Last updated: 2018-12-05
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Modified: data/DE_input.Rd
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Load data
source("code/summary_functions.R")
load("data/DE_input.Rd")
glocus <- "VPS45"
dim(dm)[1]
NULL
gcount <- dm[1:(dim(dm)[1]-76), colnames(dm1dfagg)[dm1dfagg[glocus,] >0 & nlocus==1]]
# negative control cells defined as neg gRNA targeted cells
ncount <- dm[1:(dim(dm)[1]-76), colnames(dm1dfagg)[dm1dfagg["neg",] >0 & nlocus==1]]
coldata <- data.frame(row.names = c(colnames(gcount),colnames(ncount)),
condition=c(rep('G',dim(gcount)[2]),rep('N',dim(ncount)[2])))
library(DESeq2)
dds = DESeqDataSetFromMatrix(countData = cbind(gcount,ncount),
colData = coldata,
design = ~condition)
dds = estimateSizeFactors(dds)
ddsWARD = DESeq(dds)
resWARD = results(ddsWARD)
summ_pvalues(resWARD$pvalue[!is.na(resWARD$pvalue)])
resSigWARD <- subset(resWARD, padj < 0.1)
There are 0 genes passed FDR <0.1 cutoff.
Following recommendation for single cell from here.
ddsLRT = DESeq(dds, test="LRT", reduced = ~1, sfType="poscounts", useT=TRUE, minmu=1e-6,minReplicatesForReplace=Inf)
resLRT = results(ddsLRT)
summ_pvalues(resLRT$pvalue[!is.na(resLRT$pvalue)])
resSigLRT <- subset(resLRT, padj < 0.1)
There are 0 genes passed FDR <0.1 cutoff.
library(edgeR)
y <- DGEList(counts= cbind(gcount,ncount),group=coldata$condition)
y <- calcNormFactors(y)
group=coldata$condition
design <- model.matrix(~group)
y <- estimateDisp(y,design)
fitqlf <- glmQLFit(y,design)
qlf <- glmQLFTest(fitqlf,coef=2)
summ_pvalues(qlf$table$PValue)
topTags(qlf)
Coefficient: groupN
logFC logCPM F PValue FDR
ENSG00000176956.12 -2.776350 6.601479 58.72481 3.918884e-13 1.169630e-08
ENSG00000100097.11 -2.299929 6.624158 45.44607 1.064435e-10 1.171374e-06
ENSG00000130203.9 -1.831334 6.391155 45.21290 1.177418e-10 1.171374e-06
ENSG00000100300.17 -1.607537 6.410639 40.80264 8.073546e-10 6.024077e-06
ENSG00000138136.6 -1.976572 6.423656 39.44806 1.468212e-09 8.764051e-06
ENSG00000089116.3 -1.542926 6.320288 36.99506 1.605330e-08 7.578892e-05
ENSG00000175899.14 -1.584250 6.857262 33.87858 1.777533e-08 7.578892e-05
ENSG00000162992.3 -1.608906 6.373881 34.49478 2.385458e-08 8.899547e-05
ENSG00000198417.6 -1.600763 6.403261 32.30436 3.635396e-08 1.205578e-04
ENSG00000104327.7 -1.374926 6.326720 49.50835 7.206623e-08 2.150889e-04
fitlrt <- glmFit(y,design)
lrt <- glmLRT(fitlrt,coef=2)
topTags(lrt)
Coefficient: groupN
logFC logCPM LR PValue FDR
ENSG00000175899.14 -1.4640348 6.857262 19.83212 8.454987e-06 0.1170441
ENSG00000176956.12 -2.6064930 6.601479 19.25383 1.144407e-05 0.1170441
ENSG00000100097.11 -2.2150044 6.624158 19.20106 1.176480e-05 0.1170441
ENSG00000100300.17 -1.4959097 6.410639 14.35365 1.514859e-04 0.9651347
ENSG00000119906.11 1.0518706 6.444915 13.90103 1.926926e-04 0.9651347
ENSG00000185900.9 -0.7876253 6.269218 13.68055 2.166870e-04 0.9651347
ENSG00000219626.8 -0.9907215 6.458688 13.59855 2.263601e-04 0.9651347
ENSG00000078061.12 -0.8304381 6.581079 12.89330 3.297606e-04 1.0000000
ENSG00000234912.11 1.0422011 6.412729 12.49191 4.087177e-04 1.0000000
ENSG00000175806.14 -0.7904605 6.612756 12.19362 4.795325e-04 1.0000000
summ_pvalues(lrt$table$PValue)
Filter genes with 0 coverage in all cells.
mycount <- cbind(gcount,ncount)
totalcount <- apply(mycount,1,sum)
y <- DGEList(counts= mycount[totalcount>0,],group=coldata$condition)
y <- calcNormFactors(y)
group=coldata$condition
design <- model.matrix(~group)
y <- estimateDisp(y,design)
fitqlf <- glmQLFit(y,design)
qlf <- glmQLFTest(fitqlf,coef=2)
topTags(qlf)
Coefficient: groupN
logFC logCPM F PValue FDR
ENSG00000176956.12 -2.776350 6.601479 58.72481 3.918884e-13 6.170676e-09
ENSG00000100097.11 -2.299929 6.624158 45.44607 1.064435e-10 6.179874e-07
ENSG00000130203.9 -1.831334 6.391155 45.21290 1.177418e-10 6.179874e-07
ENSG00000100300.17 -1.607537 6.410639 40.80264 8.073546e-10 3.178152e-06
ENSG00000138136.6 -1.976572 6.423656 39.44806 1.468212e-09 4.623693e-06
ENSG00000089116.3 -1.542926 6.320288 36.99506 1.605330e-08 3.998433e-05
ENSG00000175899.14 -1.584250 6.857262 33.87858 1.777533e-08 3.998433e-05
ENSG00000162992.3 -1.608906 6.373881 34.49478 2.385458e-08 4.695177e-05
ENSG00000198417.6 -1.600763 6.403261 32.30436 3.635396e-08 6.360327e-05
ENSG00000104327.7 -1.374926 6.326720 49.50835 7.206623e-08 1.134755e-04
summ_pvalues(qlf$table$PValue)
Filter genes with 0 coverage in all cells.
fitlrt <- glmFit(y,design)
lrt <- glmLRT(fitlrt,coef=2)
topTags(lrt)
Coefficient: groupN
logFC logCPM LR PValue FDR
ENSG00000175899.14 -1.4640348 6.857262 19.83212 8.454987e-06 0.06174953
ENSG00000176956.12 -2.6064930 6.601479 19.25383 1.144407e-05 0.06174953
ENSG00000100097.11 -2.2150044 6.624158 19.20106 1.176480e-05 0.06174953
ENSG00000100300.17 -1.4959097 6.410639 14.35365 1.514859e-04 0.50918085
ENSG00000119906.11 1.0518706 6.444915 13.90103 1.926926e-04 0.50918085
ENSG00000185900.9 -0.7876253 6.269218 13.68055 2.166870e-04 0.50918085
ENSG00000219626.8 -0.9907215 6.458688 13.59855 2.263601e-04 0.50918085
ENSG00000078061.12 -0.8304381 6.581079 12.89330 3.297606e-04 0.64905134
ENSG00000234912.11 1.0422011 6.412729 12.49191 4.087177e-04 0.66841632
ENSG00000175806.14 -0.7904605 6.612756 12.19362 4.795325e-04 0.66841632
summ_pvalues(lrt$table$PValue)
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin14.5.0 (64-bit)
Running under: OS X El Capitan 10.11.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] edgeR_3.22.5 limma_3.36.5
[3] gridExtra_2.3 lattice_0.20-35
[5] DESeq2_1.20.0 SummarizedExperiment_1.10.1
[7] DelayedArray_0.6.6 BiocParallel_1.14.2
[9] matrixStats_0.54.0 Biobase_2.40.0
[11] GenomicRanges_1.32.7 GenomeInfoDb_1.16.0
[13] IRanges_2.14.12 S4Vectors_0.18.3
[15] BiocGenerics_0.26.0 Matrix_1.2-14
loaded via a namespace (and not attached):
[1] bit64_0.9-7 splines_3.5.1 R.utils_2.7.0
[4] Formula_1.2-3 assertthat_0.2.0 latticeExtra_0.6-28
[7] blob_1.1.1 GenomeInfoDbData_1.1.0 yaml_2.2.0
[10] RSQLite_2.1.1 pillar_1.3.0 backports_1.1.2
[13] glue_1.3.0 digest_0.6.18 RColorBrewer_1.1-2
[16] XVector_0.20.0 checkmate_1.8.5 colorspace_1.3-2
[19] htmltools_0.3.6 R.oo_1.22.0 plyr_1.8.4
[22] XML_3.98-1.16 pkgconfig_2.0.2 genefilter_1.62.0
[25] zlibbioc_1.26.0 purrr_0.2.5 xtable_1.8-3
[28] scales_1.0.0 whisker_0.3-2 git2r_0.23.0
[31] tibble_1.4.2 htmlTable_1.12 annotate_1.58.0
[34] ggplot2_3.1.0 nnet_7.3-12 lazyeval_0.2.1
[37] survival_2.42-6 magrittr_1.5 crayon_1.3.4
[40] memoise_1.1.0 evaluate_0.12 R.methodsS3_1.7.1
[43] foreign_0.8-71 tools_3.5.1 data.table_1.11.6
[46] stringr_1.3.1 locfit_1.5-9.1 munsell_0.5.0
[49] cluster_2.0.7-1 AnnotationDbi_1.42.1 bindrcpp_0.2.2
[52] compiler_3.5.1 rlang_0.3.0.1 RCurl_1.95-4.11
[55] rstudioapi_0.8 htmlwidgets_1.2 bitops_1.0-6
[58] base64enc_0.1-3 rmarkdown_1.10 gtable_0.2.0
[61] DBI_1.0.0 R6_2.3.0 knitr_1.20
[64] dplyr_0.7.6 bit_1.1-12 bindr_0.1.1
[67] Hmisc_4.1-1 workflowr_1.1.1 rprojroot_1.3-2
[70] stringi_1.2.4 Rcpp_1.0.0 geneplotter_1.58.0
[73] rpart_4.1-13 acepack_1.4.1 tidyselect_0.2.4
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