Differential expression analysis--EdgeR quasi-likelihood F-tests

Last updated: 2019-02-15

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Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	0d6ec80	simingz	2019-02-15	permutation-qqplot
Rmd	02a94e5	simingz	2019-02-15	permutation-qqplot
html	02a94e5	simingz	2019-02-15	permutation-qqplot
Rmd	01a5914	simingz	2019-02-14	permutation
html	01a5914	simingz	2019-02-14	permutation
html	a78d83a	simingz	2018-12-17	explore filtering
Rmd	49ecf6e	simingz	2018-12-16	explore filtering
html	49ecf6e	simingz	2018-12-16	explore filtering

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)
}

Run edgeR–No filtering

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

Expand here to see past versions of edgeRall-1.png:

Version	Author	Date
01a5914	simingz	2019-02-14
49ecf6e	simingz	2018-12-16

[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

Permutation

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
}

Expand here to see past versions of permall-1.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of permall-2.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of permall-3.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of permall-4.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of permall-5.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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)

gene	fdr	perm.p_1	perm.fdr_1	perm.p_2	perm.fdr_2	perm.p_3	perm.fdr_3	perm.p_4	perm.fdr_4	perm.p_5	perm.fdr_5
A2M	0.00	0.08	1.00	0.54	1.00	0.76	1.00	0.31	1.00	0.17	1.00
APOE	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
CALB1	0.00	0.13	1.00	0.16	1.00	0.95	1.00	0.11	1.00	0.34	1.00
EDN1	0.06	0.00	1.00	0.05	1.00	0.01	1.00	0.04	1.00	0.19	1.00
FIBIN	0.02	0.48	1.00	0.99	1.00	0.72	1.00	0.43	1.00	0.58	1.00
H1F0	0.04	0.00	0.25	0.00	0.33	0.03	1.00	0.00	0.37	0.00	0.62
LBX1	0.00	0.06	1.00	0.07	1.00	0.00	0.06	0.00	0.00	0.01	0.96
LGALS1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
LHX5	0.00	0.41	1.00	0.36	1.00	0.00	0.00	0.00	0.00	0.00	0.04
LY6H	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
MT1F	0.00	0.02	1.00	0.00	0.66	0.00	0.01	0.02	1.00	0.00	0.00
NEUROD1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
NEUROG1	0.00	0.33	1.00	0.01	1.00	0.00	0.02	0.47	1.00	0.06	1.00
PI15	0.01	0.00	0.31	0.00	0.40	0.00	0.03	0.00	0.17	0.00	0.00
RGS4	0.08	0.01	1.00	0.22	1.00	0.56	1.00	0.00	0.21	0.77	1.00
SLF2	0.08	0.96	1.00	0.30	1.00	0.24	1.00	0.06	1.00	0.89	1.00
SST	0.01	0.00	0.06	0.12	1.00	0.06	1.00	0.03	1.00	0.00	0.01
TSPO	0.00	0.16	1.00	0.02	1.00	0.16	1.00	0.21	1.00	0.00	0.53

Run edgeR–at least one cell UMI > 0

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

Version Author Date simingz 2019-02-14 simingz 2018-12-16

[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

Permutation

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
}

Version Author Date simingz 2019-02-15

[1] "There are 29 genes passed FDR <0.1 cutoff"

                 logFC   logCPM    F   PValue       FDR
--------------  ------  -------  ---  -------  --------
LY6H               2.8      6.6   55    0e+00   0.0e+00
APOE               1.7      6.4   38    0e+00   2.4e-05
G0S2               1.6      6.4   42    0e+00   2.4e-05
LGALS1             2.0      6.6   33    0e+00   1.2e-04
RP11-389K14.3     -1.3      6.3   40    2e-07   7.2e-04
NEUROD1            1.5      6.4   28    3e-07   7.9e-04

Version Author Date simingz 2019-02-15

[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

Version Author Date simingz 2019-02-15

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

           logFC   logCPM    F   PValue   FDR
--------  ------  -------  ---  -------  ----
LY6H        -3.2      6.6   77        0     0
S100A11     -2.2      6.4   69        0     0
CLDN5       -2.9      6.8   54        0     0
APOE        -1.9      6.4   50        0     0
LGALS1      -2.4      6.6   50        0     0
GAL         -1.9      6.4   50        0     0

Version Author Date simingz 2019-02-15

[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

Version Author Date simingz 2019-02-15

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

           logFC   logCPM    F   PValue       FDR
--------  ------  -------  ---  -------  --------
LY6H         2.9      6.6   56    0e+00   0.0e+00
S100A11      2.3      6.4   42    0e+00   9.1e-06
APOE         1.7      6.4   35    0e+00   4.6e-05
CALB1        1.4      6.3   51    1e-07   2.4e-04
LBX1         1.8      6.4   31    1e-07   2.4e-04
LGALS1       1.9      6.6   30    1e-07   2.4e-04

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

gene	fdr	perm.p_1	perm.fdr_1	perm.p_2	perm.fdr_2	perm.p_3	perm.fdr_3	perm.p_4	perm.fdr_4	perm.p_5	perm.fdr_5
A2M	0.00	0.24	0.68	0.47	0.87	0.77	0.98	0.58	0.93	0.01	0.66
APOE	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
CALB1	0.00	0.00	0.13	0.13	0.71	0.00	0.06	0.10	0.73	0.00	0.00
CMTM8	0.06	0.24	0.68	0.92	0.99	0.09	0.74	0.08	0.73	0.95	0.99
EDN1	0.03	0.00	0.02	0.00	0.02	0.03	0.74	0.00	0.24	0.00	0.47
FAM228B	0.07	0.04	0.67	0.22	0.71	0.84	0.99	0.26	0.73	0.75	0.96
FIBIN	0.01	0.82	0.98	0.87	0.99	0.92	0.99	0.61	0.93	0.01	0.66
GAL	0.10	0.00	0.00	0.03	0.71	0.00	0.00	0.00	0.00	0.01	0.57
GAP43	0.07	0.61	0.90	0.26	0.72	0.67	0.94	0.92	0.99	0.01	0.54
H1F0	0.02	0.00	0.29	0.00	0.50	0.60	0.93	0.00	0.36	0.00	0.29
KRT18	0.07	0.00	0.27	0.04	0.71	0.00	0.38	0.02	0.73	0.00	0.28
LBX1	0.00	0.22	0.68	0.11	0.71	0.04	0.74	0.04	0.73	0.00	0.00
LGALS1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
LHX5	0.00	0.56	0.88	0.16	0.71	0.00	0.00	0.07	0.73	0.00	0.00
LINC00338	0.08	0.89	1.00	0.33	0.78	0.03	0.74	0.47	0.89	0.31	0.76
LY6H	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
MT1F	0.00	0.00	0.03	0.00	0.48	0.00	0.06	0.00	0.00	0.01	0.55
NEUROD1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01
NEUROG1	0.00	0.99	1.00	0.02	0.65	0.05	0.74	0.06	0.73	0.33	0.78
OTP	0.07	0.00	0.09	0.00	0.17	0.00	0.08	0.00	0.20	0.00	0.17
PI15	0.00	0.00	0.09	0.00	0.26	0.00	0.05	0.00	0.20	0.00	0.02
RGS4	0.04	0.58	0.88	0.01	0.65	0.42	0.86	0.79	0.99	0.76	0.96
SLF2	0.04	0.57	0.88	0.33	0.78	0.56	0.93	0.77	0.98	0.53	0.89
SMOC2	0.10	0.14	0.68	0.02	0.65	0.46	0.89	0.03	0.73	0.03	0.71
SST	0.00	0.02	0.62	0.00	0.08	0.11	0.74	0.05	0.73	0.00	0.02
TSPO	0.00	0.45	0.84	0.99	1.00	0.02	0.74	0.13	0.73	0.14	0.71

Run edgeR–3% cells with UMI > 0

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

Expand here to see past versions of edgeR0.03-1.png:

Version	Author	Date
01a5914	simingz	2019-02-14
49ecf6e	simingz	2018-12-16

[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

Permutation

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
}

Expand here to see past versions of perm0.03-1.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.03-2.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.03-3.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.03-4.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.03-5.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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)

gene	fdr	perm.p_1	perm.fdr_1	perm.p_2	perm.fdr_2	perm.p_3	perm.fdr_3	perm.p_4	perm.fdr_4	perm.p_5	perm.fdr_5
A2M	0.00	0.23	0.96	0.80	1.00	0.71	1.00	0.18	0.86	0.03	0.67
APOE	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
CALB1	0.00	0.48	0.98	0.25	0.94	0.23	0.93	0.33	0.92	0.00	0.00
CMTM8	0.05	0.53	0.98	0.03	0.73	0.06	0.84	0.05	0.71	0.22	0.87
EDN1	0.04	0.27	0.97	0.32	0.95	0.02	0.75	0.39	0.94	0.00	0.12
FAM228B	0.06	0.39	0.97	0.96	1.00	0.80	1.00	0.83	0.99	0.55	0.95
GAL	0.08	0.00	0.53	0.00	0.22	0.00	0.01	0.00	0.26	0.00	0.31
GAP43	0.05	0.07	0.92	0.42	0.97	0.91	1.00	0.20	0.87	0.05	0.71
GLRX	0.08	0.12	0.92	0.21	0.93	0.25	0.94	0.00	0.03	0.01	0.53
H1F0	0.02	0.08	0.92	0.01	0.59	0.06	0.84	0.00	0.31	0.08	0.76
KRT18	0.05	0.09	0.92	0.24	0.94	0.41	0.96	0.77	0.99	0.00	0.03
LGALS1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
LINC00338	0.08	0.92	0.99	0.33	0.95	0.06	0.84	0.54	0.96	0.06	0.72
LY6H	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
MT1F	0.00	0.00	0.16	0.00	0.05	0.00	0.01	0.00	0.15	0.00	0.29
NEUROG1	0.00	0.13	0.92	0.01	0.53	0.18	0.93	0.00	0.03	0.06	0.73
RGS4	0.05	0.09	0.92	0.17	0.92	0.07	0.84	0.02	0.63	0.03	0.67
SLF2	0.05	0.06	0.90	0.93	1.00	0.46	0.97	0.14	0.85	0.01	0.49
SMOC2	0.09	0.04	0.83	0.55	0.98	0.24	0.94	0.01	0.53	0.01	0.56
TSPO	0.00	0.00	0.53	0.97	1.00	0.01	0.67	0.00	0.47	0.68	0.97

Run edgeR–10% cells with UMI > 0

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

Expand here to see past versions of edgeR0.1-1.png:

Version	Author	Date
01a5914	simingz	2019-02-14
49ecf6e	simingz	2018-12-16

[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")

Permutation

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
}

Expand here to see past versions of perm0.1-1.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.1-2.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.1-3.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.1-4.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.1-5.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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)

gene	fdr	perm.p_1	perm.fdr_1	perm.p_2	perm.fdr_2	perm.p_3	perm.fdr_3	perm.p_4	perm.fdr_4	perm.p_5	perm.fdr_5
A2M	0.00	0.01	0.43	0.13	0.88	0.01	0.72	0.01	0.77	0.65	0.98
CMTM8	0.09	0.00	0.35	0.00	0.45	0.12	0.94	0.04	0.86	0.00	0.62
GAP43	0.09	0.02	0.46	0.07	0.82	0.10	0.94	0.02	0.83	0.01	0.71
KRT18	0.09	0.00	0.03	0.44	0.94	0.09	0.94	0.00	0.71	0.00	0.62
LGALS1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
SLF2	0.09	0.02	0.49	0.49	0.95	0.84	1.00	0.87	1.00	0.49	0.97
TSPO	0.00	0.03	0.54	0.06	0.81	0.33	0.98	0.00	0.46	0.00	0.54

Run edgeR–20% cells with UMI > 0

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

Expand here to see past versions of edgeR0.2-1.png:

Version	Author	Date
01a5914	simingz	2019-02-14
49ecf6e	simingz	2018-12-16

[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

Permutation

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
}

Expand here to see past versions of perm0.2-1.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.2-2.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.2-3.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.2-4.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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

Expand here to see past versions of perm0.2-5.png:

Version	Author	Date
02a94e5	simingz	2019-02-15

[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)

gene	p	fdr	perm.p_1	perm.fdr_1	perm.p_2	perm.fdr_2	perm.p_3	perm.fdr_3	perm.p_4	perm.fdr_4	perm.p_5	perm.fdr_5
A2M	0	0	0.15	0.96	0	0.42	0.14	0.82	0.1	0.96	0.67	0.99

Parameters used

We used data processed after QC step here.
targeted locus, choose VPS45.

Session information

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

Differential expression analysis–EdgeR quasi-likelihood F-tests

Siming Zhao

December 2, 2018

Run edgeR–No filtering

Permutation

Run edgeR–at least one cell UMI > 0

Permutation

Run edgeR–3% cells with UMI > 0

Permutation

Run edgeR–10% cells with UMI > 0

Permutation

Run edgeR–20% cells with UMI > 0

Permutation

Parameters used

Session information