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Knit directory: locust-phase-transition-RNAseq/

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Load required R libraries

#(install first from CRAN or Bioconductor)
library("knitr")
library("rmdformats")
library("tidyverse")
library("DT")  # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")
library("DESeq2")
library("data.table")
library("apeglm")
library("ggpubr")
library("ggplot2")
library("ggrepel")
library("EnhancedVolcano")
library("SARTools")
library("pheatmap")
library("VennDiagram")

## Global options
options(max.print="10000")
knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE,
    cache = FALSE,
    comment = FALSE,
    prompt = FALSE,
    tidy = TRUE
)
opts_knit$set(width=75)

Import the final files from DEseq2 and edgeR

projectName <- "SPICE_HEAD"  # name of the project
author <- "Maeva TECHER"  # author of the statistical analysis/report

# Import output from edgeR/DEseq2
workDir_edgeR <- "/Users/alphamanae/Documents/GitHub/locust-phase-transition-RNAseq/data/piceifrons/edgeR_SPICE_HEAD"
setwd(workDir_edgeR)
table_edgeR <- read.table("tables/CrowdedvsIsolated.complete.txt", header = TRUE)

workDir_DEseq2 <- "/Users/alphamanae/Documents/GitHub/locust-phase-transition-RNAseq/data/piceifrons/DEseq2_SPICE_HEAD"
setwd(workDir_DEseq2)
table_DEseq2 <- read.table("tables/CrowdedvsIsolated.complete.txt", header = TRUE)

tresh_logfold <- 1
tresh_padj <- 0.05

# Select DE genes
table_edgeR_DE <- subset(table_edgeR, table_edgeR$padj <= tresh_padj & (table_edgeR$log2FoldChange >=
    tresh_logfold | table_edgeR$log2FoldChange <= -tresh_logfold), select = Id:trended.dispersion)

table_DEseq2_DE <- subset(table_DEseq2, table_DEseq2$padj <= tresh_padj & (table_DEseq2$log2FoldChange >=
    tresh_logfold | table_DEseq2$log2FoldChange <= -tresh_logfold), select = Id:maxCooks)

# Write output file
setwd(workDir_edgeR)
Filename <- paste("DE-genes_[", projectName, "_edgeR]_[", length(table_edgeR_DE[[1]]),
    "_genes].txt", sep = "")
write.table(table_edgeR_DE, file = Filename, row.names = FALSE, quote = FALSE, sep = "\t")

setwd(workDir_DEseq2)
Filename <- paste("DE-genes_[", projectName, "_DEseq2]_[", length(table_DEseq2_DE[[1]]),
    "_genes].txt", sep = "")
write.table(table_DEseq2_DE, file = Filename, row.names = FALSE, quote = FALSE, sep = "\t")

Calculate similarities between DEseq2 and edgeR

table_edgeR_DE$Coder <- "edgeR"
table_edgeR_DE  <- table_edgeR_DE[c("Coder", "Id")]
table_DEseq2_DE$Coder <- "DEseq2"
table_DEseq2_DE  <- table_DEseq2_DE[c("Coder", "Id")]
table5 <- rbind(table_edgeR_DE, table_DEseq2_DE)

dupsBetweenGroups <- function (df, idcol) {
    datacols <- setdiff(names(df), idcol)
    sortorder <- do.call(order, df)
    df <- df[sortorder,]
    dupWithin <- duplicated(df)
    dupBetween = rep(NA, nrow(df))
    dupBetween[!dupWithin] <- duplicated(df[!dupWithin,datacols])
    dupBetween[!dupWithin] <- duplicated(df[!dupWithin,datacols], fromLast=TRUE) | dupBetween[!dupWithin]
    goodIdx <- !is.na(dupBetween)
    goodVals <- c(NA, dupBetween[goodIdx])
    fillIdx <- cumsum(goodIdx)+1
    dupBetween <- goodVals[fillIdx]
    dupBetween[sortorder] <- dupBetween
    return(dupBetween)}
dupRows <- dupsBetweenGroups(table5, "Coder")
table5 <- cbind(table5, dup=dupRows)
table5 <- subset(table5, Coder=="edgeR" & dup==TRUE, select=-Coder)
setwd('..')
Filename <- paste("DE-genes_strict_[", projectName, "]_[", length(table5[[1]]), "_genes].txt", sep = "")
write.table(table5, file = Filename, row.names = FALSE, quote = FALSE, sep="\t")

#Print result statistics
cat("Amongst a total of", length(table_edgeR[[1]]), "genes,
DEseq2 found", length(table_DEseq2_DE[[1]]), "differentially expressed genes. 
EdgeR found",  length(table_edgeR_DE[[1]]), "differentially expressed genes.
A total of", length(table5[[1]]), "genes was found by both programs.")
FALSE Amongst a total of 28731 genes,
FALSE DEseq2 found 378 differentially expressed genes. 
FALSE EdgeR found 342 differentially expressed genes.
FALSE A total of 238 genes was found by both programs.
test_a_vector <- as.vector(unlist(table_DEseq2_DE$Id))
test_b_vector <- as.vector(unlist(table_edgeR_DE$Id))

library("RColorBrewer")
myCol <- brewer.pal(3, "Pastel2")

venn.plot <- venn.diagram(
        x = list(test_a_vector, test_b_vector),
        category.names = c("DEseq2" , "EdgeR"),
        filename = 'venn_diagramm.png',
        output=TRUE,
        
        # Output features
        imagetype="png" ,
        resolution = 300,
        compression = "lzw",
        
        # Circles
        lwd = 2,
        lty = 'blank',
        fill = c("red", "blue"),
        
        # Numbers
        cex = 3,
        fontface = "bold",
        fontfamily = "sans",
        
        # Set names
        cat.cex = 1.5,
        cat.fontface = "bold",
        cat.default.pos = "outer",
        cat.fontfamily = "sans"
)


sessionInfo()
FALSE R version 4.2.1 (2022-06-23)
FALSE Platform: x86_64-apple-darwin17.0 (64-bit)
FALSE Running under: macOS Big Sur ... 10.16
FALSE 
FALSE Matrix products: default
FALSE BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
FALSE LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
FALSE 
FALSE locale:
FALSE [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
FALSE 
FALSE attached base packages:
FALSE [1] grid      stats4    stats     graphics  grDevices utils     datasets 
FALSE [8] methods   base     
FALSE 
FALSE other attached packages:
FALSE  [1] RColorBrewer_1.1-3          VennDiagram_1.7.3          
FALSE  [3] futile.logger_1.4.3         pheatmap_1.0.12            
FALSE  [5] SARTools_1.8.1              kableExtra_1.3.4           
FALSE  [7] edgeR_3.38.4                limma_3.52.4               
FALSE  [9] ashr_2.2-54                 EnhancedVolcano_1.14.0     
FALSE [11] ggrepel_0.9.1               ggpubr_0.4.0               
FALSE [13] apeglm_1.18.0               data.table_1.14.4          
FALSE [15] DESeq2_1.36.0               SummarizedExperiment_1.26.1
FALSE [17] Biobase_2.56.0              MatrixGenerics_1.8.1       
FALSE [19] matrixStats_0.62.0          GenomicRanges_1.48.0       
FALSE [21] GenomeInfoDb_1.32.4         IRanges_2.31.2             
FALSE [23] S4Vectors_0.34.0            BiocGenerics_0.42.0        
FALSE [25] reshape2_1.4.4              ggthemes_4.2.4             
FALSE [27] plotly_4.10.0               DT_0.26                    
FALSE [29] forcats_0.5.2               stringr_1.4.1              
FALSE [31] dplyr_1.0.10                purrr_0.3.5                
FALSE [33] readr_2.1.3                 tidyr_1.2.1                
FALSE [35] tibble_3.1.8                ggplot2_3.3.6              
FALSE [37] tidyverse_1.3.2             rmdformats_1.0.4           
FALSE [39] knitr_1.40                 
FALSE 
FALSE loaded via a namespace (and not attached):
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FALSE  [13] fansi_1.0.3            magrittr_2.0.3         memoise_2.0.1         
FALSE  [16] googlesheets4_1.0.1    tzdb_0.3.0             Biostrings_2.64.1     
FALSE  [19] annotate_1.74.0        modelr_0.1.9           svglite_2.1.0         
FALSE  [22] bdsmatrix_1.3-6        colorspace_2.0-3       blob_1.2.3            
FALSE  [25] rvest_1.0.3            haven_2.5.1            xfun_0.34             
FALSE  [28] crayon_1.5.2           RCurl_1.98-1.9         jsonlite_1.8.3        
FALSE  [31] genefilter_1.78.0      survival_3.4-0         glue_1.6.2            
FALSE  [34] gtable_0.3.1           gargle_1.2.1           zlibbioc_1.42.0       
FALSE  [37] XVector_0.36.0         webshot_0.5.4          DelayedArray_0.22.0   
FALSE  [40] car_3.1-1              abind_1.4-5            scales_1.2.1          
FALSE  [43] futile.options_1.0.1   mvtnorm_1.1-3          GGally_2.1.2          
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FALSE  [49] viridisLite_0.4.1      xtable_1.8-4           emdbook_1.3.12        
FALSE  [52] bit_4.0.4              truncnorm_1.0-8        htmlwidgets_1.5.4     
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