compact.tree {Pigengene} | R Documentation

## Reduces the number of genes in a decision tree

### Description

In a greedy way, this function removes the genes with smaller weight one-by-one, while assessing the accuracy of the predictions of the resulting trees.

### Usage

    compact.tree(c5Tree, pigengene, Data=pigengene$Data, Labels=pigengene$Labels,
      testD=NULL, testL=NULL, saveDir=".", verbose=0)

### Arguments

`c5Tree` | A decision tree of class `C50` that uses module eigengenes, or `NULL`. If `NULL`, If `NULL`, expression plots for all modules are created.
`pigengene` | A object of `pigengene-class`, output of `compute.pigengene`
`Data` | A matrix or data frame containing the expression data, with genes corresponding to columns and rows corresponding to samples. Rows and columns must be named.
`Labels` | Labels (condition types) for the (training) expression data. It is a named vector of characters. `Data` will be subset according to these names.
`testD` | The test expression data, for example, from an independent dataset. Optional.
`testL` | Labels (condition types) for the (test) expression data. Optional.
`saveDir` | Where to save the plots of the tree(s)
`verbose` | Integer level of verbosity. 0 means silent and higher values produce more details of computation.

### Value

A list with following elements is invisibly returned:

`call` | The call that created the results
`predTrain` | Prediction using projected data without compacting
`predTrainCompact` | Prediction after compacting
`genes` | A character vector of all genes in the full tree before compacting
`genesCompacted` | A character vector of all genes in the compacted tree
`trainErrors` | A matrix reporting errors on the train data. The rows are named according to the number of removed genes. Each column reports the number of misclassified samples in one condition (type) except the last column that reports the total.
`testErrors` | A matrix reporting errors on the test data similar to `trainErrors`
`queue` | A numeric vector named by all genes contributing to the full tree before compacting. The numeric values are weights increasingly ordered by absolute value.
`pos` | The number of removed genes
`txtFile` | Confusion matrices and other details on compacting are reported in this text file

### References

Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia, Foroushani A, Agrahari R, Docking R, Karsan A, and Zare H. In preparation.

Gene shaving as a method for identifying distinct sets of genes with similar expression patterns, Hastie, Trevor, et al. Genome Biol 1.2 (2000): 1-0003.

### See Also

`Pigengene-package`, `compute.pigengene`, `make.decision.tree`, `C5.0`, `Pigengene-package`

### Examples

         ## Data:
         data(aml)
         data(mds)
         data(pigengene)
         d1 <- rbind(aml,mds)

         ## Fiting the trees:
         trees <- make.decision.tree(pigengene=pigengene, Data=d1,
         saveDir="trees", minPerLeaf=14:15, doHeat=FALSE,verbose=3,
         toCompact=FALSE)
         c1 <- compact.tree(c5Tree=trees$c5Trees[["15"]], pigengene=pigengene,
             saveDir="compacted", verbose=1)

[Package Pigengene version 1.8.1 Index]