benchmark_shared_hits {scsR} | R Documentation

## benchmark_shared_hits

### Description

This method can be used to benchmark sorted gene vectors (A) that comes out from a siRNA screen. The benchmark is done against other sorted gene vectors (B) that we know to contain high density of real hits (e.g. the results of a second siRNA screen performed with a different library). The benchmark is performed simply comparing the top n hits of the two lists. If the two lists contain many shared best hits than we have a strong statistical signal. Then we display the number of shared best hits for different n, in a graph (if visualize_pval variable is set to true the pvalue of the t-test is plotted instead of the number of shared hits).

### Usage

    benchmark_shared_hits(glA, glB, col, avoidIntersectL=FALSE,
                                         output_file=NULL, npoints=400, title="", scaleAXPoint = 1,
                                         scaleBXPoint = NULL, fixedBXPoint=400, displayRandomMultipleLines=TRUE,
                                         nrandom=20, intersectGenes=TRUE, visualize_pval=FALSE, max_ylim=NULL, xlab=NULL, ylab="shared hits")

### Arguments

`glA` | sorted list containing one or more sorted vectors of genes (i.e. hits of a genome wide screen sorted by significance). Each element i of glA will be benchmarked against element i of glB. In case glB contains only one element, each glA vector will be benchmarked against glB[1].
`glB` | sorted list containing one or more sorted vectors of genes (i.e. hits of a genome wide screen sorted by significance).
`col` | sorted vector of booleans (a boolean i in the vector corresponds to the shared hits of glA[i] with glB[i] )
`avoidIntersectL` | sorted vector of colors (a color i in the vector corresponds to the shared hits line obtain intersecting glA[i] with glB[i] ) To perform the benchmark we construct a background to be used (this background is given by the intersection of all the glA and glB vectors) When an element i of the vector is set to TRUE, we don't use the elements of glA[i] to compute the vector. This allows to benchmark also methods that do output only few putative good genes (instead of a sorted list of all the genes tested).
`npoints` | number of points on the x-axis of the graph (integer)
`nrandom` | number of random lines to compute (in order to infer the variation of the noise) (integer)
`output_file` | path to the output file where to store the graph (character vector)
`title` | title of the graph (character vector)
`scaleAXPoint` | for position x in the graph we compare the best x * scaleAXPoint best hits of the genesA vector (integer)
`scaleBXPoint` | for position x in the graph we compare the best x * scaleBXPoint best hits of the genesB vector (integer)
`fixedBXPoint` | for position x in the graph we compare the best fixedBXPoint best hits of the genesB vector (integer)
`intersectGenes` | specify whether to intersect the genes from the various input vectors to form a suitable background to be used for the benchmark. (boolean)
`visualize_pval` | specify whether a p-value (derived by an hypergeometric test) should be visualized instead of the number of shared hits. (boolean)
`displayRandomMultipleLines` | specify whether to display several random lines in the graph (instead of only one line that is the average of all the random lines) (boolean)
`max_ylim` | y upper limit (integer)
`xlab` | xlab (character vector)
`ylab` | ylab (character vector)

### Author(s)

Andrea Franceschini

### Examples

    data(uuk_screen)
    data(uuk_screen_dh)

    benchmark_shared_hits(
      glA=list(
        uuk_screen[1:1000,]$GeneID,
        arrange(add_rank_col(uuk_screen[1:1000,]), log_pval_rsa)$GeneID
      ),
      glB=list(uuk_screen_dh$GeneID),
      col=c("black", "blue"),
      title="UUKUNIEMI Hela Cell Killers"
    )

[Package scsR version 1.18.0 Index]