Last updated: 2019-02-15

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    File Version Author Date Message
    Rmd 01a5914 simingz 2019-02-14 permutation
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    Rmd a78d83a simingz 2018-12-17 explore filtering
    html a78d83a simingz 2018-12-17 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 log likelihood ratio 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)

  fitlrt <- glmFit(y,design)
  lrt <- glmLRT(fitlrt,coef=2)
  out <- topTags(lrt, n=Inf, adjust.method = "BH")

  if (plotit==T) {
    outsig <- subset(out$table,FDR <0.1) 
    summ_pvalues(lrt$table$PValue)
    print(paste0("There are ",dim(outsig)[1], " genes passed FDR <0.1 cutoff"))
    print(knitr::kable(signif(as.matrix(head(out$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
a78d83a simingz 2018-12-17 

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

          logFC   logCPM   LR    PValue    FDR
-------  ------  -------  ---  --------  -----
A2M       -1.50      6.9   20   8.6e-06   0.12
LY6H      -2.60      6.6   19   1.2e-05   0.12
LGALS1    -2.20      6.6   19   1.2e-05   0.12
TSPO      -1.50      6.4   14   1.5e-04   0.97
SLF2       1.10      6.4   14   1.9e-04   0.97
POMK      -0.79      6.3   14   2.2e-04   0.97

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
}

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

          logFC   logCPM   LR    PValue       FDR
-------  ------  -------  ---  --------  --------
MYC       -3.20      7.4   49   0.0e+00   1.0e-07
LY6H       2.60      6.6   19   1.3e-05   1.9e-01
MED9      -0.97      6.5   15   1.1e-04   7.5e-01
FZD4       0.85      6.3   14   1.4e-04   7.5e-01
LGALS1     2.00      6.6   14   1.7e-04   7.5e-01
GALT      -1.00      6.5   14   1.8e-04   7.5e-01

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

             logFC   logCPM   LR    PValue     FDR
----------  ------  -------  ---  --------  ------
LY6H         -2.80      6.6   24   1.1e-06   0.033
LGALS1       -2.10      6.6   16   5.5e-05   0.710
CHD6          0.96      6.6   16   7.8e-05   0.710
HIST1H2BC     0.88      6.3   15   1.1e-04   0.710
CLCF1        -0.89      6.3   15   1.2e-04   0.710
PNMA6A       -1.10      6.4   14   1.7e-04   0.870

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

                 logFC   logCPM   LR    PValue    FDR
--------------  ------  -------  ---  --------  -----
LGALS1            2.30      6.6   21   4.5e-06   0.13
LY6H              2.50      6.6   17   3.5e-05   0.51
CTD-2368P22.1    -1.10      6.3   15   1.1e-04   0.51
TPP1             -1.00      6.5   15   1.1e-04   0.51
SCLT1             1.00      6.4   15   1.1e-04   0.51
ZDHHC4           -0.64      7.2   15   1.2e-04   0.51

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

           logFC   logCPM   LR    PValue    FDR
--------  ------  -------  ---  --------  -----
LBX1        2.70      6.4   21   4.7e-06   0.14
LY6H       -2.50      6.6   17   4.7e-05   0.70
PAK3        1.20      6.5   15   9.3e-05   0.93
LGALS1     -1.90      6.6   13   2.5e-04   0.99
GNB3        0.86      6.3   13   2.7e-04   0.99
ZNF385A    -0.87      6.6   13   3.0e-04   0.99

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

                logFC   logCPM   LR    PValue    FDR
-------------  ------  -------  ---  --------  -----
MYC              -2.0      7.4   18   2.0e-05   0.31
LY6H              2.6      6.6   18   2.2e-05   0.31
DYNLT3            1.2      6.3   17   3.3e-05   0.31
RP11-45B20.3      1.1      6.3   17   4.1e-05   0.31
NEUROD1          -2.0      6.4   13   2.5e-04   1.00
LRRC17            1.1      6.3   12   4.4e-04   1.00
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 —– — —- ——— ———– ——— ———– ——— ———– ——— ———– ——— ———–

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 2018-12-17 

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

          logFC   logCPM   LR    PValue     FDR
-------  ------  -------  ---  --------  ------
A2M       -1.50      6.9   20   8.6e-06   0.063
LY6H      -2.60      6.6   19   1.2e-05   0.063
LGALS1    -2.20      6.6   19   1.2e-05   0.063
TSPO      -1.50      6.4   14   1.5e-04   0.510
SLF2       1.10      6.4   14   1.9e-04   0.510
POMK      -0.79      6.3   14   2.2e-04   0.510

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
}

0-2.png" width=“672” style=“display: block; margin: auto;” />

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

          logFC   logCPM   LR    PValue       FDR
-------  ------  -------  ---  --------  --------
CLDN5      3.60      6.8   35   0.0e+00   4.9e-05
LY6H      -2.70      6.6   20   7.5e-06   5.9e-02
NMNAT1     1.20      6.4   16   5.1e-05   2.5e-01
WDR53     -1.20      6.3   16   6.3e-05   2.5e-01
RGL2       0.94      6.5   15   8.5e-05   2.7e-01
PIGU      -0.69      6.9   14   1.9e-04   4.9e-01

0-4.png" width=“672” style=“display: block; margin: auto;” />

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

          logFC   logCPM   LR    PValue    FDR
-------  ------  -------  ---  --------  -----
ATP8A1    -0.93      6.3   15   0.00011   0.57
LBR       -0.72      6.9   15   0.00013   0.57
LMO1      -1.00      6.3   14   0.00021   0.57
PARD6A    -0.96      6.5   14   0.00021   0.57
LY6H       2.30      6.6   13   0.00024   0.57
DARS2     -1.00      6.4   13   0.00026   0.57

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 0.06 0.44 0.86 0.52 0.91 0.64 0.95 0.63 0.95 0.06 0.74
LGALS1 0 0.06 0.00 0.36 0.00 0.74 0.00 0.02 0.00 0.57 0.00 0.74
LY6H 0 0.06 0.00 0.16 0.00 0.06 0.00 0.00 0.00 0.57 0.00 0.10

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
a78d83a simingz 2018-12-17 

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

           logFC   logCPM   LR    PValue     FDR
--------  ------  -------  ---  --------  ------
LY6H       -2.70      6.6   21   4.1e-06   0.023
LGALS1     -2.30      6.6   21   5.3e-06   0.023
A2M        -1.50      6.9   21   5.9e-06   0.023
TSPO       -1.50      6.4   15   1.1e-04   0.340
FAM228B    -1.00      6.5   14   2.0e-04   0.360
POMK       -0.79      6.3   14   2.1e-04   0.360

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
}

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

           logFC   logCPM   LR    PValue       FDR
--------  ------  -------  ---  --------  --------
MYC          3.1      7.4   43   0.0e+00   5.0e-07
LY6H        -2.8      6.6   25   6.0e-07   2.8e-03
DENND2A     -1.4      6.3   25   7.0e-07   2.8e-03
LGALS1      -2.3      6.6   22   2.6e-06   7.8e-03
APOE        -2.0      6.4   15   9.6e-05   2.3e-01
RASD1       -1.9      6.5   14   1.7e-04   3.0e-01

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

          logFC   logCPM   LR    PValue     FDR
-------  ------  -------  ---  --------  ------
LY6H       2.80      6.6   23   1.6e-06   0.018
NEFM       1.60      7.8   22   3.0e-06   0.018
TMEM59     0.61      7.6   18   2.5e-05   0.100
STMN4      2.20      6.5   16   5.3e-05   0.160
LOXL1     -1.10      6.7   15   1.2e-04   0.270
LRIG1      0.90      6.7   14   2.2e-04   0.380

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

          logFC   logCPM   LR    PValue       FDR
-------  ------  -------  ---  --------  --------
MYC        3.20      7.4   45   0.0e+00   2.0e-07
LY6H       2.70      6.6   21   5.1e-06   3.1e-02
LGALS1     2.20      6.6   18   2.6e-05   1.1e-01
FAXC      -0.98      6.5   15   1.1e-04   3.3e-01
COL6A1     0.88      6.7   14   1.7e-04   4.0e-01
MELTF      0.83      6.3   14   2.2e-04   4.3e-01

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

           logFC   logCPM   LR    PValue     FDR
--------  ------  -------  ---  --------  ------
LGALS1     -2.40      6.6   22   2.3e-06   0.028
LY6H       -2.70      6.6   21   5.5e-06   0.033
ICK         1.20      6.4   16   5.3e-05   0.210
LRRC58      0.85      6.7   16   7.7e-05   0.230
NRXN3       0.95      6.3   15   1.1e-04   0.250
CCDC169     1.10      6.3   14   1.8e-04   0.320

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

           logFC   logCPM   LR    PValue      FDR
--------  ------  -------  ---  --------  -------
HES6        1.20      9.1   26   3.0e-07   0.0040
LY6H       -2.80      6.6   24   1.0e-06   0.0052
LGALS1     -2.40      6.6   23   1.3e-06   0.0052
GPM6B      -0.54      8.4   16   5.4e-05   0.1600
RABL3       1.10      6.4   14   1.7e-04   0.3500
GADD45G     1.70      7.8   14   2.1e-04   0.3500
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 0.02 0.28 0.99 0.83 0.99 0.78 1.00 0.3 0.95 0.18 0.91
LGALS1 0 0.02 0.00 0.01 0.00 0.38 0.00 0.11 0.0 0.03 0.00 0.01
LY6H 0 0.02 0.00 0.00 0.00 0.02 0.00 0.03 0.0 0.03 0.00 0.01

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
a78d83a simingz 2018-12-17 

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

           logFC   logCPM   LR    PValue    FDR
--------  ------  -------  ---  --------  -----
A2M        -1.50      6.9   20   7.0e-06   0.04
LGALS1     -2.20      6.6   20   8.4e-06   0.04
TSPO       -1.50      6.4   15   1.3e-04   0.42
SLF2        1.10      6.5   14   1.8e-04   0.44
FAM228B    -0.98      6.5   14   2.3e-04   0.44
ARAF       -0.83      6.6   13   3.5e-04   0.45
save(resm, file="data/edgeR-lrt-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
}

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

           logFC   logCPM   LR    PValue     FDR
--------  ------  -------  ---  --------  ------
LGALS1     -2.30      6.6   21   4.5e-06   0.043
TMPO        0.68      7.2   14   2.4e-04   0.410
PHGDH       0.60      8.7   13   2.4e-04   0.410
HNRNPA1     0.31     12.0   13   3.7e-04   0.410
CIB2        0.74      6.8   13   4.0e-04   0.410
NPM1        0.29     11.0   12   4.6e-04   0.410

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

          logFC   logCPM   LR    PValue    FDR
-------  ------  -------  ---  --------  -----
VAV2      -1.10      6.5   19   1.5e-05   0.14
SORT1     -1.00      6.6   16   4.9e-05   0.24
LGALS1     2.00      6.6   15   1.1e-04   0.34
TOB1      -1.10      6.5   14   1.7e-04   0.36
FDXR      -0.85      7.0   14   1.9e-04   0.36
VWA1       1.10      6.5   13   2.9e-04   0.36

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

           logFC   logCPM   LR    PValue       FDR
--------  ------  -------  ---  --------  --------
MYC         3.20      7.4   46   0.0e+00   1.0e-07
CHKB       -1.20      6.4   18   2.1e-05   9.9e-02
CHMP3      -0.56      7.5   15   9.2e-05   1.9e-01
LGALS1     -2.00      6.6   15   9.5e-05   1.9e-01
SERTAD1    -0.96      6.7   15   1.0e-04   1.9e-01
PLA2G15     1.00      6.4   13   2.5e-04   3.8e-01

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

          logFC   logCPM   LR    PValue    FDR
-------  ------  -------  ---  --------  -----
LGALS1    -2.20      6.6   19   1.1e-05   0.10
EIF4E2    -0.53      7.6   16   8.0e-05   0.29
MYC       -1.90      7.4   15   9.0e-05   0.29
WDR12      0.74      6.7   13   3.1e-04   0.74
PTCD2     -0.95      6.4   12   5.5e-04   0.98
CCDC57    -0.97      6.3   12   6.5e-04   0.98

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

            logFC   logCPM   LR    PValue    FDR
---------  ------  -------  ---  --------  -----
MYC         -3.20      7.4   48   0.0e+00   0.00
PPP1R14C     1.30      6.9   15   9.9e-05   0.41
ZNF644      -0.84      6.7   15   1.3e-04   0.41
MT1X        -1.40      6.5   13   2.8e-04   0.67
DUSP14      -0.73      6.9   12   4.1e-04   0.72
NME1        -0.33      9.3   12   5.1e-04   0.72
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 0.04 0.03 0.59 0.29 0.94 0.08 0.99 0.04 0.98 0.67 0.99
LGALS1 0 0.04 0.00 0.04 0.00 0.34 0.00 0.19 0.00 0.10 0.00 0.72

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
a78d83a simingz 2018-12-17 

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

            logFC   logCPM   LR    PValue     FDR
---------  ------  -------  ---  --------  ------
A2M         -1.50      6.9   19   1.2e-05   0.093
SLF2         1.10      6.5   14   1.7e-04   0.490
FAM228B     -0.99      6.5   14   2.2e-04   0.490
ARAF        -0.82      6.6   13   3.4e-04   0.490
NINJ1        0.73      6.9   12   4.5e-04   0.490
C17orf80     0.95      6.4   12   4.6e-04   0.490

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
}

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

          logFC   logCPM   LR    PValue       FDR
-------  ------  -------  ---  --------  --------
MYC        -3.0      7.4   41   0.0e+00   1.4e-06
SPP1        2.1      7.0   20   8.0e-06   3.0e-02
CRABP1     -1.1     10.0   16   7.3e-05   1.9e-01
VGF         1.3      6.8   14   2.0e-04   3.7e-01
PLK3       -1.1      6.8   13   2.6e-04   3.7e-01
NKAIN4     -1.0      7.1   13   3.3e-04   3.7e-01

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

          logFC   logCPM   LR    PValue    FDR
-------  ------  -------  ---  --------  -----
SLC4A2     1.10      6.5   17   3.3e-05   0.25
ILVBL     -0.72      6.8   15   1.0e-04   0.27
UBQLN2    -0.84      6.6   15   1.4e-04   0.27
RIF1      -0.78      6.8   14   1.4e-04   0.27
CAV1       1.40      7.1   14   1.9e-04   0.29
AKT1S1     0.59      7.1   13   3.2e-04   0.32

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

          logFC   logCPM   LR    PValue   FDR
-------  ------  -------  ---  --------  ----
MALAT1    -0.46     14.0   15   9.7e-05   0.3
WSCD1      0.97      6.6   14   2.1e-04   0.3
HES6       0.90      9.1   14   2.3e-04   0.3
CCZ1B     -0.94      6.6   13   2.6e-04   0.3
CCDC59    -0.56      7.4   13   2.7e-04   0.3
SLC3A2    -0.69      8.6   13   3.4e-04   0.3

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

          logFC   logCPM   LR    PValue       FDR
-------  ------  -------  ---  --------  --------
MYC       -3.00      7.4   39   0.00000   3.4e-06
ATG14      1.20      6.4   18   0.00002   7.4e-02
SOCS3     -0.91      6.7   13   0.00037   6.4e-01
VPS26A     0.52      7.3   12   0.00048   6.4e-01
PIGU       0.64      6.9   12   0.00053   6.4e-01
UBR5       0.80      6.6   12   0.00056   6.4e-01

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

         logFC   logCPM   LR    PValue     FDR
------  ------  -------  ---  --------  ------
MYC      -2.20      7.4   20   8.6e-06   0.035
SPP1     -2.10      7.0   20   9.2e-06   0.035
GCLM     -1.10      6.8   13   2.4e-04   0.510
AP1S2    -0.52      7.5   13   2.7e-04   0.510
SPC25    -0.90      6.9   13   4.0e-04   0.600
NCK1      0.82      6.6   12   5.2e-04   0.660
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 0.09 0.31 0.98 0.02 0.91 0.27 0.87 0.21 0.96 0.96 1

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