Description Usage Arguments Author(s) Examples
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).
1 2 3 4 |
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) |
Andrea Franceschini
1 2 3 4 5 6 7 8 9 10 11 12 | 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"
)
|
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