Differential expression analysis–EdgeR quasi-likelihood F-tests

Last updated: 2019-02-15

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Load data

source("code/summary_functions.R")
library(dplyr)
library(gtools)
library(data.table)
load("data/DE_input.Rd")
glocus <- "VPS45"
Nperm <- 5
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])))
countall <- cbind(gcount,ncount)
totalcount <- apply(countall,1,sum)
cellpercent <-  apply(countall,1,function(x) length(x[x>0])/length(x))

edgeR quasi-likelihood F-tests function

library(edgeR)
run_edgeR <- function(y,plotit=T) {
  # y is DGElist object
  y <- calcNormFactors(y)
  group= y$samples[,"group"]
  design <- model.matrix(~group)
  y <- estimateDisp(y,design)

  fitqlf <- glmQLFit(y,design)
  qlf <- glmQLFTest(fitqlf,coef=2)
  out <- topTags(qlf, n=Inf, adjust.method = "BH")
  
  if (plotit==T) {
    summ_pvalues(qlf$table$PValue)
    outsig <- subset(out$table,FDR <0.1)
    print(paste0("There are ",dim(outsig)[1], " genes passed FDR <0.1 cutoff"))
    print(knitr::kable(signif(as.matrix(head(out$table[order(out$table$PValue),])),digit=2)))
  }
  return(out)
}

y <- DGEList(counts= countall,group=coldata$condition)
resm <- run_edgeR(y)

[1] "There are 18 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F   PValue       FDR
-------  ------  -------  ---  -------  --------
LY6H       -2.8      6.6   59        0   0.0e+00
LGALS1     -2.3      6.6   45        0   1.2e-06
APOE       -1.8      6.4   45        0   1.2e-06
TSPO       -1.6      6.4   41        0   6.2e-06
LBX1       -2.0      6.4   39        0   9.0e-06
LHX5       -1.5      6.3   37        0   7.8e-05

permreslist <- list()
permreslist[[1]] <- data.table(gene=rownames(resm$table), p=resm$table$PValue, fdr=resm$table$FDR, key="gene")
for (n in 2:(Nperm+1)){
  y <- DGEList(counts= countall,group=permute(coldata$condition))
  res <- run_edgeR(y,plotit = T)
  resp <- data.table(gene=rownames(res$table), p=res$table$PValue, fdr=res$table$FDR, key="gene")
  colnames(resp) <- c("gene", paste0("perm.p_",n-1), paste0("perm.fdr_",n-1))
  permreslist[[n]] <- resp
}

[1] "There are 9 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
MYC         -3.3      7.4   76        0   0.0e+00
LY6H         2.8      6.6   54        0   0.0e+00
APOE         1.8      6.4   41        0   6.0e-06
S100A11      1.8      6.4   39        0   1.6e-05
NEUROD1      1.7      6.4   39        0   2.4e-05
LGALS1       2.1      6.6   35        0   5.6e-05

[1] "There are 9 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F    PValue       FDR
--------  ------  -------  ---  --------  --------
LY6H        -2.9      6.6   65   0.0e+00   0.0e+00
NEUROD1     -1.8      6.4   47   0.0e+00   1.7e-06
APOE        -1.8      6.4   44   0.0e+00   1.7e-06
LGALS1      -2.1      6.6   39   0.0e+00   1.6e-05
GAL         -1.6      6.4   34   0.0e+00   9.9e-05
TFF3        -1.2      6.4   29   3.9e-06   1.9e-02

[1] "There are 18 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
LY6H         2.7      6.6   52        0   2.0e-07
LGALS1       2.4      6.6   47        0   7.0e-07
NEUROD1      1.9      6.4   49        0   8.0e-07
S100A11      1.9      6.4   45        0   8.0e-07
APOE         1.7      6.4   37        0   2.7e-05
KCNMA1      -1.5      6.4   32        0   1.7e-04

[1] "There are 13 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
LY6H        -2.6      6.6   50    0e+00   4.0e-07
S100A11     -1.9      6.4   44    0e+00   3.1e-06
APOE        -1.8      6.4   41    0e+00   8.0e-06
LBX1         2.3      6.4   37    0e+00   6.0e-05
LGALS1      -2.0      6.6   34    0e+00   8.6e-05
LHX5         1.5      6.3   34    1e-07   4.6e-04

[1] "There are 19 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
NEUROD1     -2.0      6.4   58    0e+00   1.0e-07
LY6H         2.7      6.6   50    0e+00   2.0e-07
APOE         1.9      6.4   45    0e+00   1.2e-06
MT1F        -1.6      6.4   33    0e+00   1.8e-04
CYP26A1      1.8      6.5   32    0e+00   2.2e-04
MYC         -2.0      7.4   31    1e-07   3.0e-04

mergedres <- Reduce(merge,permreslist)
knitr::kable(mergedres[fdr <0.1,],digits = 2)

y <- DGEList(counts= countall[totalcount>0,],group=coldata$condition)
resm <- run_edgeR(y)

[1] "There are 26 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F   PValue       FDR
-------  ------  -------  ---  -------  --------
LY6H       -2.8      6.6   59        0   0.0e+00
LGALS1     -2.3      6.6   45        0   6.0e-07
APOE       -1.8      6.4   45        0   6.0e-07
TSPO       -1.6      6.4   41        0   3.3e-06
LBX1       -2.0      6.4   39        0   4.8e-06
LHX5       -1.5      6.3   37        0   4.1e-05

permreslist <- list()
permreslist[[1]] <- data.table(gene=rownames(resm$table), p=resm$table$PValue, fdr=resm$table$FDR, key="gene")
for (n in 2:(Nperm+1)){
  y <- DGEList(counts= countall[totalcount>0,],group=permute(coldata$condition))
  res <- run_edgeR(y,plotit = T)
  resp <- data.table(gene=rownames(res$table), p=res$table$PValue, fdr=res$table$FDR, key="gene")
  colnames(resp) <- c("gene", paste0("perm.p_",n-1), paste0("perm.fdr_",n-1))
  permreslist[[n]] <- resp
}

[1] "There are 16 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F    PValue       FDR
--------  ------  -------  ---  --------  --------
CLDN5        3.4      6.8   69   0.0e+00   0.0e+00
LY6H        -2.8      6.6   58   0.0e+00   0.0e+00
APOE        -1.8      6.4   43   0.0e+00   1.9e-06
NEUROD1     -1.7      6.4   38   0.0e+00   2.4e-05
LGALS1      -1.7      6.6   26   7.0e-07   2.2e-03
SPARCL1     -1.0      6.3   40   6.5e-06   1.7e-02

[1] "There are 9 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
APOE         1.9      6.4   46    0e+00   8.0e-07
NEUROD1     -1.8      6.4   48    0e+00   8.0e-07
LY6H         2.5      6.6   42    0e+00   2.5e-06
MT1F        -1.6      6.4   33    0e+00   1.3e-04
LGALS1       1.9      6.6   30    1e-07   3.6e-04
GAL          1.5      6.4   27    4e-07   1.2e-03

y <- DGEList(counts= countall[cellpercent > 0.03,],group=coldata$condition)
resm <- run_edgeR(y)

[1] "There are 20 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F   PValue       FDR
-------  ------  -------  ---  -------  --------
LY6H       -2.8      6.6   61        0   0.0e+00
LGALS1     -2.3      6.6   47        0   2.0e-07
APOE       -1.9      6.4   47        0   2.0e-07
TSPO       -1.6      6.4   43        0   1.0e-06
A2M        -1.6      6.9   35        0   1.9e-05
MT1F       -1.6      6.4   34        0   3.5e-05

permreslist <- list()
permreslist[[1]] <- data.table(gene=rownames(resm$table), p=resm$table$PValue, fdr=resm$table$FDR, key="gene")
for (n in 2:(Nperm+1)){
  y <- DGEList(counts= countall[cellpercent > 0.03,],group=permute(coldata$condition))
  res <- run_edgeR(y,plotit = T)
  resp <- data.table(gene=rownames(res$table), p=res$table$PValue, fdr=res$table$FDR, key="gene")
  colnames(resp) <- c("gene", paste0("perm.p_",n-1), paste0("perm.fdr_",n-1))
  permreslist[[n]] <- resp
}

[1] "There are 12 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
MYC          3.2      7.4   70    0e+00   0.0e+00
LY6H        -2.9      6.6   66    0e+00   0.0e+00
APOE        -2.0      6.4   59    0e+00   0.0e+00
LGALS1      -2.5      6.6   53    0e+00   0.0e+00
SSTR2        1.7      6.4   32    0e+00   7.9e-05
DENND2A     -1.4      6.3   75    1e-07   2.2e-04

[1] "There are 21 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F   PValue       FDR
-------  ------  -------  ---  -------  --------
LY6H        2.9      6.6   59    0e+00   0.0e+00
APOE        1.8      6.4   43    0e+00   1.9e-06
ELAVL4      1.7      6.4   32    0e+00   1.3e-04
STMN4       1.9      6.5   32    0e+00   1.4e-04
LGALS1      1.9      6.6   31    1e-07   1.7e-04
KCNMA1      1.4      6.4   28    3e-07   5.2e-04

[1] "There are 12 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F    PValue       FDR
--------  ------  -------  ---  --------  --------
MYC          3.3      7.4   72   0.0e+00   0.0e+00
LY6H         2.8      6.6   54   0.0e+00   0.0e+00
LGALS1       2.3      6.6   43   0.0e+00   1.3e-06
APOE         1.7      6.4   36   0.0e+00   1.9e-05
CYP26A1      1.8      6.5   35   0.0e+00   2.7e-05
GAL          1.3      6.4   23   3.2e-06   6.3e-03

[1] "There are 19 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
LY6H       -2.8      6.6   60   0.0e+00   0.0e+00
APOE       -2.0      6.4   53   0.0e+00   0.0e+00
LGALS1     -2.4      6.6   49   0.0e+00   1.0e-07
KCNMA1      1.4      6.4   26   6.0e-07   1.7e-03
RASD1      -1.5      6.5   21   8.0e-06   1.9e-02
NPTX2      -1.3      6.4   20   1.2e-05   2.4e-02

[1] "There are 36 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
LY6H        -2.9      6.6   63    0e+00   0.0e+00
LGALS1      -2.4      6.6   50    0e+00   1.0e-07
APOE        -1.7      6.4   37    0e+00   1.4e-05
CALB1       -1.4      6.3   45    2e-07   6.9e-04
HES6         1.3      9.1   28    3e-07   6.9e-04
CYP26A1      1.6      6.5   27    4e-07   7.7e-04

mergedres <- Reduce(merge,permreslist)
knitr::kable(mergedres[fdr <0.1,],digits = 2)

y <- DGEList(counts= countall[cellpercent > 0.1,],group=coldata$condition)
resm <- run_edgeR(y)

[1] "There are 7 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
LGALS1     -2.3      6.6   47   0.0e+00   5.0e-07
TSPO       -1.6      6.4   42   0.0e+00   2.3e-06
A2M        -1.6      6.9   35   0.0e+00   3.4e-05
SLF2        1.1      6.5   20   5.0e-05   8.6e-02
KRT18       1.3      7.1   17   5.3e-05   8.6e-02
CMTM8       1.1      6.6   17   5.7e-05   8.6e-02

save(resm, file="data/edgeR-qlf-10%filter_res.Rd")

permreslist <- list()
permreslist[[1]] <- data.table(gene=rownames(resm$table), p=resm$table$PValue, fdr=resm$table$FDR, key="gene")
for (n in 2:(Nperm+1)){
  y <- DGEList(counts= countall[cellpercent > 0.1,],group=permute(coldata$condition))
  res <- run_edgeR(y,plotit = T)
  resp <- data.table(gene=rownames(res$table), p=res$table$PValue, fdr=res$table$FDR, key="gene")
  colnames(resp) <- c("gene", paste0("perm.p_",n-1), paste0("perm.fdr_",n-1))
  permreslist[[n]] <- resp
}

[1] "There are 4 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
LGALS1    -2.40      6.6   50   0.0e+00   1.0e-07
GDF15     -1.60      6.6   27   4.0e-07   1.9e-03
KRT18     -1.40      7.1   20   1.1e-05   3.4e-02
AHNAK     -1.10      6.4   21   4.2e-05   1.0e-01
FAM83D     1.00      6.5   16   6.8e-05   1.3e-01
ZNF91     -0.93      6.5   15   1.2e-04   1.5e-01

[1] "There are 9 genes passed FDR <0.1 cutoff"

            logFC   logCPM    F    PValue       FDR
---------  ------  -------  ---  --------  --------
LGALS1        2.1      6.6   37   0.0e+00   3.9e-05
LGALS3BP      1.3      6.4   25   3.0e-06   1.5e-02
TOB1         -1.1      6.5   21   8.5e-06   2.3e-02
SORT1        -1.1      6.6   20   9.7e-06   2.3e-02
VAV2         -1.1      6.5   22   1.4e-05   2.7e-02
VWA1          1.1      6.5   19   2.5e-05   3.6e-02

[1] "There are 7 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
MYC         3.3      7.4   75   0.0e+00   0.0e+00
LGALS1     -2.0      6.6   34   0.0e+00   6.6e-05
ACTC1      -1.4      6.4   28   2.0e-07   7.1e-04
GDF15      -1.4      6.6   21   6.6e-06   1.4e-02
RASD1       1.5      6.5   21   8.1e-06   1.4e-02
CHKB       -1.2      6.4   36   8.7e-06   1.4e-02

[1] "There are 3 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
LGALS1    -2.20      6.6   43   0.0e+00   2.5e-06
MYC       -1.80      7.4   26   7.0e-07   3.2e-03
PDP1      -1.10      6.5   19   2.1e-05   6.7e-02
PALMD     -0.99      6.5   15   1.1e-04   2.7e-01
EIF4E2    -0.53      7.6   14   2.2e-04   4.3e-01
JOSD2     -0.87      6.5   13   3.4e-04   4.6e-01

[1] "There are 5 genes passed FDR <0.1 cutoff"

            logFC   logCPM    F    PValue       FDR
---------  ------  -------  ---  --------  --------
MYC         -3.30      7.4   79   0.0e+00   0.00000
LGALS1       1.90      6.6   29   1.0e-07   0.00065
MT1X        -1.30      6.5   21   5.8e-06   0.01900
PPP1R14C     1.20      6.9   18   2.4e-05   0.05800
RGS16        1.50      6.8   17   5.1e-05   0.09800
CNN1         0.93      6.5   15   1.7e-04   0.22000

mergedres <- Reduce(merge,permreslist)
knitr::kable(mergedres[fdr <0.1,],digits = 2)

y <- DGEList(counts= countall[cellpercent > 0.2,],group=coldata$condition)
resm <- run_edgeR(y)

[1] "There are 1 genes passed FDR <0.1 cutoff"

           logFC   logCPM    F    PValue       FDR
--------  ------  -------  ---  --------  --------
A2M        -1.60      6.9   34   0.0e+00   0.00013
SLF2        1.10      6.5   20   4.8e-05   0.12000
CMTM8       1.10      6.6   17   5.9e-05   0.12000
KRT18       1.30      7.1   16   7.3e-05   0.12000
GAP43      -0.91      8.2   16   9.0e-05   0.12000
FAM228B    -0.99      6.5   18   9.2e-05   0.12000

permreslist <- list()
permreslist[[1]] <- data.table(gene=rownames(resm$table), p=resm$table$PValue, fdr=resm$table$FDR, key="gene")
for (n in 2:(Nperm+1)){
  y <- DGEList(counts= countall[cellpercent > 0.2,],group=permute(coldata$condition))
  res <- run_edgeR(y,plotit = T)
  resp <- data.table(gene=rownames(res$table), p=res$table$PValue, fdr=res$table$FDR, key="gene")
  colnames(resp) <- c("gene", paste0("perm.p_",n-1), paste0("perm.fdr_",n-1))
  permreslist[[n]] <- resp
}

[1] "There are 6 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
MYC        -3.1      7.4   73   0.0e+00   0.0e+00
SPP1        2.2      7.0   39   0.0e+00   5.7e-06
DLL3       -1.7      7.5   25   9.0e-07   2.4e-03
VGF         1.3      6.8   21   8.3e-06   1.6e-02
CRABP1     -1.1     10.0   16   7.0e-05   9.9e-02
PLK3       -1.1      6.8   16   7.8e-05   9.9e-02

[1] "There are 2 genes passed FDR <0.1 cutoff"

            logFC   logCPM    F    PValue     FDR
---------  ------  -------  ---  --------  ------
CAV1         1.40      7.1   24   1.5e-06   0.011
SLC4A2       1.10      6.5   21   7.4e-06   0.028
MIS18BP1    -0.92      6.6   15   1.4e-04   0.270
UBQLN2      -0.85      6.6   14   2.0e-04   0.270
RIF1        -0.78      6.8   14   2.1e-04   0.270
TMEM43      -0.85      6.6   14   2.3e-04   0.270

[1] "There are 0 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue    FDR
-------  ------  -------  ---  --------  -----
MYC       -1.50      7.4   18   2.8e-05   0.21
CCZ1B     -0.96      6.6   17   5.7e-05   0.22
MALAT1    -0.46     14.0   15   1.1e-04   0.26
WSCD1      0.94      6.6   15   1.4e-04   0.26
PEX14     -0.79      6.7   13   4.6e-04   0.42
SLC3A2    -0.67      8.6   12   4.9e-04   0.42

[1] "There are 3 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue    FDR
-------  ------  -------  ---  --------  -----
MYC       -3.10      7.4   70   0.0e+00   0.00
ATG14      1.20      6.4   29   7.1e-06   0.02
IGFBP5    -1.40      6.7   21   7.7e-06   0.02
SOCS3     -0.93      6.7   16   1.0e-04   0.19
BRAP       0.97      6.4   17   3.3e-04   0.47
UBR5       0.79      6.6   12   5.1e-04   0.47

[1] "There are 2 genes passed FDR <0.1 cutoff"

          logFC   logCPM    F    PValue       FDR
-------  ------  -------  ---  --------  --------
SPP1      -2.00      7.0   38   0.0e+00   1.9e-05
MYC       -2.00      7.4   33   0.0e+00   1.1e-04
GCLM      -1.00      6.8   17   5.7e-05   1.4e-01
OSGIN1    -1.20      6.7   16   9.9e-05   1.9e-01
SPC25     -0.88      6.9   14   2.1e-04   3.2e-01
NTRK3     -0.94      6.5   15   2.7e-04   3.5e-01

mergedres <- Reduce(merge,permreslist)
knitr::kable(mergedres[fdr <0.1,],digits = 2)

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] gridExtra_2.3     edgeR_3.24.3      limma_3.38.2      Matrix_1.2-15    
[5] data.table_1.12.0 gtools_3.8.1      dplyr_0.7.8       lattice_0.20-38  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        highr_0.7         compiler_3.5.1   
 [4] pillar_1.3.1      git2r_0.23.0      workflowr_1.1.1  
 [7] bindr_0.1.1       R.methodsS3_1.7.1 R.utils_2.7.0    
[10] tools_3.5.1       digest_0.6.18     gtable_0.2.0     
[13] evaluate_0.12     tibble_2.0.1      pkgconfig_2.0.2  
[16] rlang_0.3.1       yaml_2.2.0        bindrcpp_0.2.2   
[19] stringr_1.4.0     knitr_1.20        locfit_1.5-9.1   
[22] rprojroot_1.3-2   tidyselect_0.2.5  glue_1.3.0       
[25] R6_2.3.0          rmarkdown_1.10    purrr_0.2.5      
[28] magrittr_1.5      whisker_0.3-2     backports_1.1.2  
[31] htmltools_0.3.6   splines_3.5.1     assertthat_0.2.0 
[34] stringi_1.3.1     crayon_1.3.4      R.oo_1.22.0      

This reproducible R Markdown analysis was created with workflowr 1.1.1