi4NnwE8+Y60l5t9+g92vc+VCJj\n2rgJMV1Jm0nd49jOAvtDu9yN2qadwc8XUj42d0+9TrXBNcW5ob3BJn2ownko70DBd2tpTY8B\n34Exty8EeymWLLvX+NGLdZC/9Fwt60f/xbZZ7flocD0wd7rWnwLOqUfBdewAMC5cX5+DVcHn\nzg+297menwdJOQ8Y0EnlPWAC+gaOgd3A4LsX3oZnYCdQ8eSfUDPpnz7rARgOLsRNlQu3k0O1\nnXCYaDKVmlzZbelQgQc6RvfoS+PCBbacXw+M7q9l0f63gaVgJXgc/gnK+HLTfDA8DKuAC5cq\nlWiNm1fgIdgQ2sPx4EffGGiK1qTxh+CzjwVj/iowgfsOK4M+DXZZNxXEcqH4AbymzgffsTk1\nNw87Dpzvjq+bp5ugH/SAIG3okJ0Em104e8ITWX0tD9q2JbjJeAQ2AzfH60FQ7MNgY7hWj6P9\nh7GK+/+K+q8zA57leBBcDKdB2ABuS3kU1EP2qT9VbOdcnO8ObnZPAjeozokvQX/7YVJP+eER\n/Gm/2irWbwquU++CNl8GR4JyHu0G73lSB9lf7E9t1k6PbkSNW+e6cuzvBT8+PgLn34uwF9Ra\n2mT8rQKWtTnYGWymqsFu53Wlcl/ge4WYdnyeq7Rxifu0xRxsnlfBNo95fZCvqOP5L/Sl/8Ia\nE7oO9notyPH/KZy08PFz+l8YLszs0LYQv1bFfvYdHreyTloj6if2X/CpR+eM12QFWBNcy10b\nz4bLYQj8F7qC980PyjHzXRcH80Yp/Uzl7TCu1MVcXSfO/cOExrRVYze0luvpA6mykfiE2/4D\nLox9wMXb38q1gTCBDLxY1htcpTSayj/D2rAOHAVNkZNaW5Jq44GBPNaxdL44riGBhjEPMcCl\nhv+0rV5JdEX6WxXGgzYtDYuA9qqH4WiYE0yGxom2SrCdYoP8CPIjYF5YG3xWV1gJukNTNJzG\nV8F+0A2ug13hG/DDYmGI7dK2vI0uYL7jD3AF1GIzsDrP1b4h8Bk8A5eBG6lDIW8TVf/T+5Se\n+N9ZbQuOo75wYbsGpoEOEEtblb5sCY2POg1+0xY/MuLN+gWc3w1rgW36gh/Q9ZLxVErXUuli\nPwycU13hS3gU3OjXW9/T4QxZp/GYnk7d05ExN1B2jVonqzMXOcfqpR/pyI+kIG01XmUFcC0N\n8kNZO513+ngw9If4/TitiYzJ18Hx3R7K9fkt17SrGsUxbd5oitrRWFu1z2Ne8bWl8hfreO76\notxIm48mpSm42BZ+mtRNdbr2Cv3MBdNl/WlbUPCt58H3I8LFOhxD//o23quHWA3HYNtL3Od7\nzA6bwkBQj8Cs8A9YBYbAC2Be+De4j+gMpfQzla5/lay35m3n9M7gLwVehslasdMn6xepsfEG\noJvMh8AJ4m90hsGH4EdTObmglpMLxmPgAn1UuZsqrPfj6A5wM9sJtDfIcphIoS4dJ+0BF3g/\nYN+A8fA5XAQHQpDJ3SSv9G/w+SbZufW1VkjqbuRXAzcf18OqoJYF42s52AOuhFLxYJ2bBRcL\nk7G/9fTdfJYLdHdoiobS+EK4BQaA8e5CdD9sD/eCyfQkKCX9ew4MLXWxGeue4llrwJKwE9jn\nA3A5bAZhHoXxDudcmuivqve81tJ/x4B55D3oAn7kzg3a9SM4jm7wpod6yw197B/7N876gItn\nrJGc3BRX1LHs/MjbqU8PBjcHamBGw0kL/SjlT+12XuXlxufWfGWdzv14nCHqyzE3Vs+C+OMo\nuqXhA+/puKIOZcfYvLgm9AJt1J9xLGh7iAGKVSnE9C5VtSp/811c2iq6rJ3aF45ectPbUtJ/\n5pxgk3aUK2uz62dLaTE6dh1yL3ce7ALDYQ5wvVPaaIz4Dspztc6EQ11+uu+YGb4EP3D8ADEP\nzAtB2qed+t91ehx0A3NWrK84OTKuyMq2vQHivU2J2yqq8uNoN5gL+sGZMFlLpyaV94AbjN2h\nLywOBtHe0BPGw5sQy2ANE8r6qeKLNSzbpzZ1grh/Tif6T0HCJLc+qbwHFuXSizAKtsxuO5Tj\nN1nZgx8QJpdYX3DyQVxR4/LbPN8YNTFeDCbOVWB58MPjZLgCnOdu+OLYsByfL8W5eh38cHdB\nXg9cNKaE5pAJ/0mYMXuYdvrR1BlOgGBP/silho9Uj7WU/TqmfiTqt+tAXxwAK0JewU7r589f\nrOG5YxQ2bo6ti7ofcy6iSruMCxVvVifU1OendmlH7CN9G+ZTfaxovJdgY2ynG73g38afUJ87\nSvnTj183Ta1J5sXgU+1yzfEj9B+etCJp16rg2q69YW2Mbaf6f/PIcksoxOXWdB7KwQ5tdkMc\nbG8fLrTA0Rxkzgl5J2+CNob10vdorjUl309j5/Z9PgyHu8GPjzXgMzB2lbbGvg7+9Vpnf9RJ\n02b9mNe1x+N8WV18iH3bhwv+SVJL6g46H9aSBjRX3ybdpPIecNKfCG58TaaDIOgtCm4g3UyV\nkkFr+3rIj7U94SqIJ7N9h0mWr/daUmkPvEf17OAm+TZYDkzu/nY0yIQVNi2h7spQqNNRm8JG\n7ijKD2T9vsLxargEDoFPII7TOKH+yDXPQ5xQbPgrpN/laLI9GdpAc0ifLQz+htvfhJns+8Oy\ncDhoRz5OPbfdPFBrfUoH+sPftNnv32BN+Aluh1IK9jaXj0r1ka97lYqb4RvQN53BD+D4Tw2C\nXeHI5boqjrfQsRtlx701ybHNyxhobQrzPLartflS20rZ6XxvqTiM/RWXzd3O2bWjythGyxI2\n9dFtdS3av3aYh5cG53uQZT9InFfKDXRLyXkU+y/YpD3aKfo8vE98L9V1k/32gNXBXwpeBe+A\ne7lFwPcIPg7ru8dgrx+k9ZK/YA8+028hFoN9oc7YsGzODfdQrEg+f19wvErhGrM4VKPruLnc\nelnNc1r8Xp2TVN4DX3NpHtgKBuRuO41zE1ZYEJxYll1c/WBRfljVQ47jJdA915kb/G1gGDip\nWuOCilmtTo6fv1E6Gp6B3qD0Y5AJczT4pyLK5PTPhlL9fnSgq/AbO/vXpoGwBLhYHgUmTI9L\nQohH64wHPwhCDjDWg7zPmJ8NbNvUDaO/KNCe82Ah0K8m/+ngAnCB+hco7TBRnwTrgtdUU22Y\n8JRJ/3T++ouGY+BF+DfcD+9DiAGKDX9l6h0c7wP9qJ6ccKjbzz705NgNhpugI/SDsJC7sL0H\n40HVe+7ry2/BzXHQh6HQio7GmjHnXAhyvFubHL+vwLwU9GYotKKj/jR/jI1sci61NplPzG3m\nJf0aNJSCsRDm9ahwoYWOzmdzu7beAmF+U2zInRtxDDm8n5UtJG28E7TP9WXaqOwaaf29oK0j\nwPtbSsao6/pe4Do3B6hB8DBov/aaO81hEuQaVi+Z17VDm84AYzL417K+PBzOAs/94L8VqtGX\n3HwubD4JmisfLkcf/4DJRlNONpa2jKFuPntMoms3xMeDm722YJDqU4NYDYVJtfeeRf3RRLkZ\nciPiJsnJHRKmCUC0Rx6HxuzhlpJatmRtdZUzcnvR/ivtaeVKbyxzn2M4MwQ7TabLZ+dPcoyf\n34FzpW/d9LtYVaoVK71xEvfNwLWh8B50BhP9CbBwBocGuakyse894bQhkfqes4PxotwUhHdu\nqMh+zBufFCyvRzttMBmfmR1P5Lg7zAddYBh0gvZgPK8Ou8FcoPxY0sZyWqrchSrqfVfH+xDo\nBvr3RugPvoPXpgH78pcjKswt25byX8NN0Y/FonLRYicajgM3oZ1hMFwIxp8bD+e/c020z7qP\noBL7uK1Bi4RCwaN5cPoMbVAjoBobGho18mNBrv/cyD2TumyO108q2DmGcnPb2YlnmqOLyg1n\n+6xxsNONfXPbaRybT4rK+WH+VMFO501z2zk3z/zQTpog33VNMPcsDX+CTqCC7c6tpthujm2K\ntOMuWAjMHeZr57c6IMN7vgP7KmprGDMeUViOR1/oBm2yp2irH6FqMzAXnQFF7QxzlUcUkmPc\nFZxPvrPn28IwUO/Chg2lCXu6dllZH38BvmMltk+XtWvKQVvM8+vDxtmD9F8Yf6sOhBOzuiEc\n/UV9JfZxW4N8/1fh4QmnNf1p/O4Jp9W0l2Z8uM5JKu2BNai+D+JgLHWnPnQzoII/nUzKhTuU\nGyrK/HDj5ybR5FetnDy3QrDTxKQdnnu0f/HZTq6myA1O0Y2oyfFaCD5qih2NtfU3His3dlOZ\n672ovxiCnY6tvgtjo1+Dj73H+krHmVsn0mucrT1RTeUne3DrWRAWIlsGGy2XkvaGWPW65yE+\nJ/UO/rYtJGjbVSMX8RMh7ivfPsSqx1LSRu1rTI9yw9aN3VTm+pHUy6Skr7UxtlOfV2pfeLZ5\nZedwUuXxRO4/OGuT96l2aJu+8hiuh2Nj8UGTiXQLZ/tOVFP5ydlZW20SNakYm3BHsZ9X0ezv\nxZo2/Ge0O9K2HnZeQD/HFrTTsdi8TnaeTj9SRPfTqDvE/nTTVgvpS31aRA/SaLWsoXMl5FHL\nse1NjVmfdRgYo0X0FI2WgXju+stY7fXZynVdwrl11cq2fwPjrIhepNEiWUPXGO3Td/oz+DTY\nSVVhaeeucG/BJ7xNu3mztubFYGf8uGCz1y0r30X7K5XjtR30qbRBdJ99DoTZQPuUzwvjG/xr\nnTZKtfbRpEE+ww+qpyacpp/JA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJAyU84JdnUvJA8kDy\nQPJA8kDyQPJA8kDyQPJA8kBTPOCfzPaEBWAa8D8VfBP8U6oBMNko/PHdZGNwMjR5IHkgeSB5\nIHkgeSB5IHkgeSB5oFV54FCsuRr8T/eGgR9G/ieBfjSdCp+A/2tBUvJA8kDyQPJA8kDyQPJA\n8kDyQPJA8sDv2gP+hTffwaJl3nKr7PpUZa6n6uSB5IHkgeSB5IHkgeSB5IHkgeSB5IHfjQf8\nMBrRyNt8zHX/0pGk5IHkgeSB5IHkgeSB5IHkgeSB5IHkgd+1B/w7DUZCbwh/+1/8wrtx8i20\niytTOXkgeSB5IHkgeSB5IHkgeSB5IHkgeeD36oFuvNhQ8P81egL8Jy1eAP8dRD+O/GdpJhul\nv8VushmqZGjyQPJA8kDyQPJA8kDyQPJA8kCr9YD/QLj/GK//LlZn8B9+HwT+G2lfQ1LyQPJA\n8kDyQPJA8kDyQPJA8kDyQPLAH9IDK/HWD0+ub17qvxOcXN8l2Z08kDyQPJA8kDyQPJA8kDyQ\nPJA80PIeaI8JK7S8GcUsSB9IxfyWWiUPJA8kDyQPJA8kDyQPJA8kDyQP/A49kP6h2N/hoKZX\nSh5IHkgeSB5IHkgeSB5IHkgeaEEP/ELfo+ClFrQhdZ08kDyQPJA8kDyQPJA8kDyQPJA8kDyQ\nPJA8kDyQPJA8kDyQPJA8kDyQPJA8kDyQPJA80EweSH/Nd3lHzsYl/2Grevx/Wv4d8VeVN2WS\nV+bm6k5Qj7EcTT/XT9Ka8hc7cmk7qIedw+nnlvKmTPLKAlzdZpJ3NN/FD3nUnQUf14V2mxds\nW22zATS4t9pG2f1LctykYNtqm71FgwerbZTdvxzHev0bDa/S1yMF7fRvBVqnYNtqmz1Pg37V\nNsruX43jGgXbVtvsKRo8U22j7P61Oa5csG21zR6nwYvVNsru/wvH5Qu2rbZZHxq8Vm2j7P6N\nOS5VsG21zR6gwdvVNsru34zjogXbVtvsHhq8X22j7P6tOS5YsG21ze6gweBqG2X39+LoGl9r\n/R8d3Aqu8UW0M43cM9Va2nkDuGcqoj1o5B601vqVDq4B96BJOQ/UY7Oa63KyOe2JpTfCK41Y\nPDPXTQxOCDe834FaDKaF18H/DrOcZuDCEtAOfip30yTqd+PaJVB0QZuftlOBtmun/19aZ1DT\ngYl9HPi3kSwE2llE+9PodHizSOMybaam3kVuCnChHA/zwkygX4voSBodDUUX3kr7NPlNA9pb\nRCfRSJ++V0Vj5/uK8AU43sasY98FpgQ37nnNQcXP4D1FdDaNdoVKNghLcd8I+BKCnEMLg+MR\nzyPH3Dnmvf43zi56vpcfOkV0GY22gg8m0XhOrjlug+BH0J/zgfOk3DgYi46x9qt5YDis7kkB\nmZPcLA8u0LZUk6WpHArfg/78BMxhjnc/sK8iuptGq8LQXOO5OHdu5v3sGPvvZYyBSaktF7Vz\nNHwKneA52AKKqA+NzL/GXTkZW46x4x5rEU6+go/jyqw8FccuYGx+DguAH8U7QhE9TSPXGZ/X\nmIzR2SEfk52p+wlGQtA0FHwP338sOA53wd5QRK5DromOTyUyf+sfxzLIMV4c3gfXHhXywDDK\nznl9ey0cCkXks815xrsx6Tx1zIaCY6qMC+vegR+hiHyPC+CYIo1p41j9AE3ZwOrPeM504HxG\naAdDwHm3JJwCp0MRmXudBx6LaCEa/QpDs6NjsyBou+MR5qf56ki4EIrIPYLzOIxxkWfYphNo\nm3nYdUlbzRH6Up8uB/vCNVCtfK52Gnc+rzn0Jx7i+uocN9b1gXNrBTAn3Q5JyQMVe2Bb7hxd\nwd0uJG4ors/deynnJtWdcvX5065UuFF1UhTR7jTKL9yVPqcNN2rjZrkGa3FustKu+UF1Bydt\nUR1Aw+b8ONKO48BNgHa6KVA9oClJxeT7nA+qsXrx/FFN6OMk2vatsv0G3K+v8pveq7N6NyF5\n9aZiYL6yivNzuPe+Cu73g8fN2ya5e124tXm+XP2mnJvg3cCog8DNWVFdTsNbGmn8Btf/mbvH\nuNO+VXP14XQXCm4cgo6m8HQ4KXC8iTZXFmhXqsk0VGr72mAeGQMupP4plfV9oKjuoeF5JRr/\ni7pScfso9cZKYzKPDIlusg/7Kirf8dRGGl/M9QdK3OM4nlKi3qp/wNvRNcfMsSsq+zJ2KtHh\n3FQq195Mfd4G88gL0UOdA5dH59UWnYPOxUo1khv3yN08K+fGn3EYZGw8EU44mlMqiZeoyURF\nc5q5TfmcYWAMxnqMk8/ANaGojHV9XFSjaOha0RTtR+Oh0QMuo6z/nDdhA++a15T3/JL2PaCI\nOtDI8XazHsuN+3jwAzPIuDYHFJXP6160cdauHcefYJPoOU9Sdu78AtPDIDCnFlFbGumPrkUa\nl2kzQ/ZM17D4I9hfTGxbps0fvtpNSVLTPGDSdqMxR/SYeShvBd9D2MBFl1tNsQ2WOBn9wIvl\nuRul0VDNYhc/ox5lfetvH13EDq5Hh5N5Hy4OJl4Td5j7HlcEY1VaSn6QO5f+BlNmRhiDB8IH\nMCKrCwfH3kVK6iX7zM8Vf7ur7VOXMaI/9W74titzvSWrtf1Z2B+033Olz+OPuobKZvrhpnMN\niDdDy3O+JnitMZUag8baNPW6dq0LS0UPWoXyylDO5pawM5j3OIUlId4ILsj5ppC3Vztbct5r\nz97gGhrkmmNedzMXVEt/6i/X7JB37NOcuAZoh31PzsqPsT43Nnzf8G6lfjlWr3cONuRzazjv\niSFz18uYCvppwz0S7LOJPjVHuJ66p2pt+gaDXoJ5Icx3bQ2+p5iU90CcEPLXfu/nblr+CeV8\n4ALTvgInvM89fWB9+C98BJuDk6cDPAmtVT9j2KtwCvjbztHgpDkUXoej4R74M4wFN6wtKX8z\n48bETfHz8CgcDsfB8eBvXPztjskr6bceeJEq47IHmCwd47WhI9wBLS03Rs6Xd2AgzA8LwUaQ\n11NUuBC5ubokf7FG58bbXnANfJX14Vxx8dG3sZbgRPsHwLFwE7jQzwP1kPnLcX0Phkyiw/24\n9gQsBtrrbz7ngH9Dc2xK/PByzjonX4CH4C5w/OxDbQmWH/SkEfXl+hmwMVRyfyOP+9/lRSgZ\nax+COT2WOdDc/hxo55SgzTfC41BKxsoxsA6Uu6dUu0rqOnOT4zUczNt5vUyFv3XX5rvBOb8F\naL82x9JOf7m0Kni9udSFBy0IxpNzuZyO5sIz4Hs8AovC6rAd/AhB2nkbLAevhcpmOj7Ac3z3\nbnA/OLe3Av24MpiPJke5XuuvH0C/bgK+qzG8JziH9P3NYOy3lH6lY/dNp4B+/z+YCvyTImNi\nVngT7oW5oKWlDe/CsWAe+xnOhQPhJzA/zQKtTX/DIO39Jzjf54G2kFTGAyb6P6qcgHNDOR/M\nyzXvqUQ9uMkJ3B1M6m4KZgblBN8a8hsor7WEnBBbQFdwkXeSmJBGgQuCtrtR8l20eQXYG0y0\n3tdS6knHl8P04EfcN6Bd18Bx0A86wQLwPST91gPfUuXC6CapCzimyoV0ebDufWgpDaJjN5PO\nFxdsx/lpcEzXhMFwF4yDj+FguBg2h/Zg7NZSx/Pw9cDF0XntBnBV2BG+AzUHuJlbGlw4zS/X\ngZsTY9j3MnZrpZl5sBugteEX0IfXgeOuPXm9RsUysB84vxeFfuC8/wyaorVo/ClMCz7b994D\ndoBesCkobbu5odT4D+09E+4DN3vGrHHTFJkP/wH6qw3cA9oY8oi2G5OO80bgfbvA7VBOz3Dh\nEugD94M+fh6aIuP7KVgdfgZjy1gzrr6AWP6yoT9sCTPA4WCutF0s298A/UA7VwRjuylyjM+F\n4E/jcScI/qT4P42kZO4x/paDAXAovAqxHG/H5Vmw7L0fQnOpGw/yF0aO79fwEzi3fYc74Xro\nDda3Vs2IYX5gzAujwHm1EmizMazfnDPfwirwJowG8/9wqKe0az3wIy34eQvKX8H9sDJMD8Z6\neBfH6FdoCRlvG0IPCD5dgrLzzg+3ZcFcexF0gpayk65/I+NB3y4C+ncOWB+MifGQVMYDJtg/\nqj7ixbedxMufz7W/TeJ6fGlvThaG72G67IKJ34VnGTAped3gbEk5MVxgO4KTWb4GF3EnkUnT\nReIWWBLegrfBxas7PAj1lklxH3CD8iVos5tj/XwzHAhuEP4KjqnJqjcklfaAmx/9NzeYINVY\n+BRcmJaAn6EldCKdOt6HgIv6TPBPcLF0w7Q4nJeVf+T4JuwIq4H31Hrz8nnWz4XZ8TOOm4F2\nPQH23xXcyGpbJ3DO+yFgbO4CR4MLba10Ow9eHezXzebSoI++hOGwCcwMH4Abduf6MDgsw03L\nNmCuaEocuLbYtwuwGzDH70m4FZYF564EGY/bgf2+DPeAm9NSOpLK/uDmfxEodx+XGlUb7lgY\ntPMHMK84XpfCzhDkhuf6jFCXP+rXXmB+fQf+Do+CMdLUDZM+dIzawXcwFQyC+eFK2Ary0odS\nTn4MGCsD4SBwDjXFlzRvsG8xjuPAOTo9GHPnwl5QSs6rE0pdiOrMS/rU954JmhKbNP+NjqFm\nSRgNc4LrjPPnMTCO9a/vU+4duNSi0nZjbVrQn8aF8XwFGHvG954wK3wD+8FN0A3WAce+Xjqf\njuzfNcf5rvrBbXAaGJfuOx4GbTWWLsgwr9ZbJ9PhUaC9s8H/wXtwNRwHm2TnR3B0Lupv52Zr\n0OYYcQt8Ah3AfDcGjHPPjYek5IGqPeAkriQJL8V9LiqDwYkR48T2Gd/CjvD/2DsLeDmKrG9/\nSEgguEuMhEBwt2DB3V2DBXcN7izL4rrYEtwluCQEd3dCiAe34ATZ73lu+vA2szP33umZO3cC\n/f/9nlvV1V1Vp09Vnaqe8O5bTD0pdMG1KXazGWW78ExzF+PbPKt9HpDDTgOQAfUXGAfa7KZg\n2WCYA9Tq4P2s2o+K5Qa3f1NH34atpvrzQ/gKtN3rLhDakMzXcZEh7UudZzLUK7fKNlQYU26l\n1PMnkR+Qum5u9k4eDH+mffsl5V6vXNBQH66dB1l1FhX7N7Py5zxnX85FU8c21tBO5MclZdop\nMW+3I+8B7xXIqkuo6EbSmDx8PAYeUAfBcAgfehiJ/L3k1XTgXHKzd82po+DJhly2Px5qLitR\n1c3OsX0epkyemY3UvsNnYaPP+R6uo55QqFMpeLCwsIxrfWAfjln0OZb8G/APSKsXF9/AMNCv\nxks/pvR3UzqHB+5o6qFG7j/FvVgPjmHktblNI/UKb3mI1s/GpoGgXx33mUA5Zo5dVjmmYZv+\nibwHNW2dHpqrSXnQOODacj6/m+Q3JXUNuBayajgVtU2bHHvx2jVtv1m0N5Vs71VwvIwL74Ox\nJasGU9HYplYAbTwSnKPmHT/TsN0+ze8B5WgADxurs2oMFbdpRmX94Xhq/0jwTKG9Hur7w9cw\nGl4BNRH8BxyXJ8FY2heyyvY3bEbljXnGOWF80T7x2lRbRoG2a5Pv4b60DIQ8Q+wXFxlS+1q9\njHqr8qxzbzPQPu38OUnDZtMnwHjqejJuDYFdIIvaUEkf9MxSOVVnRvKekVwn+jJtr3nt1R9b\nQa4iHpi4SFleVJ4HNuJxN8Y5wAnnYloWDEaTgRubC6Y7tKYMTD3gNIiN5R7yzgE31/dgUtBu\ng0I3MNieD62hTeg0AowHZ4OShysD+7RgINL2b2B9yNU8D4SvXuBxf71zbrq5TQceEONAR7am\ninnYnl7nhdXATcID5yRwITwKBnznwPLg+nIO/xt8j5bWUXQwF8wHK8PjoC3fgb7UXq/XhiXA\nQ9ax4PvMAN5rSdmPfZwAbuazw6fwCVjuGh8JnUC/TQ7+aHId6ONqyvF0zc4NHcCDTluYBdJz\nzDG0/xtB364M80NXOBpaWh5G1NowNezrBdL+dRtyzftzNY89C9q9KhjvPYScCdWQY6WOBD9+\nFwH3m3lAW43hzdWePNgLFoeVwH3hX3AlhD/IZtKk1PoVFgLt7AfORctdN+VKP54Le4PvvBys\nAh2hWnKvGQQ7w+PgWol44ry9CQaCOh+6NeTq549+de08BK6v3UB5FnF9Od6Oh+80J6gtYRtY\nEYylg6EW0tcPwqFJZ7uTaqN+NgbNDkqb9fN9cB1MDK0h7dWva4H2XQ6eP4aA/rXM+bILxDgc\nSb4etDpGaOOsMBsYL16Cj8F91HXp/VwlPNBak66EOTUt7kpvz8PLJfCr2onflFzc04CpG4Ib\nwTkwHNwgFwDVbnxS078eet1QDDS+jwe4J8AgqpYEg6byUOIC+tEL9DWcDB4SWmOeaO84cAxm\nAv37BQwDy0eCtmuv93I17QH9pT+dB0vB5/AoeABR3jeAtoZcK/IDPA8jwM3HMuWYu4buBt/h\nXbgGYuyd4y2tDejAA5K2eXBbH9xsXPebgvYq7XNjXxCcn64ff7nV7y2pT5PGjyP142wMWDYv\nqMnhLBgNu4F2toEusBBUW34QuV59d9erYzUjvAYhPyRnh74Qm7X+PQ82hJaWPvgJHFvjywXg\nOMmK0Bx14qGF4WiwLeXaOg2q9Q7OLdfACnA1vAjOO+UcG26mmdKmfvBW6vnjyTtPZ06VZcnq\nt7Fg6qHdNRBr9HbyMRfJNkvuP8Pg36mnHyf/Seq60qz2TQ3GkPVSjbkHPglrgevId3Jc14HW\nlod093Z9vExiTF9S5197CFunIm8ceDMpi3jqHLgNnoGWVgc6WB5mAX3tfq4djusa0AemAdei\neHD3nukR0BUWgFpLW+cAU+ehP/isAs7v7hDx3vU3FIxb7g/VWvM0VVKO8bIwX8knxtttzHBv\nmhSsszRMB1+Bc8PyXCU88Hd2jgH2Gijlg424ZwBqSgYYDx5pOQmVQcBNzAXkgq+lLqWznSEW\nsR8/X4ALWHuUhxWDkEyR8BJp6FcyE4NBqxYykO4Di8BqUNh3L8oMTr6T76b8ZeThhlz+pykP\nOBdDzk1ZEPxYUo7zA7AVpOcBly0ux1p1G580/NWersm1G772urZeBAO889P1a13vt7Scd24y\no8C5qtzY1b/Bj7uPwHfwuafAtabdB0FLazAduM796Ig162ao9I9+0mfKd1Ftxyd/XCeXFSeO\nS9jggciDt9facS2EtMOysCvK/ViKORFlLZFqk/F7L7DP4dAZ1OLjkyb/hi8L38Hrar2DhzTH\nysO5/noe5gfnmeV+OA2C5ig9D+J523SexphFebmp4+6+8hbY5ndJqi9cC4/AfOD6bY70baFf\nrWfb1dJzNLRnQWNtuPYjxJgpW4D222+MN9lW0cn0ejjoazUaIsa8TN6PDu2cBfTdmbAxzA2f\ngnIOeHhuSU1B4zfC+kkn2vQuzAXOsy9hA/BfabzWVn2rXWfBbhBjX2ufb0jf14HrS2m78aEr\nGGf1ZWgombAz3iHuVTNdiMZ2h/gwct0r92rnp3aEZiUzD0wP4TvnSUdwbps6B5w3uXIPlO2B\nc6kRkz4qe9jYAfaHZeBYGAtOsmK4oD4C2+kGae3Bxfvgx4mLz0mbRbtQyc3mJ/AXDtvzoFbM\nHsu0Rcz7rKkL5xvQjgNAGdweg3u9QKtDJQF1P+q/bkMFmoFr+yhlb5QXvtP3SZ2TCtozsH1d\nUFbOZV8efqacChmf3YZ6YzLWtZrv/S3ohztgTmhMK3DzEwh/FktHct8x/wIGgXPK+T0UssqN\nrn8zK8d8LGZbusz5uiZ8CQ+CNjpur0BWXUJF39d1cB3MCoW6gIK0Hel8+Mo1b7m2mfpOjnXo\nKDJPxkWG9FrqXFainge56D9tW+Qf5/47MBN4cNG2F8B52AbSOpULfZtVthv9ptO3Cxp0E9d3\nxhZ9djnMBR+Ac6cpncMDzv+sGkrFtH3pvDZtAANAGz+Gf0E7KJR+dd5MktxwLJ6Hm5Nrx8yx\ny6rPqahtxui0jebvA2Ooct5eD85jMW9ZWody8Rl0SRUeQt4Yexu4FrLKD6JC++L6Su6Nhv1T\njW9J/k3Q1+/BzpDW/Fw4p7dKFS5K/kdozvxIVftTdjBXfZIS3zdsLEztexgcB64Xr3tAYzIW\nOyeNzcbowj2KombrU560X/3qOzsPC21MX+tHfRxlxvx3wRisLbYxKfQG21wAlHte34Zctj/u\nucaM10B77UdfhR3F0vR99xyfsWz7JPVscDGMglhXniH2g6zSP6sXVHaOPQDarL/1RTF7/0t5\n2Ky9cY56kbyaGYbCGTAEdoEsMhbbV9igP4cl19F/3NOGD8Extl9j1M/ge+q3dyCeLZb6zltB\nriIe8AsyV/M8sCKPfQBng782PQ1HwuRQShNxYxZwQ7JuyGvbuQYqCUrRXqQjyHwFxTZwn9Ee\nx9wF9BMYdAzi1msPLsrD4VZws+oK+0JLaCkadfM2mK/djA7S72RQ8F32gGOaUSgywIgAAEAA\nSURBVDfLIxtT6XxYB2KddCB/JpwOHuwKdSAFHVOFR5N3fpSq5ztUoqFU7g0zwROwGtwAz4Fz\nayFQS8Mg8LlSMnjeD8vBtAkGzrvAedIS8v2dB+tDZ3DjLiaf0z7lZjE7aGtbWAl2AudzpXqB\nBnaH+eBZ2ARmg5D3So2Zm5rzxPtfgva5hpzjUgvNSScxV4v1tzyFc8HHsAXoU991R9Cv1dRk\nJRqbm/I9k3segAZCxEYPcNr1Dni4PwFaWs6hxsb0Tu7rG9fC8bAtFBvPnShfF94G4+dgmBEO\ngmrI+aX+Oz7546/XXWBOmAIegx6wGzhfzVvmvdB5ZN6A18H3c66fBnuBe0MlKhUrPLDtANqr\nrWo7uBbuA+PtzXAx7A2ht8gY4/X5ILgHnoFPoBpyf+vTSEMTcc+4eRz4bu7570IpOaefAMe+\nNwyFSqS/3NN/SBqZrERj2qlGwPcQ19OQ/xBWA33ZDrTxGngQjHl3QzeoRPZ3GPi+5kclKUlR\nDac0bPQB7TQejQY/fI1j7mPOmR3hN2gJdabRJ0A/bwODYGkoJp/RZtPJQRvNLwC3g3HLD8UT\noRoaSSP3gv10hh8h7TP95b1ZYW7YEY6Hz8D14T7umKTrcPknlZpPf3ro73qhc//OcnI40Ysx\nCeVOrAtgZzB4uwicjKuD8v6kDbn/m4SFk3Ef7p+dPGNinwZ8N84TwWBfqdz8bHcuMPiFtOVX\n+CkKSA22bvhjYUByPYjUd/T5W8AD3jmwIBjwqikXujY8B1uBATtkv4X+83pIPED6BRwMHeBS\naAm1p9He0A/WBsdJuZF8DC+Dm8uckJbv5gFKLQ7rgkGtWL3pKI+5QzaTDITa4VyyrQdgavAQ\nMRvo4xXA+WYwDYWP0+nE3PRApa//C7eDh6fHwcNNJVqKyo9BX4jx7kVe+7XxDnCe2a8qTC27\nCPSp8/JVUB7w5gVtrYZG0chLMDk4ljfBGHC9mBoTtC3sI/uH9OVIeB581z4wFYStZKsmx8rx\nXg6uhCfgEjgUQumxtczrs8Dx/QS060xwc38Yqi3nfTFp+/Fgugc4t5aA+eFq6A8ehk4DY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2pUbpx5GqaDfyV9arPzIGKQa3wN+BVaS2GL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\ndNJdX+0Kri2O7Y5QbL523B27rHqdjsGXwZ9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8wK1KjrFxUdOxH8E9elf1oCqunca1E6q4bt5x3ruIBuWL2XCtquMDXLykqgZVXDuLa0cm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\nK3ioMpDuC0HdSYwKmQxXN139cBNcCnvA7LAjHAPbQnpObUl+IKT9q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3oa1sx1GHoHbAXu+VmR8ak7aNvzEOKT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\nZoXPwY3K9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KG6I+ehvWAMfSQHkq3A/B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6rl2izbZ5A24F55fn60Mt0L/vgvYmiS+6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\n9dr6Hjj/XEzDvRRL1ko84XwYBI/Bc3A/rAVJX4Q+w5HLNWPue9n+CHB8v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kJQNoJvloK7g/LFcAb4EtMTzcbBNiYbYD0cqJUDzWpwjyvo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5jXh8QTGRtksmGzvKBjAtuG5Mxm2cJim3hfRPjy+FtWEO2AjaoX9xU2NUp4KJtKyIexI2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\n2ewP9iNzjl6uCVPD7BB6MwptWEa+2ZJ7O9aYO5X+2U6N/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07xl05u8mPHS2B/dk6QaoIDYzfhGqPzJXwZmrbEiF6OfM4hMKeFkvwV9L9K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\nHUgHqnTg/wHt1e9TSL3O9gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all pairs\n",
    "par(family = \"sans\")\n",
    "pairs(tbl.all[2:18],\n",
    "     labels = names(tbl.all)[2:18])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few things stand out here; first of all, the 4 different Random Forest distributions and the 4 different Square Root LASSO distributions look to be in very good agreement, producing the linear plots found near the bottom right corner. The other 2 solvers don't show nearly the same level of correlation between different distributions, though LASSO looks at least somewhat similar when comparing the log2 and asinh data. When it comes to the gene correlations themselves, LASSO looks to be the most similar, particularly for the asinh- transformed case.\n",
    "\n",
    "This broad view can clue us in to some trends, but it's likely that we'll get much better resolution by looking more directly at smaller pieces of the data. Let us now zoom in to look more closely at how the different cases compare. We'll begin by looking at how different distributions affect results within the same solver, then shift to how different solvers affect results within the same distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Within-Solver Trends\n",
    "\n",
    "We are interested in the question: how does transforming the data affect the correlations between genes? We'll look at the top genes found by the solvers, sorted by their Pearson correlation with the target gene. First, let's look at LASSO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>lasso.as.is</th><th scope=col>lasso.log2</th><th scope=col>lasso.asinh</th><th scope=col>lasso.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF        </td><td> 0.7745883 </td><td>0.13419907 </td><td>0.209422903</td><td>0.24105628 </td><td>0.9232394  </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4      </td><td> 2.6053432 </td><td>0.15719002 </td><td>0.076155038</td><td>0.09551869 </td><td>0.9047599  </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1      </td><td> 0.0000000 </td><td>0.00000000 </td><td>0.000000000</td><td>0.00000000 </td><td>0.8628270  </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2      </td><td> 0.1367563 </td><td>0.21613389 </td><td>0.228754703</td><td>0.26140384 </td><td>0.8423299  </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2       </td><td> 0.0000000 </td><td>0.00000000 </td><td>0.009811994</td><td>0.00000000 </td><td>0.8278686  </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2      </td><td> 2.6150851 </td><td>0.10666729 </td><td>0.120449138</td><td>0.13231032 </td><td>0.8196172  </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2      </td><td> 0.6924360 </td><td>0.10456283 </td><td>0.115311122</td><td>0.09676352 </td><td>0.7704362  </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX       </td><td> 0.0000000 </td><td>0.00000000 </td><td>0.000000000</td><td>0.00000000 </td><td>0.7309450  </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX        </td><td>11.2649243 </td><td>0.00000000 </td><td>0.000000000</td><td>0.00000000 </td><td>0.7241906  </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6       </td><td> 1.2795080 </td><td>0.06635221 </td><td>0.090529696</td><td>0.09315715 </td><td>0.7146115  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & lasso.as.is & lasso.log2 & lasso.asinh & lasso.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF         &  0.7745883  & 0.13419907  & 0.209422903 & 0.24105628  & 0.9232394  \\\\\n",
       "\t5 & STAT4       &  2.6053432  & 0.15719002  & 0.076155038 & 0.09551869  & 0.9047599  \\\\\n",
       "\t35 & SATB1       &  0.0000000  & 0.00000000  & 0.000000000 & 0.00000000  & 0.8628270  \\\\\n",
       "\t15 & SATB2       &  0.1367563  & 0.21613389  & 0.228754703 & 0.26140384  & 0.8423299  \\\\\n",
       "\t33 & ATF2        &  0.0000000  & 0.00000000  & 0.009811994 & 0.00000000  & 0.8278686  \\\\\n",
       "\t4 & FOXP2       &  2.6150851  & 0.10666729  & 0.120449138 & 0.13231032  & 0.8196172  \\\\\n",
       "\t11 & TSHZ2       &  0.6924360  & 0.10456283  & 0.115311122 & 0.09676352  & 0.7704362  \\\\\n",
       "\t40 & DRGX        &  0.0000000  & 0.00000000  & 0.000000000 & 0.00000000  & 0.7309450  \\\\\n",
       "\t1 & HDX         & 11.2649243  & 0.00000000  & 0.000000000 & 0.00000000  & 0.7241906  \\\\\n",
       "\t8 & LHX6        &  1.2795080  & 0.06635221  & 0.090529696 & 0.09315715  & 0.7146115  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF         |  0.7745883  | 0.13419907  | 0.209422903 | 0.24105628  | 0.9232394   | \n",
       "| 5 | STAT4       |  2.6053432  | 0.15719002  | 0.076155038 | 0.09551869  | 0.9047599   | \n",
       "| 35 | SATB1       |  0.0000000  | 0.00000000  | 0.000000000 | 0.00000000  | 0.8628270   | \n",
       "| 15 | SATB2       |  0.1367563  | 0.21613389  | 0.228754703 | 0.26140384  | 0.8423299   | \n",
       "| 33 | ATF2        |  0.0000000  | 0.00000000  | 0.009811994 | 0.00000000  | 0.8278686   | \n",
       "| 4 | FOXP2       |  2.6150851  | 0.10666729  | 0.120449138 | 0.13231032  | 0.8196172   | \n",
       "| 11 | TSHZ2       |  0.6924360  | 0.10456283  | 0.115311122 | 0.09676352  | 0.7704362   | \n",
       "| 40 | DRGX        |  0.0000000  | 0.00000000  | 0.000000000 | 0.00000000  | 0.7309450   | \n",
       "| 1 | HDX         | 11.2649243  | 0.00000000  | 0.000000000 | 0.00000000  | 0.7241906   | \n",
       "| 8 | LHX6        |  1.2795080  | 0.06635221  | 0.090529696 | 0.09315715  | 0.7146115   | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  lasso.as.is lasso.log2 lasso.asinh lasso.voom gene.cor \n",
       "9  HLF    0.7745883  0.13419907 0.209422903 0.24105628 0.9232394\n",
       "5  STAT4  2.6053432  0.15719002 0.076155038 0.09551869 0.9047599\n",
       "35 SATB1  0.0000000  0.00000000 0.000000000 0.00000000 0.8628270\n",
       "15 SATB2  0.1367563  0.21613389 0.228754703 0.26140384 0.8423299\n",
       "33 ATF2   0.0000000  0.00000000 0.009811994 0.00000000 0.8278686\n",
       "4  FOXP2  2.6150851  0.10666729 0.120449138 0.13231032 0.8196172\n",
       "11 TSHZ2  0.6924360  0.10456283 0.115311122 0.09676352 0.7704362\n",
       "40 DRGX   0.0000000  0.00000000 0.000000000 0.00000000 0.7309450\n",
       "1  HDX   11.2649243  0.00000000 0.000000000 0.00000000 0.7241906\n",
       "8  LHX6   1.2795080  0.06635221 0.090529696 0.09315715 0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,2,3,4,5,18)],10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we would expect from LASSO, many genes are missing coefficients because they've been shrunken out of the model. What we wouldn't necessarily expect is that the remaining coefficients don't necessarily line up well with one another. To quantify that, let us compare the LASSO coefficients between transformation types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>lasso.as.is</th><th scope=col>lasso.log2</th><th scope=col>lasso.asinh</th><th scope=col>lasso.voom</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>lasso.as.is</th><td>1.0000000</td><td>0.1891492</td><td>0.2135705</td><td>0.1865346</td></tr>\n",
       "\t<tr><th scope=row>lasso.log2</th><td>0.1891492</td><td>1.0000000</td><td>0.8479843</td><td>0.8549927</td></tr>\n",
       "\t<tr><th scope=row>lasso.asinh</th><td>0.2135705</td><td>0.8479843</td><td>1.0000000</td><td>0.9336374</td></tr>\n",
       "\t<tr><th scope=row>lasso.voom</th><td>0.1865346</td><td>0.8549927</td><td>0.9336374</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & lasso.as.is & lasso.log2 & lasso.asinh & lasso.voom\\\\\n",
       "\\hline\n",
       "\tlasso.as.is & 1.0000000 & 0.1891492 & 0.2135705 & 0.1865346\\\\\n",
       "\tlasso.log2 & 0.1891492 & 1.0000000 & 0.8479843 & 0.8549927\\\\\n",
       "\tlasso.asinh & 0.2135705 & 0.8479843 & 1.0000000 & 0.9336374\\\\\n",
       "\tlasso.voom & 0.1865346 & 0.8549927 & 0.9336374 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | lasso.as.is | lasso.log2 | lasso.asinh | lasso.voom | \n",
       "|---|---|---|---|\n",
       "| lasso.as.is | 1.0000000 | 0.1891492 | 0.2135705 | 0.1865346 | \n",
       "| lasso.log2 | 0.1891492 | 1.0000000 | 0.8479843 | 0.8549927 | \n",
       "| lasso.asinh | 0.2135705 | 0.8479843 | 1.0000000 | 0.9336374 | \n",
       "| lasso.voom | 0.1865346 | 0.8549927 | 0.9336374 | 1.0000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "            lasso.as.is lasso.log2 lasso.asinh lasso.voom\n",
       "lasso.as.is 1.0000000   0.1891492  0.2135705   0.1865346 \n",
       "lasso.log2  0.1891492   1.0000000  0.8479843   0.8549927 \n",
       "lasso.asinh 0.2135705   0.8479843  1.0000000   0.9336374 \n",
       "lasso.voom  0.1865346   0.8549927  0.9336374   1.0000000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For each pairwise comparison, find correlation\n",
    "cor(tbl.all[2:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation between the coefficients found for the \"as-is\" data with either transformed data type aren't particularly high, but transformed matrices are in much better agreement. We can similarly look at the results from using Bayes Spike as the solver:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>bs.as.is</th><th scope=col>bs.log2</th><th scope=col>bs.asinh</th><th scope=col>bs.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF          </td><td> 0.593005603 </td><td> 3.228768e-01</td><td>8.583764e-05 </td><td> 3.468772e-01</td><td>0.9232394    </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4        </td><td> 1.708535525 </td><td> 1.832979e-01</td><td>8.946250e-05 </td><td> 1.708533e-01</td><td>0.9047599    </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1        </td><td>-0.568306798 </td><td>-1.444623e-06</td><td>1.154825e-05 </td><td>-3.633166e-05</td><td>0.8628270    </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2        </td><td> 1.035785852 </td><td> 6.942847e-05</td><td>3.566778e-01 </td><td> 2.853092e-01</td><td>0.8423299    </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2         </td><td> 0.892883655 </td><td> 5.648769e-05</td><td>7.409788e-05 </td><td>-5.996884e-06</td><td>0.8278686    </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2        </td><td> 3.972475657 </td><td> 8.526235e-05</td><td>2.309917e-01 </td><td> 4.508482e-05</td><td>0.8196172    </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2        </td><td> 1.086813411 </td><td> 2.277099e-01</td><td>1.056974e-01 </td><td> 1.235520e-01</td><td>0.7704362    </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX         </td><td>-0.004072208 </td><td> 1.835087e-07</td><td>7.377633e-06 </td><td> 1.398560e-05</td><td>0.7309450    </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX          </td><td> 0.324175587 </td><td>-2.498712e-05</td><td>6.947728e-05 </td><td> 5.735278e-05</td><td>0.7241906    </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6         </td><td> 2.312551744 </td><td> 6.697943e-05</td><td>1.139972e-01 </td><td> 1.365092e-01</td><td>0.7146115    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & bs.as.is & bs.log2 & bs.asinh & bs.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF           &  0.593005603  &  3.228768e-01 & 8.583764e-05  &  3.468772e-01 & 0.9232394    \\\\\n",
       "\t5 & STAT4         &  1.708535525  &  1.832979e-01 & 8.946250e-05  &  1.708533e-01 & 0.9047599    \\\\\n",
       "\t35 & SATB1         & -0.568306798  & -1.444623e-06 & 1.154825e-05  & -3.633166e-05 & 0.8628270    \\\\\n",
       "\t15 & SATB2         &  1.035785852  &  6.942847e-05 & 3.566778e-01  &  2.853092e-01 & 0.8423299    \\\\\n",
       "\t33 & ATF2          &  0.892883655  &  5.648769e-05 & 7.409788e-05  & -5.996884e-06 & 0.8278686    \\\\\n",
       "\t4 & FOXP2         &  3.972475657  &  8.526235e-05 & 2.309917e-01  &  4.508482e-05 & 0.8196172    \\\\\n",
       "\t11 & TSHZ2         &  1.086813411  &  2.277099e-01 & 1.056974e-01  &  1.235520e-01 & 0.7704362    \\\\\n",
       "\t40 & DRGX          & -0.004072208  &  1.835087e-07 & 7.377633e-06  &  1.398560e-05 & 0.7309450    \\\\\n",
       "\t1 & HDX           &  0.324175587  & -2.498712e-05 & 6.947728e-05  &  5.735278e-05 & 0.7241906    \\\\\n",
       "\t8 & LHX6          &  2.312551744  &  6.697943e-05 & 1.139972e-01  &  1.365092e-01 & 0.7146115    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | bs.as.is | bs.log2 | bs.asinh | bs.voom | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF           |  0.593005603  |  3.228768e-01 | 8.583764e-05  |  3.468772e-01 | 0.9232394     | \n",
       "| 5 | STAT4         |  1.708535525  |  1.832979e-01 | 8.946250e-05  |  1.708533e-01 | 0.9047599     | \n",
       "| 35 | SATB1         | -0.568306798  | -1.444623e-06 | 1.154825e-05  | -3.633166e-05 | 0.8628270     | \n",
       "| 15 | SATB2         |  1.035785852  |  6.942847e-05 | 3.566778e-01  |  2.853092e-01 | 0.8423299     | \n",
       "| 33 | ATF2          |  0.892883655  |  5.648769e-05 | 7.409788e-05  | -5.996884e-06 | 0.8278686     | \n",
       "| 4 | FOXP2         |  3.972475657  |  8.526235e-05 | 2.309917e-01  |  4.508482e-05 | 0.8196172     | \n",
       "| 11 | TSHZ2         |  1.086813411  |  2.277099e-01 | 1.056974e-01  |  1.235520e-01 | 0.7704362     | \n",
       "| 40 | DRGX          | -0.004072208  |  1.835087e-07 | 7.377633e-06  |  1.398560e-05 | 0.7309450     | \n",
       "| 1 | HDX           |  0.324175587  | -2.498712e-05 | 6.947728e-05  |  5.735278e-05 | 0.7241906     | \n",
       "| 8 | LHX6          |  2.312551744  |  6.697943e-05 | 1.139972e-01  |  1.365092e-01 | 0.7146115     | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  bs.as.is     bs.log2       bs.asinh     bs.voom       gene.cor \n",
       "9  HLF    0.593005603  3.228768e-01 8.583764e-05  3.468772e-01 0.9232394\n",
       "5  STAT4  1.708535525  1.832979e-01 8.946250e-05  1.708533e-01 0.9047599\n",
       "35 SATB1 -0.568306798 -1.444623e-06 1.154825e-05 -3.633166e-05 0.8628270\n",
       "15 SATB2  1.035785852  6.942847e-05 3.566778e-01  2.853092e-01 0.8423299\n",
       "33 ATF2   0.892883655  5.648769e-05 7.409788e-05 -5.996884e-06 0.8278686\n",
       "4  FOXP2  3.972475657  8.526235e-05 2.309917e-01  4.508482e-05 0.8196172\n",
       "11 TSHZ2  1.086813411  2.277099e-01 1.056974e-01  1.235520e-01 0.7704362\n",
       "40 DRGX  -0.004072208  1.835087e-07 7.377633e-06  1.398560e-05 0.7309450\n",
       "1  HDX    0.324175587 -2.498712e-05 6.947728e-05  5.735278e-05 0.7241906\n",
       "8  LHX6   2.312551744  6.697943e-05 1.139972e-01  1.365092e-01 0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,6:9,18)],10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From visual inspection, these coefficients are a bit all over the map in terms of magnitude and sign. Additionally, most of the coefficients for the log-transformed, and many from the asinh-transformed, are quite small. Let's look at these correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>bs.as.is</th><th scope=col>bs.log2</th><th scope=col>bs.asinh</th><th scope=col>bs.voom</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>bs.as.is</th><td>1.00000000</td><td>0.05740733</td><td>0.07552436</td><td>0.04127314</td></tr>\n",
       "\t<tr><th scope=row>bs.log2</th><td>0.05740733</td><td>1.00000000</td><td>0.16902615</td><td>0.47330622</td></tr>\n",
       "\t<tr><th scope=row>bs.asinh</th><td>0.07552436</td><td>0.16902615</td><td>1.00000000</td><td>0.30836121</td></tr>\n",
       "\t<tr><th scope=row>bs.voom</th><td>0.04127314</td><td>0.47330622</td><td>0.30836121</td><td>1.00000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & bs.as.is & bs.log2 & bs.asinh & bs.voom\\\\\n",
       "\\hline\n",
       "\tbs.as.is & 1.00000000 & 0.05740733 & 0.07552436 & 0.04127314\\\\\n",
       "\tbs.log2 & 0.05740733 & 1.00000000 & 0.16902615 & 0.47330622\\\\\n",
       "\tbs.asinh & 0.07552436 & 0.16902615 & 1.00000000 & 0.30836121\\\\\n",
       "\tbs.voom & 0.04127314 & 0.47330622 & 0.30836121 & 1.00000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | bs.as.is | bs.log2 | bs.asinh | bs.voom | \n",
       "|---|---|---|---|\n",
       "| bs.as.is | 1.00000000 | 0.05740733 | 0.07552436 | 0.04127314 | \n",
       "| bs.log2 | 0.05740733 | 1.00000000 | 0.16902615 | 0.47330622 | \n",
       "| bs.asinh | 0.07552436 | 0.16902615 | 1.00000000 | 0.30836121 | \n",
       "| bs.voom | 0.04127314 | 0.47330622 | 0.30836121 | 1.00000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "         bs.as.is   bs.log2    bs.asinh   bs.voom   \n",
       "bs.as.is 1.00000000 0.05740733 0.07552436 0.04127314\n",
       "bs.log2  0.05740733 1.00000000 0.16902615 0.47330622\n",
       "bs.asinh 0.07552436 0.16902615 1.00000000 0.30836121\n",
       "bs.voom  0.04127314 0.47330622 0.30836121 1.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor(tbl.all[6:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be very little agreement between the as-is Bayes Spike coefficients and the other transformations, but there's some agreement in the asinh and voom transformed datasets. This is somewhat similar to what we saw for the LASSO solver, though all correlations for the Bayes Spike coefficients are weaker. \n",
    "\n",
    "Let's continue our survey of the solvers by looking at the Random Forest scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>rf.as.is</th><th scope=col>rf.log2</th><th scope=col>rf.asinh</th><th scope=col>rf.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF       </td><td>1266161.95</td><td>57.371322 </td><td>25.9160383</td><td>53.0524708</td><td>0.9232394 </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4     </td><td> 649234.98</td><td>46.374066 </td><td>22.4786757</td><td>48.9494236</td><td>0.9047599 </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1     </td><td> 577058.44</td><td>21.194070 </td><td>11.9869878</td><td>27.9882393</td><td>0.8628270 </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2     </td><td> 424329.70</td><td>26.355675 </td><td>14.2251862</td><td>30.4646927</td><td>0.8423299 </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2      </td><td> 137474.98</td><td> 5.411294 </td><td> 2.3401488</td><td> 7.3519720</td><td>0.8278686 </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2     </td><td> 112640.28</td><td> 3.914452 </td><td> 2.9705266</td><td> 3.8044576</td><td>0.8196172 </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2     </td><td>  84319.20</td><td> 5.122529 </td><td> 2.2511790</td><td> 4.6044307</td><td>0.7704362 </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX      </td><td>  88685.56</td><td>10.128620 </td><td> 3.2287507</td><td> 8.6275128</td><td>0.7309450 </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX       </td><td>  53018.02</td><td> 1.436412 </td><td> 0.7157802</td><td> 0.8661305</td><td>0.7241906 </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6      </td><td>  33707.16</td><td> 2.907339 </td><td> 1.7027348</td><td> 2.2815335</td><td>0.7146115 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & rf.as.is & rf.log2 & rf.asinh & rf.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF        & 1266161.95 & 57.371322  & 25.9160383 & 53.0524708 & 0.9232394 \\\\\n",
       "\t5 & STAT4      &  649234.98 & 46.374066  & 22.4786757 & 48.9494236 & 0.9047599 \\\\\n",
       "\t35 & SATB1      &  577058.44 & 21.194070  & 11.9869878 & 27.9882393 & 0.8628270 \\\\\n",
       "\t15 & SATB2      &  424329.70 & 26.355675  & 14.2251862 & 30.4646927 & 0.8423299 \\\\\n",
       "\t33 & ATF2       &  137474.98 &  5.411294  &  2.3401488 &  7.3519720 & 0.8278686 \\\\\n",
       "\t4 & FOXP2      &  112640.28 &  3.914452  &  2.9705266 &  3.8044576 & 0.8196172 \\\\\n",
       "\t11 & TSHZ2      &   84319.20 &  5.122529  &  2.2511790 &  4.6044307 & 0.7704362 \\\\\n",
       "\t40 & DRGX       &   88685.56 & 10.128620  &  3.2287507 &  8.6275128 & 0.7309450 \\\\\n",
       "\t1 & HDX        &   53018.02 &  1.436412  &  0.7157802 &  0.8661305 & 0.7241906 \\\\\n",
       "\t8 & LHX6       &   33707.16 &  2.907339  &  1.7027348 &  2.2815335 & 0.7146115 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | rf.as.is | rf.log2 | rf.asinh | rf.voom | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF        | 1266161.95 | 57.371322  | 25.9160383 | 53.0524708 | 0.9232394  | \n",
       "| 5 | STAT4      |  649234.98 | 46.374066  | 22.4786757 | 48.9494236 | 0.9047599  | \n",
       "| 35 | SATB1      |  577058.44 | 21.194070  | 11.9869878 | 27.9882393 | 0.8628270  | \n",
       "| 15 | SATB2      |  424329.70 | 26.355675  | 14.2251862 | 30.4646927 | 0.8423299  | \n",
       "| 33 | ATF2       |  137474.98 |  5.411294  |  2.3401488 |  7.3519720 | 0.8278686  | \n",
       "| 4 | FOXP2      |  112640.28 |  3.914452  |  2.9705266 |  3.8044576 | 0.8196172  | \n",
       "| 11 | TSHZ2      |   84319.20 |  5.122529  |  2.2511790 |  4.6044307 | 0.7704362  | \n",
       "| 40 | DRGX       |   88685.56 | 10.128620  |  3.2287507 |  8.6275128 | 0.7309450  | \n",
       "| 1 | HDX        |   53018.02 |  1.436412  |  0.7157802 |  0.8661305 | 0.7241906  | \n",
       "| 8 | LHX6       |   33707.16 |  2.907339  |  1.7027348 |  2.2815335 | 0.7146115  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  rf.as.is   rf.log2   rf.asinh   rf.voom    gene.cor \n",
       "9  HLF   1266161.95 57.371322 25.9160383 53.0524708 0.9232394\n",
       "5  STAT4  649234.98 46.374066 22.4786757 48.9494236 0.9047599\n",
       "35 SATB1  577058.44 21.194070 11.9869878 27.9882393 0.8628270\n",
       "15 SATB2  424329.70 26.355675 14.2251862 30.4646927 0.8423299\n",
       "33 ATF2   137474.98  5.411294  2.3401488  7.3519720 0.8278686\n",
       "4  FOXP2  112640.28  3.914452  2.9705266  3.8044576 0.8196172\n",
       "11 TSHZ2   84319.20  5.122529  2.2511790  4.6044307 0.7704362\n",
       "40 DRGX    88685.56 10.128620  3.2287507  8.6275128 0.7309450\n",
       "1  HDX     53018.02  1.436412  0.7157802  0.8661305 0.7241906\n",
       "8  LHX6    33707.16  2.907339  1.7027348  2.2815335 0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,10:13,18)],10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks a lot more in line with what we would expect; the Random Forest scores look to decrease in size almost monotonically along with the Pearson correlations. Furthermore, they look to do so regardless of whether or not the data have been transformed, although the coefficient sizes vary quite a bit. Let's follow up with the correlations between different transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>rf.as.is</th><th scope=col>rf.log2</th><th scope=col>rf.asinh</th><th scope=col>rf.voom</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>rf.as.is</th><td>1.0000000</td><td>0.9650516</td><td>0.9604815</td><td>0.9529617</td></tr>\n",
       "\t<tr><th scope=row>rf.log2</th><td>0.9650516</td><td>1.0000000</td><td>0.9949891</td><td>0.9913273</td></tr>\n",
       "\t<tr><th scope=row>rf.asinh</th><td>0.9604815</td><td>0.9949891</td><td>1.0000000</td><td>0.9972757</td></tr>\n",
       "\t<tr><th scope=row>rf.voom</th><td>0.9529617</td><td>0.9913273</td><td>0.9972757</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & rf.as.is & rf.log2 & rf.asinh & rf.voom\\\\\n",
       "\\hline\n",
       "\trf.as.is & 1.0000000 & 0.9650516 & 0.9604815 & 0.9529617\\\\\n",
       "\trf.log2 & 0.9650516 & 1.0000000 & 0.9949891 & 0.9913273\\\\\n",
       "\trf.asinh & 0.9604815 & 0.9949891 & 1.0000000 & 0.9972757\\\\\n",
       "\trf.voom & 0.9529617 & 0.9913273 & 0.9972757 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | rf.as.is | rf.log2 | rf.asinh | rf.voom | \n",
       "|---|---|---|---|\n",
       "| rf.as.is | 1.0000000 | 0.9650516 | 0.9604815 | 0.9529617 | \n",
       "| rf.log2 | 0.9650516 | 1.0000000 | 0.9949891 | 0.9913273 | \n",
       "| rf.asinh | 0.9604815 | 0.9949891 | 1.0000000 | 0.9972757 | \n",
       "| rf.voom | 0.9529617 | 0.9913273 | 0.9972757 | 1.0000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "         rf.as.is  rf.log2   rf.asinh  rf.voom  \n",
       "rf.as.is 1.0000000 0.9650516 0.9604815 0.9529617\n",
       "rf.log2  0.9650516 1.0000000 0.9949891 0.9913273\n",
       "rf.asinh 0.9604815 0.9949891 1.0000000 0.9972757\n",
       "rf.voom  0.9529617 0.9913273 0.9972757 1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor(tbl.all[10:13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation coefficients between matrix types suggests very high agreement in all cases, and nearly identical trends when using log-transformed and asinh-transformed data. This is important information as we think about using Random Forest on other datasets; we can likely expect that regardless of transformation, we'll likely find similar results. \n",
    "\n",
    "Let's finish our survey of solvers by looking at Square Root LASSO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>sqrt.lasso.as.is</th><th scope=col>sqrt.lasso.log2</th><th scope=col>sqrt.lasso.asinh</th><th scope=col>sqrt.lasso.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF        </td><td> 0.69519133</td><td>0.140776460</td><td>0.14289609 </td><td>0.15897283 </td><td>0.9232394  </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4      </td><td> 3.47937884</td><td>0.190961358</td><td>0.15896033 </td><td>0.17336532 </td><td>0.9047599  </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1      </td><td> 0.00000000</td><td>0.000000000</td><td>0.00000000 </td><td>0.00000000 </td><td>0.8628270  </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2      </td><td> 0.02139235</td><td>0.181298914</td><td>0.22652672 </td><td>0.23814460 </td><td>0.8423299  </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2       </td><td> 0.08809962</td><td>0.008485763</td><td>0.00000000 </td><td>0.00000000 </td><td>0.8278686  </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2      </td><td> 2.44496515</td><td>0.101105760</td><td>0.10635813 </td><td>0.10469561 </td><td>0.8196172  </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2      </td><td> 1.00207756</td><td>0.100506784</td><td>0.09486455 </td><td>0.08923052 </td><td>0.7704362  </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX       </td><td> 1.18904754</td><td>0.000000000</td><td>0.00000000 </td><td>0.00000000 </td><td>0.7309450  </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX        </td><td>13.37342127</td><td>0.000000000</td><td>0.00000000 </td><td>0.00000000 </td><td>0.7241906  </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6       </td><td> 2.69414714</td><td>0.058348346</td><td>0.08655747 </td><td>0.09288872 </td><td>0.7146115  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & sqrt.lasso.as.is & sqrt.lasso.log2 & sqrt.lasso.asinh & sqrt.lasso.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF         &  0.69519133 & 0.140776460 & 0.14289609  & 0.15897283  & 0.9232394  \\\\\n",
       "\t5 & STAT4       &  3.47937884 & 0.190961358 & 0.15896033  & 0.17336532  & 0.9047599  \\\\\n",
       "\t35 & SATB1       &  0.00000000 & 0.000000000 & 0.00000000  & 0.00000000  & 0.8628270  \\\\\n",
       "\t15 & SATB2       &  0.02139235 & 0.181298914 & 0.22652672  & 0.23814460  & 0.8423299  \\\\\n",
       "\t33 & ATF2        &  0.08809962 & 0.008485763 & 0.00000000  & 0.00000000  & 0.8278686  \\\\\n",
       "\t4 & FOXP2       &  2.44496515 & 0.101105760 & 0.10635813  & 0.10469561  & 0.8196172  \\\\\n",
       "\t11 & TSHZ2       &  1.00207756 & 0.100506784 & 0.09486455  & 0.08923052  & 0.7704362  \\\\\n",
       "\t40 & DRGX        &  1.18904754 & 0.000000000 & 0.00000000  & 0.00000000  & 0.7309450  \\\\\n",
       "\t1 & HDX         & 13.37342127 & 0.000000000 & 0.00000000  & 0.00000000  & 0.7241906  \\\\\n",
       "\t8 & LHX6        &  2.69414714 & 0.058348346 & 0.08655747  & 0.09288872  & 0.7146115  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF         |  0.69519133 | 0.140776460 | 0.14289609  | 0.15897283  | 0.9232394   | \n",
       "| 5 | STAT4       |  3.47937884 | 0.190961358 | 0.15896033  | 0.17336532  | 0.9047599   | \n",
       "| 35 | SATB1       |  0.00000000 | 0.000000000 | 0.00000000  | 0.00000000  | 0.8628270   | \n",
       "| 15 | SATB2       |  0.02139235 | 0.181298914 | 0.22652672  | 0.23814460  | 0.8423299   | \n",
       "| 33 | ATF2        |  0.08809962 | 0.008485763 | 0.00000000  | 0.00000000  | 0.8278686   | \n",
       "| 4 | FOXP2       |  2.44496515 | 0.101105760 | 0.10635813  | 0.10469561  | 0.8196172   | \n",
       "| 11 | TSHZ2       |  1.00207756 | 0.100506784 | 0.09486455  | 0.08923052  | 0.7704362   | \n",
       "| 40 | DRGX        |  1.18904754 | 0.000000000 | 0.00000000  | 0.00000000  | 0.7309450   | \n",
       "| 1 | HDX         | 13.37342127 | 0.000000000 | 0.00000000  | 0.00000000  | 0.7241906   | \n",
       "| 8 | LHX6        |  2.69414714 | 0.058348346 | 0.08655747  | 0.09288872  | 0.7146115   | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  sqrt.lasso.as.is sqrt.lasso.log2 sqrt.lasso.asinh sqrt.lasso.voom\n",
       "9  HLF    0.69519133      0.140776460     0.14289609       0.15897283     \n",
       "5  STAT4  3.47937884      0.190961358     0.15896033       0.17336532     \n",
       "35 SATB1  0.00000000      0.000000000     0.00000000       0.00000000     \n",
       "15 SATB2  0.02139235      0.181298914     0.22652672       0.23814460     \n",
       "33 ATF2   0.08809962      0.008485763     0.00000000       0.00000000     \n",
       "4  FOXP2  2.44496515      0.101105760     0.10635813       0.10469561     \n",
       "11 TSHZ2  1.00207756      0.100506784     0.09486455       0.08923052     \n",
       "40 DRGX   1.18904754      0.000000000     0.00000000       0.00000000     \n",
       "1  HDX   13.37342127      0.000000000     0.00000000       0.00000000     \n",
       "8  LHX6   2.69414714      0.058348346     0.08655747       0.09288872     \n",
       "   gene.cor \n",
       "9  0.9232394\n",
       "5  0.9047599\n",
       "35 0.8628270\n",
       "15 0.8423299\n",
       "33 0.8278686\n",
       "4  0.8196172\n",
       "11 0.7704362\n",
       "40 0.7309450\n",
       "1  0.7241906\n",
       "8  0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,14:17,18)],10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much like regular LASSO, we see many coefficients shrunken to 0 and there seems to be good agreement in which coefficients are 0 in the transformed cases, particularly between the asinh and voom transformations. We'll look at the correlations to get a better look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>sqrt.lasso.as.is</th><th scope=col>sqrt.lasso.log2</th><th scope=col>sqrt.lasso.asinh</th><th scope=col>sqrt.lasso.voom</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>sqrt.lasso.as.is</th><td>1.0000000</td><td>0.1854567</td><td>0.2254451</td><td>0.1996011</td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.log2</th><td>0.1854567</td><td>1.0000000</td><td>0.9597672</td><td>0.9139551</td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.asinh</th><td>0.2254451</td><td>0.9597672</td><td>1.0000000</td><td>0.9490039</td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.voom</th><td>0.1996011</td><td>0.9139551</td><td>0.9490039</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & sqrt.lasso.as.is & sqrt.lasso.log2 & sqrt.lasso.asinh & sqrt.lasso.voom\\\\\n",
       "\\hline\n",
       "\tsqrt.lasso.as.is & 1.0000000 & 0.1854567 & 0.2254451 & 0.1996011\\\\\n",
       "\tsqrt.lasso.log2 & 0.1854567 & 1.0000000 & 0.9597672 & 0.9139551\\\\\n",
       "\tsqrt.lasso.asinh & 0.2254451 & 0.9597672 & 1.0000000 & 0.9490039\\\\\n",
       "\tsqrt.lasso.voom & 0.1996011 & 0.9139551 & 0.9490039 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | sqrt.lasso.as.is | sqrt.lasso.log2 | sqrt.lasso.asinh | sqrt.lasso.voom | \n",
       "|---|---|---|---|\n",
       "| sqrt.lasso.as.is | 1.0000000 | 0.1854567 | 0.2254451 | 0.1996011 | \n",
       "| sqrt.lasso.log2 | 0.1854567 | 1.0000000 | 0.9597672 | 0.9139551 | \n",
       "| sqrt.lasso.asinh | 0.2254451 | 0.9597672 | 1.0000000 | 0.9490039 | \n",
       "| sqrt.lasso.voom | 0.1996011 | 0.9139551 | 0.9490039 | 1.0000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                 sqrt.lasso.as.is sqrt.lasso.log2 sqrt.lasso.asinh\n",
       "sqrt.lasso.as.is 1.0000000        0.1854567       0.2254451       \n",
       "sqrt.lasso.log2  0.1854567        1.0000000       0.9597672       \n",
       "sqrt.lasso.asinh 0.2254451        0.9597672       1.0000000       \n",
       "sqrt.lasso.voom  0.1996011        0.9139551       0.9490039       \n",
       "                 sqrt.lasso.voom\n",
       "sqrt.lasso.as.is 0.1996011      \n",
       "sqrt.lasso.log2  0.9139551      \n",
       "sqrt.lasso.asinh 0.9490039      \n",
       "sqrt.lasso.voom  1.0000000      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor(tbl.all[14:17])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The as-is coefficients don't really line up with the others, but all 3 transformations are in close agreement here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Inspect Trends Between Solvers\n",
    "\n",
    "From looking at the within-solvers comparisons, it seems evident that we can probably expect some major differences between solver types. Let's look first at the as-is data across solvers; we'll look at the columns in a table and compute the correlation coefficients concurrently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>lasso.as.is</th><th scope=col>bs.as.is</th><th scope=col>rf.as.is</th><th scope=col>sqrt.lasso.as.is</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF         </td><td> 0.7745883  </td><td> 0.593005603</td><td>1266161.95  </td><td> 0.69519133 </td><td>0.9232394   </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4       </td><td> 2.6053432  </td><td> 1.708535525</td><td> 649234.98  </td><td> 3.47937884 </td><td>0.9047599   </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1       </td><td> 0.0000000  </td><td>-0.568306798</td><td> 577058.44  </td><td> 0.00000000 </td><td>0.8628270   </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2       </td><td> 0.1367563  </td><td> 1.035785852</td><td> 424329.70  </td><td> 0.02139235 </td><td>0.8423299   </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2        </td><td> 0.0000000  </td><td> 0.892883655</td><td> 137474.98  </td><td> 0.08809962 </td><td>0.8278686   </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2       </td><td> 2.6150851  </td><td> 3.972475657</td><td> 112640.28  </td><td> 2.44496515 </td><td>0.8196172   </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2       </td><td> 0.6924360  </td><td> 1.086813411</td><td>  84319.20  </td><td> 1.00207756 </td><td>0.7704362   </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX        </td><td> 0.0000000  </td><td>-0.004072208</td><td>  88685.56  </td><td> 1.18904754 </td><td>0.7309450   </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX         </td><td>11.2649243  </td><td> 0.324175587</td><td>  53018.02  </td><td>13.37342127 </td><td>0.7241906   </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6        </td><td> 1.2795080  </td><td> 2.312551744</td><td>  33707.16  </td><td> 2.69414714 </td><td>0.7146115   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & lasso.as.is & bs.as.is & rf.as.is & sqrt.lasso.as.is & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF          &  0.7745883   &  0.593005603 & 1266161.95   &  0.69519133  & 0.9232394   \\\\\n",
       "\t5 & STAT4        &  2.6053432   &  1.708535525 &  649234.98   &  3.47937884  & 0.9047599   \\\\\n",
       "\t35 & SATB1        &  0.0000000   & -0.568306798 &  577058.44   &  0.00000000  & 0.8628270   \\\\\n",
       "\t15 & SATB2        &  0.1367563   &  1.035785852 &  424329.70   &  0.02139235  & 0.8423299   \\\\\n",
       "\t33 & ATF2         &  0.0000000   &  0.892883655 &  137474.98   &  0.08809962  & 0.8278686   \\\\\n",
       "\t4 & FOXP2        &  2.6150851   &  3.972475657 &  112640.28   &  2.44496515  & 0.8196172   \\\\\n",
       "\t11 & TSHZ2        &  0.6924360   &  1.086813411 &   84319.20   &  1.00207756  & 0.7704362   \\\\\n",
       "\t40 & DRGX         &  0.0000000   & -0.004072208 &   88685.56   &  1.18904754  & 0.7309450   \\\\\n",
       "\t1 & HDX          & 11.2649243   &  0.324175587 &   53018.02   & 13.37342127  & 0.7241906   \\\\\n",
       "\t8 & LHX6         &  1.2795080   &  2.312551744 &   33707.16   &  2.69414714  & 0.7146115   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | lasso.as.is | bs.as.is | rf.as.is | sqrt.lasso.as.is | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF          |  0.7745883   |  0.593005603 | 1266161.95   |  0.69519133  | 0.9232394    | \n",
       "| 5 | STAT4        |  2.6053432   |  1.708535525 |  649234.98   |  3.47937884  | 0.9047599    | \n",
       "| 35 | SATB1        |  0.0000000   | -0.568306798 |  577058.44   |  0.00000000  | 0.8628270    | \n",
       "| 15 | SATB2        |  0.1367563   |  1.035785852 |  424329.70   |  0.02139235  | 0.8423299    | \n",
       "| 33 | ATF2         |  0.0000000   |  0.892883655 |  137474.98   |  0.08809962  | 0.8278686    | \n",
       "| 4 | FOXP2        |  2.6150851   |  3.972475657 |  112640.28   |  2.44496515  | 0.8196172    | \n",
       "| 11 | TSHZ2        |  0.6924360   |  1.086813411 |   84319.20   |  1.00207756  | 0.7704362    | \n",
       "| 40 | DRGX         |  0.0000000   | -0.004072208 |   88685.56   |  1.18904754  | 0.7309450    | \n",
       "| 1 | HDX          | 11.2649243   |  0.324175587 |   53018.02   | 13.37342127  | 0.7241906    | \n",
       "| 8 | LHX6         |  1.2795080   |  2.312551744 |   33707.16   |  2.69414714  | 0.7146115    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  lasso.as.is bs.as.is     rf.as.is   sqrt.lasso.as.is gene.cor \n",
       "9  HLF    0.7745883   0.593005603 1266161.95  0.69519133      0.9232394\n",
       "5  STAT4  2.6053432   1.708535525  649234.98  3.47937884      0.9047599\n",
       "35 SATB1  0.0000000  -0.568306798  577058.44  0.00000000      0.8628270\n",
       "15 SATB2  0.1367563   1.035785852  424329.70  0.02139235      0.8423299\n",
       "33 ATF2   0.0000000   0.892883655  137474.98  0.08809962      0.8278686\n",
       "4  FOXP2  2.6150851   3.972475657  112640.28  2.44496515      0.8196172\n",
       "11 TSHZ2  0.6924360   1.086813411   84319.20  1.00207756      0.7704362\n",
       "40 DRGX   0.0000000  -0.004072208   88685.56  1.18904754      0.7309450\n",
       "1  HDX   11.2649243   0.324175587   53018.02 13.37342127      0.7241906\n",
       "8  LHX6   1.2795080   2.312551744   33707.16  2.69414714      0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>lasso.as.is</th><th scope=col>bs.as.is</th><th scope=col>rf.as.is</th><th scope=col>sqrt.lasso.as.is</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>lasso.as.is</th><td>1.0000000  </td><td> 0.11796108</td><td>0.14940040 </td><td> 0.79793209</td><td>0.3394498  </td></tr>\n",
       "\t<tr><th scope=row>bs.as.is</th><td>0.1179611  </td><td> 1.00000000</td><td>0.07497072 </td><td>-0.01188095</td><td>0.1611727  </td></tr>\n",
       "\t<tr><th scope=row>rf.as.is</th><td>0.1494004  </td><td> 0.07497072</td><td>1.00000000 </td><td> 0.12236983</td><td>0.5062618  </td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.as.is</th><td>0.7979321  </td><td>-0.01188095</td><td>0.12236983 </td><td> 1.00000000</td><td>0.3866010  </td></tr>\n",
       "\t<tr><th scope=row>gene.cor</th><td>0.3394498  </td><td> 0.16117270</td><td>0.50626182 </td><td> 0.38660105</td><td>1.0000000  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & lasso.as.is & bs.as.is & rf.as.is & sqrt.lasso.as.is & gene.cor\\\\\n",
       "\\hline\n",
       "\tlasso.as.is & 1.0000000   &  0.11796108 & 0.14940040  &  0.79793209 & 0.3394498  \\\\\n",
       "\tbs.as.is & 0.1179611   &  1.00000000 & 0.07497072  & -0.01188095 & 0.1611727  \\\\\n",
       "\trf.as.is & 0.1494004   &  0.07497072 & 1.00000000  &  0.12236983 & 0.5062618  \\\\\n",
       "\tsqrt.lasso.as.is & 0.7979321   & -0.01188095 & 0.12236983  &  1.00000000 & 0.3866010  \\\\\n",
       "\tgene.cor & 0.3394498   &  0.16117270 & 0.50626182  &  0.38660105 & 1.0000000  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | lasso.as.is | bs.as.is | rf.as.is | sqrt.lasso.as.is | gene.cor | \n",
       "|---|---|---|---|---|\n",
       "| lasso.as.is | 1.0000000   |  0.11796108 | 0.14940040  |  0.79793209 | 0.3394498   | \n",
       "| bs.as.is | 0.1179611   |  1.00000000 | 0.07497072  | -0.01188095 | 0.1611727   | \n",
       "| rf.as.is | 0.1494004   |  0.07497072 | 1.00000000  |  0.12236983 | 0.5062618   | \n",
       "| sqrt.lasso.as.is | 0.7979321   | -0.01188095 | 0.12236983  |  1.00000000 | 0.3866010   | \n",
       "| gene.cor | 0.3394498   |  0.16117270 | 0.50626182  |  0.38660105 | 1.0000000   | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                 lasso.as.is bs.as.is    rf.as.is   sqrt.lasso.as.is gene.cor \n",
       "lasso.as.is      1.0000000    0.11796108 0.14940040  0.79793209      0.3394498\n",
       "bs.as.is         0.1179611    1.00000000 0.07497072 -0.01188095      0.1611727\n",
       "rf.as.is         0.1494004    0.07497072 1.00000000  0.12236983      0.5062618\n",
       "sqrt.lasso.as.is 0.7979321   -0.01188095 0.12236983  1.00000000      0.3866010\n",
       "gene.cor         0.3394498    0.16117270 0.50626182  0.38660105      1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,2,6,10,14,18)],10)\n",
    "cor(tbl.all[c(2,6,10,14,18)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we might expect, the 2 LASSO variations have the highest correlation, but otherwise there's not very good agreement between solvers. In terms of the gene correlations themselves, the Random Forest solver has the strongest agreement for the \"as-is\" data matrix, both LASSO variations are a little weaker, and the Bayes Spike shows virtually no correlation with the Pearson coefficients. Let us move on to the transformations to see what differences there are; we'll begin with log2-transformed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>lasso.log2</th><th scope=col>bs.log2</th><th scope=col>rf.log2</th><th scope=col>sqrt.lasso.log2</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF          </td><td>0.13419907   </td><td> 3.228768e-01</td><td>57.371322    </td><td>0.140776460  </td><td>0.9232394    </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4        </td><td>0.15719002   </td><td> 1.832979e-01</td><td>46.374066    </td><td>0.190961358  </td><td>0.9047599    </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1        </td><td>0.00000000   </td><td>-1.444623e-06</td><td>21.194070    </td><td>0.000000000  </td><td>0.8628270    </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2        </td><td>0.21613389   </td><td> 6.942847e-05</td><td>26.355675    </td><td>0.181298914  </td><td>0.8423299    </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2         </td><td>0.00000000   </td><td> 5.648769e-05</td><td> 5.411294    </td><td>0.008485763  </td><td>0.8278686    </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2        </td><td>0.10666729   </td><td> 8.526235e-05</td><td> 3.914452    </td><td>0.101105760  </td><td>0.8196172    </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2        </td><td>0.10456283   </td><td> 2.277099e-01</td><td> 5.122529    </td><td>0.100506784  </td><td>0.7704362    </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX         </td><td>0.00000000   </td><td> 1.835087e-07</td><td>10.128620    </td><td>0.000000000  </td><td>0.7309450    </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX          </td><td>0.00000000   </td><td>-2.498712e-05</td><td> 1.436412    </td><td>0.000000000  </td><td>0.7241906    </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6         </td><td>0.06635221   </td><td> 6.697943e-05</td><td> 2.907339    </td><td>0.058348346  </td><td>0.7146115    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & lasso.log2 & bs.log2 & rf.log2 & sqrt.lasso.log2 & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF           & 0.13419907    &  3.228768e-01 & 57.371322     & 0.140776460   & 0.9232394    \\\\\n",
       "\t5 & STAT4         & 0.15719002    &  1.832979e-01 & 46.374066     & 0.190961358   & 0.9047599    \\\\\n",
       "\t35 & SATB1         & 0.00000000    & -1.444623e-06 & 21.194070     & 0.000000000   & 0.8628270    \\\\\n",
       "\t15 & SATB2         & 0.21613389    &  6.942847e-05 & 26.355675     & 0.181298914   & 0.8423299    \\\\\n",
       "\t33 & ATF2          & 0.00000000    &  5.648769e-05 &  5.411294     & 0.008485763   & 0.8278686    \\\\\n",
       "\t4 & FOXP2         & 0.10666729    &  8.526235e-05 &  3.914452     & 0.101105760   & 0.8196172    \\\\\n",
       "\t11 & TSHZ2         & 0.10456283    &  2.277099e-01 &  5.122529     & 0.100506784   & 0.7704362    \\\\\n",
       "\t40 & DRGX          & 0.00000000    &  1.835087e-07 & 10.128620     & 0.000000000   & 0.7309450    \\\\\n",
       "\t1 & HDX           & 0.00000000    & -2.498712e-05 &  1.436412     & 0.000000000   & 0.7241906    \\\\\n",
       "\t8 & LHX6          & 0.06635221    &  6.697943e-05 &  2.907339     & 0.058348346   & 0.7146115    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | lasso.log2 | bs.log2 | rf.log2 | sqrt.lasso.log2 | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF           | 0.13419907    |  3.228768e-01 | 57.371322     | 0.140776460   | 0.9232394     | \n",
       "| 5 | STAT4         | 0.15719002    |  1.832979e-01 | 46.374066     | 0.190961358   | 0.9047599     | \n",
       "| 35 | SATB1         | 0.00000000    | -1.444623e-06 | 21.194070     | 0.000000000   | 0.8628270     | \n",
       "| 15 | SATB2         | 0.21613389    |  6.942847e-05 | 26.355675     | 0.181298914   | 0.8423299     | \n",
       "| 33 | ATF2          | 0.00000000    |  5.648769e-05 |  5.411294     | 0.008485763   | 0.8278686     | \n",
       "| 4 | FOXP2         | 0.10666729    |  8.526235e-05 |  3.914452     | 0.101105760   | 0.8196172     | \n",
       "| 11 | TSHZ2         | 0.10456283    |  2.277099e-01 |  5.122529     | 0.100506784   | 0.7704362     | \n",
       "| 40 | DRGX          | 0.00000000    |  1.835087e-07 | 10.128620     | 0.000000000   | 0.7309450     | \n",
       "| 1 | HDX           | 0.00000000    | -2.498712e-05 |  1.436412     | 0.000000000   | 0.7241906     | \n",
       "| 8 | LHX6          | 0.06635221    |  6.697943e-05 |  2.907339     | 0.058348346   | 0.7146115     | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  lasso.log2 bs.log2       rf.log2   sqrt.lasso.log2 gene.cor \n",
       "9  HLF   0.13419907  3.228768e-01 57.371322 0.140776460     0.9232394\n",
       "5  STAT4 0.15719002  1.832979e-01 46.374066 0.190961358     0.9047599\n",
       "35 SATB1 0.00000000 -1.444623e-06 21.194070 0.000000000     0.8628270\n",
       "15 SATB2 0.21613389  6.942847e-05 26.355675 0.181298914     0.8423299\n",
       "33 ATF2  0.00000000  5.648769e-05  5.411294 0.008485763     0.8278686\n",
       "4  FOXP2 0.10666729  8.526235e-05  3.914452 0.101105760     0.8196172\n",
       "11 TSHZ2 0.10456283  2.277099e-01  5.122529 0.100506784     0.7704362\n",
       "40 DRGX  0.00000000  1.835087e-07 10.128620 0.000000000     0.7309450\n",
       "1  HDX   0.00000000 -2.498712e-05  1.436412 0.000000000     0.7241906\n",
       "8  LHX6  0.06635221  6.697943e-05  2.907339 0.058348346     0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>lasso.log2</th><th scope=col>bs.log2</th><th scope=col>rf.log2</th><th scope=col>sqrt.lasso.log2</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>lasso.log2</th><td>1.0000000</td><td>0.4912528</td><td>0.6601102</td><td>0.9852655</td><td>0.7078153</td></tr>\n",
       "\t<tr><th scope=row>bs.log2</th><td>0.4912528</td><td>1.0000000</td><td>0.5400662</td><td>0.5151536</td><td>0.4787279</td></tr>\n",
       "\t<tr><th scope=row>rf.log2</th><td>0.6601102</td><td>0.5400662</td><td>1.0000000</td><td>0.7166040</td><td>0.5547292</td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.log2</th><td>0.9852655</td><td>0.5151536</td><td>0.7166040</td><td>1.0000000</td><td>0.7057869</td></tr>\n",
       "\t<tr><th scope=row>gene.cor</th><td>0.7078153</td><td>0.4787279</td><td>0.5547292</td><td>0.7057869</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & lasso.log2 & bs.log2 & rf.log2 & sqrt.lasso.log2 & gene.cor\\\\\n",
       "\\hline\n",
       "\tlasso.log2 & 1.0000000 & 0.4912528 & 0.6601102 & 0.9852655 & 0.7078153\\\\\n",
       "\tbs.log2 & 0.4912528 & 1.0000000 & 0.5400662 & 0.5151536 & 0.4787279\\\\\n",
       "\trf.log2 & 0.6601102 & 0.5400662 & 1.0000000 & 0.7166040 & 0.5547292\\\\\n",
       "\tsqrt.lasso.log2 & 0.9852655 & 0.5151536 & 0.7166040 & 1.0000000 & 0.7057869\\\\\n",
       "\tgene.cor & 0.7078153 & 0.4787279 & 0.5547292 & 0.7057869 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | lasso.log2 | bs.log2 | rf.log2 | sqrt.lasso.log2 | gene.cor | \n",
       "|---|---|---|---|---|\n",
       "| lasso.log2 | 1.0000000 | 0.4912528 | 0.6601102 | 0.9852655 | 0.7078153 | \n",
       "| bs.log2 | 0.4912528 | 1.0000000 | 0.5400662 | 0.5151536 | 0.4787279 | \n",
       "| rf.log2 | 0.6601102 | 0.5400662 | 1.0000000 | 0.7166040 | 0.5547292 | \n",
       "| sqrt.lasso.log2 | 0.9852655 | 0.5151536 | 0.7166040 | 1.0000000 | 0.7057869 | \n",
       "| gene.cor | 0.7078153 | 0.4787279 | 0.5547292 | 0.7057869 | 1.0000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                lasso.log2 bs.log2   rf.log2   sqrt.lasso.log2 gene.cor \n",
       "lasso.log2      1.0000000  0.4912528 0.6601102 0.9852655       0.7078153\n",
       "bs.log2         0.4912528  1.0000000 0.5400662 0.5151536       0.4787279\n",
       "rf.log2         0.6601102  0.5400662 1.0000000 0.7166040       0.5547292\n",
       "sqrt.lasso.log2 0.9852655  0.5151536 0.7166040 1.0000000       0.7057869\n",
       "gene.cor        0.7078153  0.4787279 0.5547292 0.7057869       1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,3,7,11,15,18)],10)\n",
    "cor(tbl.all[c(3,7,11,15,18)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, the 2 LASSO methods are in close agreement, this time agreeing nearly identically. We now also see much stronger agreement between all methods, with correlation coefficients in the 0.5-0.66 range. In contrast to the \"as-is\", the log2-transformed data demonstrate at least some correlation between each solver and the gene correlation coefficients, though the 2 LASSO methods have the highest coefficients. Now we'll look at the asinh-transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>lasso.asinh</th><th scope=col>bs.asinh</th><th scope=col>rf.asinh</th><th scope=col>sqrt.lasso.asinh</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF         </td><td>0.209422903 </td><td>8.583764e-05</td><td>25.9160383  </td><td>0.14289609  </td><td>0.9232394   </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4       </td><td>0.076155038 </td><td>8.946250e-05</td><td>22.4786757  </td><td>0.15896033  </td><td>0.9047599   </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1       </td><td>0.000000000 </td><td>1.154825e-05</td><td>11.9869878  </td><td>0.00000000  </td><td>0.8628270   </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2       </td><td>0.228754703 </td><td>3.566778e-01</td><td>14.2251862  </td><td>0.22652672  </td><td>0.8423299   </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2        </td><td>0.009811994 </td><td>7.409788e-05</td><td> 2.3401488  </td><td>0.00000000  </td><td>0.8278686   </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2       </td><td>0.120449138 </td><td>2.309917e-01</td><td> 2.9705266  </td><td>0.10635813  </td><td>0.8196172   </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2       </td><td>0.115311122 </td><td>1.056974e-01</td><td> 2.2511790  </td><td>0.09486455  </td><td>0.7704362   </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX        </td><td>0.000000000 </td><td>7.377633e-06</td><td> 3.2287507  </td><td>0.00000000  </td><td>0.7309450   </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX         </td><td>0.000000000 </td><td>6.947728e-05</td><td> 0.7157802  </td><td>0.00000000  </td><td>0.7241906   </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6        </td><td>0.090529696 </td><td>1.139972e-01</td><td> 1.7027348  </td><td>0.08655747  </td><td>0.7146115   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & lasso.asinh & bs.asinh & rf.asinh & sqrt.lasso.asinh & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF          & 0.209422903  & 8.583764e-05 & 25.9160383   & 0.14289609   & 0.9232394   \\\\\n",
       "\t5 & STAT4        & 0.076155038  & 8.946250e-05 & 22.4786757   & 0.15896033   & 0.9047599   \\\\\n",
       "\t35 & SATB1        & 0.000000000  & 1.154825e-05 & 11.9869878   & 0.00000000   & 0.8628270   \\\\\n",
       "\t15 & SATB2        & 0.228754703  & 3.566778e-01 & 14.2251862   & 0.22652672   & 0.8423299   \\\\\n",
       "\t33 & ATF2         & 0.009811994  & 7.409788e-05 &  2.3401488   & 0.00000000   & 0.8278686   \\\\\n",
       "\t4 & FOXP2        & 0.120449138  & 2.309917e-01 &  2.9705266   & 0.10635813   & 0.8196172   \\\\\n",
       "\t11 & TSHZ2        & 0.115311122  & 1.056974e-01 &  2.2511790   & 0.09486455   & 0.7704362   \\\\\n",
       "\t40 & DRGX         & 0.000000000  & 7.377633e-06 &  3.2287507   & 0.00000000   & 0.7309450   \\\\\n",
       "\t1 & HDX          & 0.000000000  & 6.947728e-05 &  0.7157802   & 0.00000000   & 0.7241906   \\\\\n",
       "\t8 & LHX6         & 0.090529696  & 1.139972e-01 &  1.7027348   & 0.08655747   & 0.7146115   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | lasso.asinh | bs.asinh | rf.asinh | sqrt.lasso.asinh | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF          | 0.209422903  | 8.583764e-05 | 25.9160383   | 0.14289609   | 0.9232394    | \n",
       "| 5 | STAT4        | 0.076155038  | 8.946250e-05 | 22.4786757   | 0.15896033   | 0.9047599    | \n",
       "| 35 | SATB1        | 0.000000000  | 1.154825e-05 | 11.9869878   | 0.00000000   | 0.8628270    | \n",
       "| 15 | SATB2        | 0.228754703  | 3.566778e-01 | 14.2251862   | 0.22652672   | 0.8423299    | \n",
       "| 33 | ATF2         | 0.009811994  | 7.409788e-05 |  2.3401488   | 0.00000000   | 0.8278686    | \n",
       "| 4 | FOXP2        | 0.120449138  | 2.309917e-01 |  2.9705266   | 0.10635813   | 0.8196172    | \n",
       "| 11 | TSHZ2        | 0.115311122  | 1.056974e-01 |  2.2511790   | 0.09486455   | 0.7704362    | \n",
       "| 40 | DRGX         | 0.000000000  | 7.377633e-06 |  3.2287507   | 0.00000000   | 0.7309450    | \n",
       "| 1 | HDX          | 0.000000000  | 6.947728e-05 |  0.7157802   | 0.00000000   | 0.7241906    | \n",
       "| 8 | LHX6         | 0.090529696  | 1.139972e-01 |  1.7027348   | 0.08655747   | 0.7146115    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  lasso.asinh bs.asinh     rf.asinh   sqrt.lasso.asinh gene.cor \n",
       "9  HLF   0.209422903 8.583764e-05 25.9160383 0.14289609       0.9232394\n",
       "5  STAT4 0.076155038 8.946250e-05 22.4786757 0.15896033       0.9047599\n",
       "35 SATB1 0.000000000 1.154825e-05 11.9869878 0.00000000       0.8628270\n",
       "15 SATB2 0.228754703 3.566778e-01 14.2251862 0.22652672       0.8423299\n",
       "33 ATF2  0.009811994 7.409788e-05  2.3401488 0.00000000       0.8278686\n",
       "4  FOXP2 0.120449138 2.309917e-01  2.9705266 0.10635813       0.8196172\n",
       "11 TSHZ2 0.115311122 1.056974e-01  2.2511790 0.09486455       0.7704362\n",
       "40 DRGX  0.000000000 7.377633e-06  3.2287507 0.00000000       0.7309450\n",
       "1  HDX   0.000000000 6.947728e-05  0.7157802 0.00000000       0.7241906\n",
       "8  LHX6  0.090529696 1.139972e-01  1.7027348 0.08655747       0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>lasso.asinh</th><th scope=col>bs.asinh</th><th scope=col>rf.asinh</th><th scope=col>sqrt.lasso.asinh</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>lasso.asinh</th><td>1.0000000</td><td>0.6089727</td><td>0.6190488</td><td>0.8586945</td><td>0.6848150</td></tr>\n",
       "\t<tr><th scope=row>bs.asinh</th><td>0.6089727</td><td>1.0000000</td><td>0.2264054</td><td>0.7004467</td><td>0.5063666</td></tr>\n",
       "\t<tr><th scope=row>rf.asinh</th><td>0.6190488</td><td>0.2264054</td><td>1.0000000</td><td>0.6842537</td><td>0.5657811</td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.asinh</th><td>0.8586945</td><td>0.7004467</td><td>0.6842537</td><td>1.0000000</td><td>0.7040126</td></tr>\n",
       "\t<tr><th scope=row>gene.cor</th><td>0.6848150</td><td>0.5063666</td><td>0.5657811</td><td>0.7040126</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & lasso.asinh & bs.asinh & rf.asinh & sqrt.lasso.asinh & gene.cor\\\\\n",
       "\\hline\n",
       "\tlasso.asinh & 1.0000000 & 0.6089727 & 0.6190488 & 0.8586945 & 0.6848150\\\\\n",
       "\tbs.asinh & 0.6089727 & 1.0000000 & 0.2264054 & 0.7004467 & 0.5063666\\\\\n",
       "\trf.asinh & 0.6190488 & 0.2264054 & 1.0000000 & 0.6842537 & 0.5657811\\\\\n",
       "\tsqrt.lasso.asinh & 0.8586945 & 0.7004467 & 0.6842537 & 1.0000000 & 0.7040126\\\\\n",
       "\tgene.cor & 0.6848150 & 0.5063666 & 0.5657811 & 0.7040126 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | lasso.asinh | bs.asinh | rf.asinh | sqrt.lasso.asinh | gene.cor | \n",
       "|---|---|---|---|---|\n",
       "| lasso.asinh | 1.0000000 | 0.6089727 | 0.6190488 | 0.8586945 | 0.6848150 | \n",
       "| bs.asinh | 0.6089727 | 1.0000000 | 0.2264054 | 0.7004467 | 0.5063666 | \n",
       "| rf.asinh | 0.6190488 | 0.2264054 | 1.0000000 | 0.6842537 | 0.5657811 | \n",
       "| sqrt.lasso.asinh | 0.8586945 | 0.7004467 | 0.6842537 | 1.0000000 | 0.7040126 | \n",
       "| gene.cor | 0.6848150 | 0.5063666 | 0.5657811 | 0.7040126 | 1.0000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                 lasso.asinh bs.asinh  rf.asinh  sqrt.lasso.asinh gene.cor \n",
       "lasso.asinh      1.0000000   0.6089727 0.6190488 0.8586945        0.6848150\n",
       "bs.asinh         0.6089727   1.0000000 0.2264054 0.7004467        0.5063666\n",
       "rf.asinh         0.6190488   0.2264054 1.0000000 0.6842537        0.5657811\n",
       "sqrt.lasso.asinh 0.8586945   0.7004467 0.6842537 1.0000000        0.7040126\n",
       "gene.cor         0.6848150   0.5063666 0.5657811 0.7040126        1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,4,8,12,16,18)],10)\n",
    "cor(tbl.all[c(4,8,12,16,18)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outside of the expected high agreement between LASSO methods, this transformation also shows fairly strong agreement between Random Forest and the LASSO methods. Bayes Spike is less correlated with the others, but still much more so than in the as-is case. Compared to the gene correlation coefficients, the 2 LASSO methods still show the strongest agreement and Bayes Spike has fallen off a bit. Let's finish by looking at the VOOM-transformed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>gene</th><th scope=col>lasso.voom</th><th scope=col>bs.voom</th><th scope=col>rf.voom</th><th scope=col>sqrt.lasso.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>9</th><td>HLF          </td><td>0.24105628   </td><td> 3.468772e-01</td><td>53.0524708   </td><td>0.15897283   </td><td>0.9232394    </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>STAT4        </td><td>0.09551869   </td><td> 1.708533e-01</td><td>48.9494236   </td><td>0.17336532   </td><td>0.9047599    </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>SATB1        </td><td>0.00000000   </td><td>-3.633166e-05</td><td>27.9882393   </td><td>0.00000000   </td><td>0.8628270    </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>SATB2        </td><td>0.26140384   </td><td> 2.853092e-01</td><td>30.4646927   </td><td>0.23814460   </td><td>0.8423299    </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>ATF2         </td><td>0.00000000   </td><td>-5.996884e-06</td><td> 7.3519720   </td><td>0.00000000   </td><td>0.8278686    </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FOXP2        </td><td>0.13231032   </td><td> 4.508482e-05</td><td> 3.8044576   </td><td>0.10469561   </td><td>0.8196172    </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>TSHZ2        </td><td>0.09676352   </td><td> 1.235520e-01</td><td> 4.6044307   </td><td>0.08923052   </td><td>0.7704362    </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>DRGX         </td><td>0.00000000   </td><td> 1.398560e-05</td><td> 8.6275128   </td><td>0.00000000   </td><td>0.7309450    </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HDX          </td><td>0.00000000   </td><td> 5.735278e-05</td><td> 0.8661305   </td><td>0.00000000   </td><td>0.7241906    </td></tr>\n",
       "\t<tr><th scope=row>8</th><td>LHX6         </td><td>0.09315715   </td><td> 1.365092e-01</td><td> 2.2815335   </td><td>0.09288872   </td><td>0.7146115    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & gene & lasso.voom & bs.voom & rf.voom & sqrt.lasso.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\t9 & HLF           & 0.24105628    &  3.468772e-01 & 53.0524708    & 0.15897283    & 0.9232394    \\\\\n",
       "\t5 & STAT4         & 0.09551869    &  1.708533e-01 & 48.9494236    & 0.17336532    & 0.9047599    \\\\\n",
       "\t35 & SATB1         & 0.00000000    & -3.633166e-05 & 27.9882393    & 0.00000000    & 0.8628270    \\\\\n",
       "\t15 & SATB2         & 0.26140384    &  2.853092e-01 & 30.4646927    & 0.23814460    & 0.8423299    \\\\\n",
       "\t33 & ATF2          & 0.00000000    & -5.996884e-06 &  7.3519720    & 0.00000000    & 0.8278686    \\\\\n",
       "\t4 & FOXP2         & 0.13231032    &  4.508482e-05 &  3.8044576    & 0.10469561    & 0.8196172    \\\\\n",
       "\t11 & TSHZ2         & 0.09676352    &  1.235520e-01 &  4.6044307    & 0.08923052    & 0.7704362    \\\\\n",
       "\t40 & DRGX          & 0.00000000    &  1.398560e-05 &  8.6275128    & 0.00000000    & 0.7309450    \\\\\n",
       "\t1 & HDX           & 0.00000000    &  5.735278e-05 &  0.8661305    & 0.00000000    & 0.7241906    \\\\\n",
       "\t8 & LHX6          & 0.09315715    &  1.365092e-01 &  2.2815335    & 0.09288872    & 0.7146115    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | gene | lasso.voom | bs.voom | rf.voom | sqrt.lasso.voom | gene.cor | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 9 | HLF           | 0.24105628    |  3.468772e-01 | 53.0524708    | 0.15897283    | 0.9232394     | \n",
       "| 5 | STAT4         | 0.09551869    |  1.708533e-01 | 48.9494236    | 0.17336532    | 0.9047599     | \n",
       "| 35 | SATB1         | 0.00000000    | -3.633166e-05 | 27.9882393    | 0.00000000    | 0.8628270     | \n",
       "| 15 | SATB2         | 0.26140384    |  2.853092e-01 | 30.4646927    | 0.23814460    | 0.8423299     | \n",
       "| 33 | ATF2          | 0.00000000    | -5.996884e-06 |  7.3519720    | 0.00000000    | 0.8278686     | \n",
       "| 4 | FOXP2         | 0.13231032    |  4.508482e-05 |  3.8044576    | 0.10469561    | 0.8196172     | \n",
       "| 11 | TSHZ2         | 0.09676352    |  1.235520e-01 |  4.6044307    | 0.08923052    | 0.7704362     | \n",
       "| 40 | DRGX          | 0.00000000    |  1.398560e-05 |  8.6275128    | 0.00000000    | 0.7309450     | \n",
       "| 1 | HDX           | 0.00000000    |  5.735278e-05 |  0.8661305    | 0.00000000    | 0.7241906     | \n",
       "| 8 | LHX6          | 0.09315715    |  1.365092e-01 |  2.2815335    | 0.09288872    | 0.7146115     | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   gene  lasso.voom bs.voom       rf.voom    sqrt.lasso.voom gene.cor \n",
       "9  HLF   0.24105628  3.468772e-01 53.0524708 0.15897283      0.9232394\n",
       "5  STAT4 0.09551869  1.708533e-01 48.9494236 0.17336532      0.9047599\n",
       "35 SATB1 0.00000000 -3.633166e-05 27.9882393 0.00000000      0.8628270\n",
       "15 SATB2 0.26140384  2.853092e-01 30.4646927 0.23814460      0.8423299\n",
       "33 ATF2  0.00000000 -5.996884e-06  7.3519720 0.00000000      0.8278686\n",
       "4  FOXP2 0.13231032  4.508482e-05  3.8044576 0.10469561      0.8196172\n",
       "11 TSHZ2 0.09676352  1.235520e-01  4.6044307 0.08923052      0.7704362\n",
       "40 DRGX  0.00000000  1.398560e-05  8.6275128 0.00000000      0.7309450\n",
       "1  HDX   0.00000000  5.735278e-05  0.8661305 0.00000000      0.7241906\n",
       "8  LHX6  0.09315715  1.365092e-01  2.2815335 0.09288872      0.7146115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>lasso.voom</th><th scope=col>bs.voom</th><th scope=col>rf.voom</th><th scope=col>sqrt.lasso.voom</th><th scope=col>gene.cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>lasso.voom</th><td>1.0000000</td><td>0.7414561</td><td>0.6195594</td><td>0.9121495</td><td>0.6367442</td></tr>\n",
       "\t<tr><th scope=row>bs.voom</th><td>0.7414561</td><td>1.0000000</td><td>0.6838455</td><td>0.6833242</td><td>0.5055787</td></tr>\n",
       "\t<tr><th scope=row>rf.voom</th><td>0.6195594</td><td>0.6838455</td><td>1.0000000</td><td>0.6444418</td><td>0.5682585</td></tr>\n",
       "\t<tr><th scope=row>sqrt.lasso.voom</th><td>0.9121495</td><td>0.6833242</td><td>0.6444418</td><td>1.0000000</td><td>0.6647592</td></tr>\n",
       "\t<tr><th scope=row>gene.cor</th><td>0.6367442</td><td>0.5055787</td><td>0.5682585</td><td>0.6647592</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & lasso.voom & bs.voom & rf.voom & sqrt.lasso.voom & gene.cor\\\\\n",
       "\\hline\n",
       "\tlasso.voom & 1.0000000 & 0.7414561 & 0.6195594 & 0.9121495 & 0.6367442\\\\\n",
       "\tbs.voom & 0.7414561 & 1.0000000 & 0.6838455 & 0.6833242 & 0.5055787\\\\\n",
       "\trf.voom & 0.6195594 & 0.6838455 & 1.0000000 & 0.6444418 & 0.5682585\\\\\n",
       "\tsqrt.lasso.voom & 0.9121495 & 0.6833242 & 0.6444418 & 1.0000000 & 0.6647592\\\\\n",
       "\tgene.cor & 0.6367442 & 0.5055787 & 0.5682585 & 0.6647592 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | lasso.voom | bs.voom | rf.voom | sqrt.lasso.voom | gene.cor | \n",
       "|---|---|---|---|---|\n",
       "| lasso.voom | 1.0000000 | 0.7414561 | 0.6195594 | 0.9121495 | 0.6367442 | \n",
       "| bs.voom | 0.7414561 | 1.0000000 | 0.6838455 | 0.6833242 | 0.5055787 | \n",
       "| rf.voom | 0.6195594 | 0.6838455 | 1.0000000 | 0.6444418 | 0.5682585 | \n",
       "| sqrt.lasso.voom | 0.9121495 | 0.6833242 | 0.6444418 | 1.0000000 | 0.6647592 | \n",
       "| gene.cor | 0.6367442 | 0.5055787 | 0.5682585 | 0.6647592 | 1.0000000 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                lasso.voom bs.voom   rf.voom   sqrt.lasso.voom gene.cor \n",
       "lasso.voom      1.0000000  0.7414561 0.6195594 0.9121495       0.6367442\n",
       "bs.voom         0.7414561  1.0000000 0.6838455 0.6833242       0.5055787\n",
       "rf.voom         0.6195594  0.6838455 1.0000000 0.6444418       0.5682585\n",
       "sqrt.lasso.voom 0.9121495  0.6833242 0.6444418 1.0000000       0.6647592\n",
       "gene.cor        0.6367442  0.5055787 0.5682585 0.6647592       1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tbl.all[,c(1,5,9,13,17,18)],10)\n",
    "cor(tbl.all[c(5,9,13,17,18)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we see the strongest overall agreement between solvers. This is mainly driven by heightened correlation between Bayes Spike and the other methods. The two LASSO methods still have the highest agreement with the Pearson correlations between target gene and transcription factors, but they are not as strong as in the other transformations. \n",
    "\n",
    "At this point, we've delved into a whole bunch of information and pulled out lots of correlations between the different transformations, different solvers, and the gene correlations coefficients. So let's step back and sum up what we've seen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "\n",
    "Based upon this short data exploration, there are a few key points to take away:\n",
    "\n",
    "1. Data transformation can profoundly affect results. There is no guarantee of consistency across different transformations, even for the same solvers. Furthermore, different transformations can affect which solvers agree with one another. \n",
    "\n",
    "2. Random Forest scores are the most consistent across different transformations, so the distribution of the data may be less of a concern if using this solver. LASSO and Square Root LASSO also show strong agreement between the transformed cases, but not compared to the as-is data.\n",
    "\n",
    "3. Regardless of transformation, LASSO coefficients and Random Forest scores tend to have stronger correlation with gene correlations than do Bayes Spike coefficients.\n",
    "\n",
    "Though it is tempting to recommend a particular solver and/or transformation to use in every case, this is not a realistic outcome of the exploration done here. Rather, these data should serve as an example of the benefits and consequences involved with these choices, as we have demonstrated that choice of solver and transformation have a sizable impact on the final results.\n",
    "\n",
    "Perhaps the best summation of this foray into different transformations is found in the [VOOM paper itself](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29):\n",
    "\n",
    "\"...correct modeling of the mean-variance relationship inherent in a data generating process is the key to designing statistically powerful methods of analysis.\"\n",
    "\n",
    "Thus, transformation techniques such as VOOM that take into account the mean-variance relationship represent a standard way for preparing robust data for use in tools such as TReNA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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