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).
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)
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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> library(scsR)
Error in library(scsR) : there is no package called 'scsR'
Execution halted