Last updated: 2018-12-16

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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))

run deseq2 standard function

library(DESeq2)
run_deseq2 <- function(dds) {
  dds = estimateSizeFactors(dds)
  ddsWARD = DESeq(dds)
  resWARD = results(ddsWARD)
  summ_pvalues(resWARD$pvalue[!is.na(resWARD$pvalue)])
  resSigWARD <- subset(resWARD, padj < 0.1)
  print(paste0("There are ",dim(resSigWARD)[1], " genes passed FDR <0.1 cutoff"))
  print(knitr::kable(signif(as.matrix(head(resWARD[order(resWARD$pvalue),])),digit=2)))
  return(resWARD)
}

Run DEseq2–No filtering

dds = DESeqDataSetFromMatrix(countData = countall, colData = coldata,design = ~condition)
res <- run_deseq2(dds)

Expand here to see past versions of deseq2-1.png:
Version Author Date
49ecf6e simingz 2018-12-16 
[1] "There are 0 genes passed FDR <0.1 cutoff"

                      baseMean   log2FoldChange   lfcSE   stat    pvalue   padj
-------------------  ---------  ---------------  ------  -----  --------  -----
ENSG00000104722.13        2.60            -1.70   0.380   -4.5   7.4e-06   0.12
ENSG00000175899.14        0.88            -1.60   0.420   -3.8   1.5e-04   1.00
ENSG00000184900.15        3.90             0.43   0.120    3.6   3.7e-04   1.00
ENSG00000131669.9         0.99             0.82   0.230    3.5   4.3e-04   1.00
ENSG00000175390.13       16.00            -0.21   0.065   -3.3   1.1e-03   1.00
ENSG00000101198.14        1.80            -1.10   0.340   -3.2   1.3e-03   1.00

Run DEseq2–at least one cell UMI > 0

dds = DESeqDataSetFromMatrix(countData = countall[totalcount>0,], colData = coldata,design = ~condition)
res <- run_deseq2(dds)

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

                      baseMean   log2FoldChange   lfcSE   stat    pvalue   padj
-------------------  ---------  ---------------  ------  -----  --------  -----
ENSG00000104722.13        2.60            -1.70   0.380   -4.5   7.4e-06   0.12
ENSG00000175899.14        0.88            -1.60   0.420   -3.8   1.5e-04   1.00
ENSG00000184900.15        3.90             0.43   0.120    3.6   3.7e-04   1.00
ENSG00000131669.9         0.99             0.82   0.230    3.5   4.3e-04   1.00
ENSG00000175390.13       16.00            -0.21   0.065   -3.3   1.1e-03   1.00
ENSG00000101198.14        1.80            -1.10   0.340   -3.2   1.3e-03   1.00

Run DEseq2–3% cells with UMI > 0

dds = DESeqDataSetFromMatrix(countData = countall[cellpercent > 0.03,], colData = coldata,design = ~condition)
res <- run_deseq2(dds)

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

                      baseMean   log2FoldChange   lfcSE   stat    pvalue    padj
-------------------  ---------  ---------------  ------  -----  --------  ------
ENSG00000104722.13        2.60            -1.70   0.380   -4.5   7.4e-06   0.088
ENSG00000175899.14        0.88            -1.60   0.420   -3.9   1.1e-04   0.650
ENSG00000184900.15        3.90             0.43   0.120    3.5   3.9e-04   1.000
ENSG00000131669.9         0.99             0.82   0.240    3.4   6.3e-04   1.000
ENSG00000175390.13       16.00            -0.21   0.065   -3.3   1.0e-03   1.000
ENSG00000101198.14        1.80            -1.10   0.340   -3.3   1.1e-03   1.000

Run DEseq2–10% cells with UMI > 0

dds = DESeqDataSetFromMatrix(countData = countall[cellpercent > 0.1,], colData = coldata,design = ~condition)
res <- run_deseq2(dds)

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

                      baseMean   log2FoldChange   lfcSE   stat    pvalue    padj
-------------------  ---------  ---------------  ------  -----  --------  ------
ENSG00000104722.13        2.60            -1.70   0.380   -4.5   7.4e-06   0.071
ENSG00000175899.14        0.88            -1.60   0.410   -3.9   9.0e-05   0.430
ENSG00000184900.15        3.90             0.43   0.120    3.5   3.9e-04   1.000
ENSG00000131669.9         0.99             0.82   0.240    3.4   5.9e-04   1.000
ENSG00000175390.13       16.00            -0.21   0.065   -3.3   1.1e-03   1.000
ENSG00000101198.14        1.80            -1.10   0.340   -3.2   1.3e-03   1.000

Run DEseq2–20% cells with UMI > 0

dds = DESeqDataSetFromMatrix(countData = countall[cellpercent > 0.2,], colData = coldata,design = ~condition)
res <- run_deseq2(dds)

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

                      baseMean   log2FoldChange   lfcSE   stat    pvalue    padj
-------------------  ---------  ---------------  ------  -----  --------  ------
ENSG00000104722.13        2.60            -1.70   0.380   -4.5   7.4e-06   0.056
ENSG00000175899.14        0.88            -1.60   0.420   -3.8   1.5e-04   0.570
ENSG00000184900.15        3.90             0.43   0.120    3.5   3.9e-04   0.990
ENSG00000131669.9         0.99             0.82   0.240    3.5   5.6e-04   1.000
ENSG00000175390.13       16.00            -0.21   0.066   -3.3   1.1e-03   1.000
ENSG00000101198.14        1.80            -1.10   0.340   -3.2   1.3e-03   1.000

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      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] gridExtra_2.3               lattice_0.20-35            
 [3] DESeq2_1.20.0               SummarizedExperiment_1.10.1
 [5] DelayedArray_0.6.6          BiocParallel_1.14.2        
 [7] matrixStats_0.54.0          Biobase_2.40.0             
 [9] GenomicRanges_1.32.7        GenomeInfoDb_1.16.0        
[11] IRanges_2.14.12             S4Vectors_0.18.3           
[13] BiocGenerics_0.26.0         Matrix_1.2-14              
[15] dplyr_0.7.6                

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       highr_0.7             
 [7] latticeExtra_0.6-28    blob_1.1.1             GenomeInfoDbData_1.1.0
[10] yaml_2.2.0             RSQLite_2.1.1          pillar_1.3.0          
[13] backports_1.1.2        glue_1.3.0             digest_0.6.18         
[16] RColorBrewer_1.1-2     XVector_0.20.0         checkmate_1.8.5       
[19] colorspace_1.3-2       htmltools_0.3.6        R.oo_1.22.0           
[22] plyr_1.8.4             XML_3.98-1.16          pkgconfig_2.0.2       
[25] genefilter_1.62.0      zlibbioc_1.26.0        xtable_1.8-3          
[28] purrr_0.2.5            scales_1.0.0           whisker_0.3-2         
[31] annotate_1.58.0        git2r_0.23.0           tibble_1.4.2          
[34] htmlTable_1.12         ggplot2_3.1.0          nnet_7.3-12           
[37] lazyeval_0.2.1         survival_2.42-6        magrittr_1.5          
[40] crayon_1.3.4           memoise_1.1.0          evaluate_0.12         
[43] R.methodsS3_1.7.1      foreign_0.8-71         tools_3.5.1           
[46] data.table_1.11.6      stringr_1.3.1          locfit_1.5-9.1        
[49] munsell_0.5.0          cluster_2.0.7-1        AnnotationDbi_1.42.1  
[52] bindrcpp_0.2.2         compiler_3.5.1         rlang_0.3.0.1         
[55] RCurl_1.95-4.11        rstudioapi_0.8         htmlwidgets_1.2       
[58] bitops_1.0-6           base64enc_0.1-3        rmarkdown_1.10        
[61] gtable_0.2.0           DBI_1.0.0              R6_2.3.0              
[64] knitr_1.20             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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