Last updated: 2018-12-17

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    Rmd 49ecf6e simingz 2018-12-16 explore filtering
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Load data

source("code/summary_functions.R")
library(dplyr)
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])))
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) {
  # y is DGElist object
  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$PValue)
  out <- topTags(qlf, n=Inf, adjust.method = "BH")
  outsig <- subset(out$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)
res <- run_edgeR(y)

Expand here to see past versions of edgeRall-1.png:
Version Author Date
49ecf6e simingz 2018-12-16 
[1] "There are 18 genes passed FDR <0.1 cutoff"

                      logFC   logCPM    F   PValue       FDR
-------------------  ------  -------  ---  -------  --------
ENSG00000176956.12     -2.8      6.6   59        0   0.0e+00
ENSG00000100097.11     -2.3      6.6   45        0   1.2e-06
ENSG00000130203.9      -1.8      6.4   45        0   1.2e-06
ENSG00000100300.17     -1.6      6.4   41        0   6.0e-06
ENSG00000138136.6      -2.0      6.4   39        0   8.8e-06
ENSG00000089116.3      -1.5      6.3   37        0   7.6e-05

Run edgeR–at least one cell UMI > 0

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

Expand here to see past versions of edgeR>0-1.png:
Version Author Date
49ecf6e simingz 2018-12-16 
[1] "There are 26 genes passed FDR <0.1 cutoff"

                      logFC   logCPM    F   PValue       FDR
-------------------  ------  -------  ---  -------  --------
ENSG00000176956.12     -2.8      6.6   59        0   0.0e+00
ENSG00000100097.11     -2.3      6.6   45        0   6.0e-07
ENSG00000130203.9      -1.8      6.4   45        0   6.0e-07
ENSG00000100300.17     -1.6      6.4   41        0   3.2e-06
ENSG00000138136.6      -2.0      6.4   39        0   4.6e-06
ENSG00000089116.3      -1.5      6.3   37        0   4.0e-05

Run edgeR–3% cells with UMI > 0

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

Expand here to see past versions of edgeR0.03-1.png:
Version Author Date
49ecf6e simingz 2018-12-16 
[1] "There are 20 genes passed FDR <0.1 cutoff"

                      logFC   logCPM    F   PValue       FDR
-------------------  ------  -------  ---  -------  --------
ENSG00000176956.12     -2.8      6.6   61        0   0.0e+00
ENSG00000100097.11     -2.3      6.6   47        0   2.0e-07
ENSG00000130203.9      -1.9      6.4   47        0   2.0e-07
ENSG00000100300.17     -1.6      6.4   43        0   1.0e-06
ENSG00000175899.14     -1.6      6.9   35        0   1.9e-05
ENSG00000198417.6      -1.6      6.4   34        0   3.5e-05

Run edgeR–10% cells with UMI > 0

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

Expand here to see past versions of edgeR0.1-1.png:
Version Author Date
49ecf6e simingz 2018-12-16 
[1] "There are 7 genes passed FDR <0.1 cutoff"

                      logFC   logCPM    F    PValue       FDR
-------------------  ------  -------  ---  --------  --------
ENSG00000100097.11     -2.3      6.6   47   0.0e+00   5.0e-07
ENSG00000100300.17     -1.6      6.4   42   0.0e+00   2.3e-06
ENSG00000175899.14     -1.6      6.9   35   0.0e+00   3.4e-05
ENSG00000119906.11      1.1      6.5   20   5.0e-05   8.4e-02
ENSG00000111057.10      1.3      7.1   17   5.3e-05   8.4e-02
ENSG00000170293.8       1.1      6.6   17   5.7e-05   8.4e-02

Run edgeR–20% cells with UMI > 0

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

Expand here to see past versions of edgeR0.2-1.png:
Version Author Date
49ecf6e simingz 2018-12-16 
[1] "There are 1 genes passed FDR <0.1 cutoff"

                      logFC   logCPM    F    PValue       FDR
-------------------  ------  -------  ---  --------  --------
ENSG00000175899.14    -1.60      6.9   34   0.0e+00   0.00013
ENSG00000119906.11     1.10      6.5   20   4.8e-05   0.12000
ENSG00000170293.8      1.10      6.6   17   5.9e-05   0.12000
ENSG00000111057.10     1.30      7.1   16   7.3e-05   0.12000
ENSG00000172020.12    -0.91      8.2   16   9.0e-05   0.12000
ENSG00000219626.8     -0.99      6.5   18   9.2e-05   0.12000

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-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      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] gridExtra_2.3   lattice_0.20-35 edgeR_3.22.5    limma_3.36.5   
[5] Matrix_1.2-14   dplyr_0.7.6    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        compiler_3.5.1    pillar_1.3.0     
 [4] git2r_0.23.0      highr_0.7         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     evaluate_0.12    
[13] tibble_1.4.2      gtable_0.2.0      pkgconfig_2.0.2  
[16] rlang_0.3.0.1     yaml_2.2.0        bindrcpp_0.2.2   
[19] stringr_1.3.1     knitr_1.20        locfit_1.5-9.1   
[22] rprojroot_1.3-2   tidyselect_0.2.4  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_1.2.4     crayon_1.3.4      R.oo_1.22.0

This reproducible R Markdown analysis was created with workflowr 1.1.1