Description Usage Arguments Details Value References See Also Examples
Given normalized sgRNA read counts under treatment and control, this function computes sgRNA and gene level statistics. P-values of the gene scores are calculated by a permutation method to identify genes where some or all of the sgRNA read counts in treatment are significantly higher/lower compared to control, that is, genes with positive/negative treatment effect.
1 |
dat |
A data.frame where rows represent sgRNAs and columns are the sgRNA name (1st), targeting gene name (2nd), and sgRNA read counts under treatment (3rd) and control (4th) condition. |
multiplier |
The number of permutations. The default value is R=50. A normalized null distribution for the gene score is constructed based on R \times(total number of genes) permutation gene scores. |
r.seed |
A random seed to control the randomness of the permutation. |
sgRNA level statistics within genes are summarized by using the maxmean statistic (Efron and Tibshirani, 2007).
Returns a list containing:
gene.pos |
Test results for the alternative hypothesis of positive treatment effect. Rows represent genes and columns are the numbers of sgRNAs within genes, normalized gene scores, P-values, adjusted P-values (FDRs) by the Bejamini-Hochberg method, and ranks of genes. |
gene.neg |
Test results for the alternative hypothesis of negative treatment effect. |
stdTmat |
Normalized observed gene scores. |
stdNullTmat |
Normalized permutation gene scores. |
Tmat |
Observed gene scores. |
NullTmat |
Permutation gene scores. |
sgRNA.stat |
The input data.frame added with the sgRNA/gene scores and the numbers of sgRNAs within genes. |
Efron, B. and Tibshirani, R. (2007). On testing the significance of sets of genes. The Annals of Applied Statistics, 1(1):107–129.
Noh, J., Chen, B., Xiao, G. and Xie, Y. (2015+). A robust test for identification of essential genes from CRISPR/Cas9 knockout screens.
UQnormalize
, melanoma808
, plotScores
, plotNScores
1 2 3 4 5 6 7 8 9 10 | data(melanoma808)
dat = UQnormalize(melanoma808, trt=c('PLX7_R1','PLX7_R2'), ctrl=c('D7_R1','D7_R2'))
results = sgRSEA(dat=dat, multiplier=30)
## To see the top 10 genes with positive/negative treatment effect
results$gene.pos[1:10,]
results$gene.neg[1:10,]
## histograms of permutation and observed gene scores
## plotNScores(results)
## plotScores(results, m=8)
|
m NScore p.value.pos FDR.pos rank.pos
NF2 8 29.166909 4.125242e-05 0.01666598 1
NF1 4 22.259037 4.125242e-05 0.01666598 2
CUL3 12 13.225455 1.237573e-04 0.03333196 3
MED12 8 8.506410 1.196320e-03 0.24165670 4
TADA1 8 7.929661 1.526340e-03 0.24665649 5
TADA3 4 5.674543 5.527825e-03 0.71425625 6
TADA2B 6 5.336954 6.187864e-03 0.71425625 7
CREBBP 8 3.242870 1.340704e-02 0.94382071 8
TSHZ1 4 3.037158 1.472712e-02 0.94382071 9
CLDN10 8 2.393022 2.087373e-02 0.94382071 10
m NScore p.value.neg FDR.neg rank.neg
EGFR 8 -2.653635 4.125242e-05 0.01111065 1
RREB1 8 -2.356478 4.125242e-05 0.01111065 2
RYR1 8 -2.107559 4.125242e-05 0.01111065 3
HM13 8 -1.540723 7.425436e-04 0.14999381 4
ARTN 12 -1.475205 1.361330e-03 0.16189808 5
FMN1 6 -1.465902 1.361330e-03 0.16189808 6
HPDL 4 -1.462877 1.402582e-03 0.16189808 7
ABLIM2 6 -1.424686 1.897611e-03 0.19165876 8
ZNF540 4 -1.401072 2.392641e-03 0.20999134 9
CLCN3 4 -1.392674 2.598903e-03 0.20999134 10
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