Differential Gene Expression using EdgeR
Maeva Techer
2022-11-01
Last updated: 2022-11-01
Checks: 6 1
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")
## 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)Here we present the workflow example with the head data from S. piceifrons
EdgeR analysis using STAR input
Preparing input files
Raw count matrices
We generated this in the precedent section.
Transcript-to-gene annotation file
Below is the example with S. piceifrons
# Download annotation and place it into the folder refgenomes
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/021/461/385/GCF_021461385.2_iqSchPice1.1/GCF_021461385.2_iqSchPice1.1_genomic.gtf.gz
# first column is the transcript ID, second column is the gene ID, third column is the gene symbol
zcat GCF_021461385.2_iqSchPice1.1_genomic.gtf.gz | awk -F "\t" 'BEGIN{OFS="\t"}{if($3=="transcript"){split($9, a, "\""); print a[4],a[2],a[8]}}' > tx2gene.piceifrons.csvSample sheet
Differential expression analysis
Import input count data
We start by reading the sample sheet.
############################### MOSTLY FOR THE HMTL REPORT PARAMETERS for
############################### running the script
homeDir <- "/Users/alphamanae/Documents/GitHub/locust-phase-transition-RNAseq"
workDir <- "/Users/alphamanae/Documents/GitHub/locust-phase-transition-RNAseq/data/piceifrons" # Working directory
projectName <- "SPICE_HEAD" # name of the project
author <- "Maeva TECHER" # author of the statistical analysis/report
## Create all the needed directories
setwd(workDir)
Dirname <- paste("edgeR_", projectName, sep = "")
dir.create(Dirname)
setwd(Dirname)
workDir_edgeR <- getwd()
# path to the directory containing raw counts files
rawDir <- "/Users/alphamanae/Documents/GitHub/locust-phase-transition-RNAseq/data/piceifrons/STAR_counts_4thcol"
## PARAMETERS for running EdgeR
tresh_logfold <- 1 # Treshold for log2(foldchange) in final DE-files
tresh_padj <- 0.05 # Treshold for adjusted p-valued in final DE-files
alpha_edgeR <- 0.05 # threshold of statistical significance
pAdjustMethod_edgeR <- "BH" # p-value adjustment method: 'BH' (default) or 'BY'
featuresToRemove <- c(NULL) # names of the features to be removed, NULL if none or if using Idxstats
varInt <- "RearingCondition" # factor of interest
condRef <- "Isolated" # reference biological condition
batch <- NULL # blocking factor: NULL (default) or 'batch' for example
cpmCutoff <- 1 # counts-per-million cut-off to filter low counts
gene.selection <- "pairwise" # selection of the features in MDSPlot
normalizationMethod <- "TMM" # normalization method: 'TMM' (default), 'RLE' (DESeq) or 'upperquartile'
# Path and name of targetfile containing conditions and file names
targetFile <- "/Users/alphamanae/Documents/GitHub/locust-phase-transition-RNAseq/data/piceifrons/list/HeadSPICE.txt"
colors <- c("#B31B21", "#1465AC")
# checking parameters
setwd(workDir_edgeR)
checkParameters.edgeR(projectName = projectName, author = author, targetFile = targetFile,
rawDir = rawDir, featuresToRemove = featuresToRemove, varInt = varInt, condRef = condRef,
batch = batch, alpha = alpha_edgeR, pAdjustMethod = pAdjustMethod_edgeR, cpmCutoff = cpmCutoff,
gene.selection = gene.selection, normalizationMethod = normalizationMethod, colors = colors)FALSE [1] "All the parameters are correct"We followed the comprehensive tutorial for edgeR
Filtering and normalizing
# loading target file
target <- loadTargetFile(targetFile = targetFile, varInt = varInt, condRef = condRef,
batch = batch)FALSE Target file:
FALSE SampleName
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815240
FALSE SPICE_S_Iso_SRR11815251 SPICE_S_Iso_SRR11815251
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815261
FALSE SPICE_S_Iso_SRR11815273 SPICE_S_Iso_SRR11815273
FALSE SPICE_S_Iso_SRR11815274 SPICE_S_Iso_SRR11815274
FALSE SPICE_G_Crd_SRR11815268 SPICE_G_Crd_SRR11815268
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815269
FALSE SPICE_G_Crd_SRR11815270 SPICE_G_Crd_SRR11815270
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815271
FALSE SPICE_G_Crd_SRR11815272 SPICE_G_Crd_SRR11815272
FALSE FileName Tissue
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815240_counts.txt Head
FALSE SPICE_S_Iso_SRR11815251 SPICE_S_Iso_SRR11815251_counts.txt Head
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815261_counts.txt Head
FALSE SPICE_S_Iso_SRR11815273 SPICE_S_Iso_SRR11815273_counts.txt Head
FALSE SPICE_S_Iso_SRR11815274 SPICE_S_Iso_SRR11815274_counts.txt Head
FALSE SPICE_G_Crd_SRR11815268 SPICE_G_Crd_SRR11815268_counts.txt Head
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815269_counts.txt Head
FALSE SPICE_G_Crd_SRR11815270 SPICE_G_Crd_SRR11815270_counts.txt Head
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815271_counts.txt Head
FALSE SPICE_G_Crd_SRR11815272 SPICE_G_Crd_SRR11815272_counts.txt Head
FALSE RearingCondition
FALSE SPICE_S_Iso_SRR11815240 Isolated
FALSE SPICE_S_Iso_SRR11815251 Isolated
FALSE SPICE_S_Iso_SRR11815261 Isolated
FALSE SPICE_S_Iso_SRR11815273 Isolated
FALSE SPICE_S_Iso_SRR11815274 Isolated
FALSE SPICE_G_Crd_SRR11815268 Crowded
FALSE SPICE_G_Crd_SRR11815269 Crowded
FALSE SPICE_G_Crd_SRR11815270 Crowded
FALSE SPICE_G_Crd_SRR11815271 Crowded
FALSE SPICE_G_Crd_SRR11815272 Crowded# loading counts
counts <- loadCountData(target = target, rawDir = rawDir, featuresToRemove = featuresToRemove)FALSE Loading files:
FALSE SPICE_S_Iso_SRR11815240_counts.txt: 28731 rows and 14531 null count(s)
FALSE SPICE_S_Iso_SRR11815251_counts.txt: 28731 rows and 14513 null count(s)
FALSE SPICE_S_Iso_SRR11815261_counts.txt: 28731 rows and 14392 null count(s)
FALSE SPICE_S_Iso_SRR11815273_counts.txt: 28731 rows and 14221 null count(s)
FALSE SPICE_S_Iso_SRR11815274_counts.txt: 28731 rows and 14424 null count(s)
FALSE SPICE_G_Crd_SRR11815268_counts.txt: 28731 rows and 14657 null count(s)
FALSE SPICE_G_Crd_SRR11815269_counts.txt: 28731 rows and 14521 null count(s)
FALSE SPICE_G_Crd_SRR11815270_counts.txt: 28731 rows and 14504 null count(s)
FALSE SPICE_G_Crd_SRR11815271_counts.txt: 28731 rows and 14059 null count(s)
FALSE SPICE_G_Crd_SRR11815272_counts.txt: 28731 rows and 15232 null count(s)
FALSE
FALSE Features removed:
FALSE
FALSE Top of the counts matrix:
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815251
FALSE LOC124708997 610 562
FALSE LOC124708998 510 407
FALSE LOC124708999 0 1
FALSE LOC124709000 2 0
FALSE LOC124709001 0 0
FALSE LOC124709003 0 0
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815273
FALSE LOC124708997 3346 8876
FALSE LOC124708998 891 692
FALSE LOC124708999 0 0
FALSE LOC124709000 2 4
FALSE LOC124709001 0 0
FALSE LOC124709003 0 0
FALSE SPICE_S_Iso_SRR11815274 SPICE_G_Crd_SRR11815268
FALSE LOC124708997 4341 343
FALSE LOC124708998 773 439
FALSE LOC124708999 0 0
FALSE LOC124709000 4 2
FALSE LOC124709001 1 0
FALSE LOC124709003 0 0
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815270
FALSE LOC124708997 314 463
FALSE LOC124708998 461 799
FALSE LOC124708999 0 0
FALSE LOC124709000 0 0
FALSE LOC124709001 0 0
FALSE LOC124709003 0 2
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815272
FALSE LOC124708997 638 3170
FALSE LOC124708998 936 496
FALSE LOC124708999 0 0
FALSE LOC124709000 0 0
FALSE LOC124709001 0 0
FALSE LOC124709003 0 0
FALSE
FALSE Bottom of the counts matrix:
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815251
FALSE Trnav-cac 0 0
FALSE Trnav-gac 0 0
FALSE Trnav-uac 0 0
FALSE Trnaw-cca 0 0
FALSE Trnay-aua 0 0
FALSE Trnay-gua 0 0
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815273
FALSE Trnav-cac 0 0
FALSE Trnav-gac 0 0
FALSE Trnav-uac 0 0
FALSE Trnaw-cca 0 0
FALSE Trnay-aua 0 0
FALSE Trnay-gua 0 0
FALSE SPICE_S_Iso_SRR11815274 SPICE_G_Crd_SRR11815268
FALSE Trnav-cac 0 0
FALSE Trnav-gac 0 0
FALSE Trnav-uac 0 0
FALSE Trnaw-cca 0 0
FALSE Trnay-aua 0 0
FALSE Trnay-gua 0 0
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815270
FALSE Trnav-cac 0 0
FALSE Trnav-gac 0 0
FALSE Trnav-uac 0 0
FALSE Trnaw-cca 0 0
FALSE Trnay-aua 0 0
FALSE Trnay-gua 0 0
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815272
FALSE Trnav-cac 0 0
FALSE Trnav-gac 0 0
FALSE Trnav-uac 0 0
FALSE Trnaw-cca 0 0
FALSE Trnay-aua 0 0
FALSE Trnay-gua 0 0## Create DGEList data class
dge <- DGEList(counts = counts, group = target[, varInt])
dgeFALSE An object of class "DGEList"
FALSE $counts
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815251
FALSE LOC124708997 610 562
FALSE LOC124708998 510 407
FALSE LOC124708999 0 1
FALSE LOC124709000 2 0
FALSE LOC124709001 0 0
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815273
FALSE LOC124708997 3346 8876
FALSE LOC124708998 891 692
FALSE LOC124708999 0 0
FALSE LOC124709000 2 4
FALSE LOC124709001 0 0
FALSE SPICE_S_Iso_SRR11815274 SPICE_G_Crd_SRR11815268
FALSE LOC124708997 4341 343
FALSE LOC124708998 773 439
FALSE LOC124708999 0 0
FALSE LOC124709000 4 2
FALSE LOC124709001 1 0
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815270
FALSE LOC124708997 314 463
FALSE LOC124708998 461 799
FALSE LOC124708999 0 0
FALSE LOC124709000 0 0
FALSE LOC124709001 0 0
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815272
FALSE LOC124708997 638 3170
FALSE LOC124708998 936 496
FALSE LOC124708999 0 0
FALSE LOC124709000 0 0
FALSE LOC124709001 0 0
FALSE 28726 more rows ...
FALSE
FALSE $samples
FALSE group lib.size norm.factors
FALSE SPICE_S_Iso_SRR11815240 Isolated 13138876 1
FALSE SPICE_S_Iso_SRR11815251 Isolated 12486995 1
FALSE SPICE_S_Iso_SRR11815261 Isolated 21999636 1
FALSE SPICE_S_Iso_SRR11815273 Isolated 20477987 1
FALSE SPICE_S_Iso_SRR11815274 Isolated 18270932 1
FALSE SPICE_G_Crd_SRR11815268 Crowded 11399887 1
FALSE SPICE_G_Crd_SRR11815269 Crowded 12289340 1
FALSE SPICE_G_Crd_SRR11815270 Crowded 18269364 1
FALSE SPICE_G_Crd_SRR11815271 Crowded 20693369 1
FALSE SPICE_G_Crd_SRR11815272 Crowded 10020390 1## Filter out the genes with low counts
keep <- filterByExpr(y = dge)
dge <- dge[keep, , keep.lib.sizes = FALSE]
dgeFALSE An object of class "DGEList"
FALSE $counts
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815251
FALSE LOC124708997 610 562
FALSE LOC124708998 510 407
FALSE LOC124709005 63 75
FALSE LOC124709006 13 67
FALSE LOC124709012 175162 144392
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815273
FALSE LOC124708997 3346 8876
FALSE LOC124708998 891 692
FALSE LOC124709005 103 114
FALSE LOC124709006 44 59
FALSE LOC124709012 265102 233388
FALSE SPICE_S_Iso_SRR11815274 SPICE_G_Crd_SRR11815268
FALSE LOC124708997 4341 343
FALSE LOC124708998 773 439
FALSE LOC124709005 97 66
FALSE LOC124709006 68 50
FALSE LOC124709012 241039 119771
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815270
FALSE LOC124708997 314 463
FALSE LOC124708998 461 799
FALSE LOC124709005 66 100
FALSE LOC124709006 75 40
FALSE LOC124709012 110692 151306
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815272
FALSE LOC124708997 638 3170
FALSE LOC124708998 936 496
FALSE LOC124709005 131 115
FALSE LOC124709006 65 48
FALSE LOC124709012 195999 51791
FALSE 11568 more rows ...
FALSE
FALSE $samples
FALSE group lib.size norm.factors
FALSE SPICE_S_Iso_SRR11815240 Isolated 13127949 1
FALSE SPICE_S_Iso_SRR11815251 Isolated 12475894 1
FALSE SPICE_S_Iso_SRR11815261 Isolated 21983022 1
FALSE SPICE_S_Iso_SRR11815273 Isolated 20459082 1
FALSE SPICE_S_Iso_SRR11815274 Isolated 18255578 1
FALSE SPICE_G_Crd_SRR11815268 Crowded 11388577 1
FALSE SPICE_G_Crd_SRR11815269 Crowded 12274297 1
FALSE SPICE_G_Crd_SRR11815270 Crowded 18251324 1
FALSE SPICE_G_Crd_SRR11815271 Crowded 20670445 1
FALSE SPICE_G_Crd_SRR11815272 Crowded 10004830 1## Normalization and effective library sizes
dge <- calcNormFactors(object = dge)
dgeFALSE An object of class "DGEList"
FALSE $counts
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815251
FALSE LOC124708997 610 562
FALSE LOC124708998 510 407
FALSE LOC124709005 63 75
FALSE LOC124709006 13 67
FALSE LOC124709012 175162 144392
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815273
FALSE LOC124708997 3346 8876
FALSE LOC124708998 891 692
FALSE LOC124709005 103 114
FALSE LOC124709006 44 59
FALSE LOC124709012 265102 233388
FALSE SPICE_S_Iso_SRR11815274 SPICE_G_Crd_SRR11815268
FALSE LOC124708997 4341 343
FALSE LOC124708998 773 439
FALSE LOC124709005 97 66
FALSE LOC124709006 68 50
FALSE LOC124709012 241039 119771
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815270
FALSE LOC124708997 314 463
FALSE LOC124708998 461 799
FALSE LOC124709005 66 100
FALSE LOC124709006 75 40
FALSE LOC124709012 110692 151306
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815272
FALSE LOC124708997 638 3170
FALSE LOC124708998 936 496
FALSE LOC124709005 131 115
FALSE LOC124709006 65 48
FALSE LOC124709012 195999 51791
FALSE 11568 more rows ...
FALSE
FALSE $samples
FALSE group lib.size norm.factors
FALSE SPICE_S_Iso_SRR11815240 Isolated 13127949 1.0824407
FALSE SPICE_S_Iso_SRR11815251 Isolated 12475894 1.0984955
FALSE SPICE_S_Iso_SRR11815261 Isolated 21983022 1.0086128
FALSE SPICE_S_Iso_SRR11815273 Isolated 20459082 0.9862605
FALSE SPICE_S_Iso_SRR11815274 Isolated 18255578 0.9779244
FALSE SPICE_G_Crd_SRR11815268 Crowded 11388577 1.0796259
FALSE SPICE_G_Crd_SRR11815269 Crowded 12274297 1.1719738
FALSE SPICE_G_Crd_SRR11815270 Crowded 18251324 0.9896050
FALSE SPICE_G_Crd_SRR11815271 Crowded 20670445 1.0126798
FALSE SPICE_G_Crd_SRR11815272 Crowded 10004830 0.6817906Run the model fit with EdgeR
## Dispersion estimation
dge <- estimateDisp(y = dge)
## Testing for differential gene expression
et <- exactTest(object = dge)
## Extract the table with adjusted p-values (FDR = padj for DEseq2)
top_degs = topTags(object = et, n = "Inf")
top_degsFALSE Comparison of groups: Crowded-Isolated
FALSE logFC logCPM PValue FDR
FALSE LOC124798514 3.7082936 2.391682135 3.229135e-30 3.737078e-26
FALSE LOC124712054 -6.7413463 1.050897424 1.544516e-29 8.937340e-26
FALSE LOC124798525 6.4242098 3.080744387 9.213597e-29 3.554299e-25
FALSE LOC124722326 -4.7888108 2.460969639 2.614889e-27 7.565528e-24
FALSE LOC124712031 5.5977951 3.050198672 7.450735e-27 1.724547e-23
FALSE LOC124788222 -6.3734606 6.693445588 1.030313e-23 1.987302e-20
FALSE LOC124789136 -8.2674836 2.476023732 5.759156e-20 9.521531e-17
FALSE LOC124805172 8.4136534 0.504748008 6.986180e-20 1.010638e-16
FALSE LOC124802700 -4.5225336 3.146862211 1.049843e-19 1.349982e-16
FALSE LOC124775105 -4.7501061 1.793751955 1.648828e-18 1.908189e-15
FALSE LOC124788242 -8.6845301 0.824031435 2.251385e-18 2.368662e-15
FALSE LOC124804691 8.0446862 2.450524388 5.363209e-18 5.172368e-15
FALSE LOC124789001 -7.1101819 0.709093186 1.797132e-17 1.599862e-14
FALSE LOC124722943 -6.1144324 0.543546213 3.052990e-17 2.523732e-14
FALSE LOC124803056 5.3178446 0.483769689 5.098637e-17 3.933768e-14
FALSE LOC124777333 2.8392417 2.742475984 5.546193e-17 4.011631e-14
FALSE LOC124798649 -4.9857026 -0.089840805 1.695325e-16 1.154118e-13
FALSE LOC124720060 -6.3038964 3.147548566 4.803470e-16 3.088364e-13
FALSE LOC124720306 6.3440377 -0.214513981 2.438615e-15 1.485374e-12
FALSE LOC124717148 -5.0279497 0.229976905 4.498807e-15 2.603235e-12
FALSE LOC124804802 2.4829076 4.141576060 4.964578e-15 2.735955e-12
FALSE LOC124789780 3.6074852 0.343997016 2.459357e-14 1.293734e-11
FALSE LOC124721974 -7.2878640 -0.409406125 3.479525e-14 1.742720e-11
FALSE LOC124805215 4.8985759 6.679307824 3.614040e-14 1.742720e-11
FALSE LOC124789650 -9.0399737 3.208648976 6.639751e-14 3.073674e-11
FALSE LOC124789002 -9.7566874 3.274650688 3.877403e-13 1.725892e-10
FALSE LOC124804687 4.1134318 5.506020867 9.206148e-13 3.946028e-10
FALSE LOC124804688 5.0888039 6.417295318 3.472817e-12 1.435390e-09
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FALSE [ reached 'max' / getOption("max.print") -- omitted 9073 rows ]## Obtain the summary
summary(decideTests(object = et, lfc = 1))FALSE Crowded-Isolated
FALSE Down 111
FALSE NotSig 11241
FALSE Up 221## Export the table write.csv(as.data.frame(top_degs),
## file='condition_infected_vs_control_dge.csv')DGE Visualization
Volcano plot
sakura <- as.data.frame(top_degs)
keyvals <- ifelse(sakura$logFC >= 1 & sakura$FDR <= 0.05, "#B31B21", ifelse(sakura$logFC <=
-1 & sakura$FDR <= 0.05, "#1465AC", "darkgray"))
keyvals[is.na(keyvals)] <- "darkgray"
names(keyvals)[keyvals == "#B31B21"] <- "Upregulated"
names(keyvals)[keyvals == "#1465AC"] <- "Downregulated"
names(keyvals)[keyvals == "darkgray"] <- "NS"
EnhancedVolcano(sakura, lab = rownames(sakura), x = "logFC", y = "FDR", pCutoff = 0.05,
FCcutoff = 1, pointSize = 3, labSize = 4, colAlpha = 4/5, colCustom = keyvals,
drawConnectors = TRUE)
Create a html report with EdgeR
setwd(workDir_edgeR)
# description plots
majSequences <- descriptionPlots(counts = counts, group = target[, varInt], col = colors)FALSE Matrix of SERE statistics:
FALSE SPICE_S_Iso_SRR11815240 SPICE_S_Iso_SRR11815251
FALSE SPICE_S_Iso_SRR11815240 0.000 7.681
FALSE SPICE_S_Iso_SRR11815251 7.681 0.000
FALSE SPICE_S_Iso_SRR11815261 8.278 7.525
FALSE SPICE_S_Iso_SRR11815273 8.073 8.239
FALSE SPICE_S_Iso_SRR11815274 7.342 8.961
FALSE SPICE_G_Crd_SRR11815268 8.750 5.271
FALSE SPICE_G_Crd_SRR11815269 10.462 6.957
FALSE SPICE_G_Crd_SRR11815270 11.375 8.585
FALSE SPICE_G_Crd_SRR11815271 10.225 7.865
FALSE SPICE_G_Crd_SRR11815272 21.111 20.214
FALSE SPICE_S_Iso_SRR11815261 SPICE_S_Iso_SRR11815273
FALSE SPICE_S_Iso_SRR11815240 8.278 8.073
FALSE SPICE_S_Iso_SRR11815251 7.525 8.239
FALSE SPICE_S_Iso_SRR11815261 0.000 6.150
FALSE SPICE_S_Iso_SRR11815273 6.150 0.000
FALSE SPICE_S_Iso_SRR11815274 7.051 6.487
FALSE SPICE_G_Crd_SRR11815268 8.817 9.755
FALSE SPICE_G_Crd_SRR11815269 11.514 11.846
FALSE SPICE_G_Crd_SRR11815270 11.274 11.696
FALSE SPICE_G_Crd_SRR11815271 9.681 10.195
FALSE SPICE_G_Crd_SRR11815272 21.701 21.430
FALSE SPICE_S_Iso_SRR11815274 SPICE_G_Crd_SRR11815268
FALSE SPICE_S_Iso_SRR11815240 7.342 8.750
FALSE SPICE_S_Iso_SRR11815251 8.961 5.271
FALSE SPICE_S_Iso_SRR11815261 7.051 8.817
FALSE SPICE_S_Iso_SRR11815273 6.487 9.755
FALSE SPICE_S_Iso_SRR11815274 0.000 10.433
FALSE SPICE_G_Crd_SRR11815268 10.433 0.000
FALSE SPICE_G_Crd_SRR11815269 12.567 5.995
FALSE SPICE_G_Crd_SRR11815270 12.590 6.881
FALSE SPICE_G_Crd_SRR11815271 10.910 7.054
FALSE SPICE_G_Crd_SRR11815272 20.887 19.749
FALSE SPICE_G_Crd_SRR11815269 SPICE_G_Crd_SRR11815270
FALSE SPICE_S_Iso_SRR11815240 10.462 11.375
FALSE SPICE_S_Iso_SRR11815251 6.957 8.585
FALSE SPICE_S_Iso_SRR11815261 11.514 11.274
FALSE SPICE_S_Iso_SRR11815273 11.846 11.696
FALSE SPICE_S_Iso_SRR11815274 12.567 12.590
FALSE SPICE_G_Crd_SRR11815268 5.995 6.881
FALSE SPICE_G_Crd_SRR11815269 0.000 8.480
FALSE SPICE_G_Crd_SRR11815270 8.480 0.000
FALSE SPICE_G_Crd_SRR11815271 8.659 6.828
FALSE SPICE_G_Crd_SRR11815272 20.361 20.568
FALSE SPICE_G_Crd_SRR11815271 SPICE_G_Crd_SRR11815272
FALSE SPICE_S_Iso_SRR11815240 10.225 21.111
FALSE SPICE_S_Iso_SRR11815251 7.865 20.214
FALSE SPICE_S_Iso_SRR11815261 9.681 21.701
FALSE SPICE_S_Iso_SRR11815273 10.195 21.430
FALSE SPICE_S_Iso_SRR11815274 10.910 20.887
FALSE SPICE_G_Crd_SRR11815268 7.054 19.749
FALSE SPICE_G_Crd_SRR11815269 8.659 20.361
FALSE SPICE_G_Crd_SRR11815270 6.828 20.568
FALSE SPICE_G_Crd_SRR11815271 0.000 20.397
FALSE SPICE_G_Crd_SRR11815272 20.397 0.000# edgeR analysis
out.edgeR <- run.edgeR(counts = counts, target = target, varInt = varInt, condRef = condRef,
batch = batch, cpmCutoff = cpmCutoff, normalizationMethod = normalizationMethod,
pAdjustMethod = pAdjustMethod_edgeR)FALSE Number of features discarded by the filtering:
FALSE 17679
FALSE
FALSE Design of the statistical model:
FALSE ~ RearingCondition
FALSE
FALSE Normalization factors:
FALSE [1] 1.0698955 1.1033057 1.0021391 0.9838068 0.9737239 1.0920553 1.1893713
FALSE [8] 0.9830245 1.0067124 0.6865264
FALSE Coefficients of the model: (Intercept) RearingConditionCrowded
FALSE Comparison Crowded vs Isolated: testing coefficient RearingConditionCrowded# MDS + clustering
exploreCounts(object = out.edgeR$dge, group = target[, varInt], gene.selection = gene.selection,
col = colors)FALSE quartz_off_screen
FALSE 2# summary of the analysis (boxplots, dispersions, export table, nDiffTotal,
# histograms, MA plot)
summaryResults <- summarizeResults.edgeR(out.edgeR, group = target[, varInt], counts = counts,
alpha = alpha_edgeR, col = colors)FALSE Number of features down/up and total:
FALSE Test vs Ref # down # up # total
FALSE [1,] Crowded vs Isolated 109 236 345# generating HTML report
writeReport.edgeR(target = target, counts = counts, out.edgeR = out.edgeR, summaryResults = summaryResults,
majSequences = majSequences, workDir = workDir_edgeR, projectName = projectName,
author = author, targetFile = targetFile, rawDir = rawDir, featuresToRemove = featuresToRemove,
varInt = varInt, condRef = condRef, batch = batch, alpha = alpha_edgeR, pAdjustMethod = pAdjustMethod_edgeR,
colors = colors, gene.selection = gene.selection, normalizationMethod = normalizationMethod,
cpmCutoff = cpmCutoff)FALSE HTML report created
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] stats4 stats graphics grDevices utils datasets methods
FALSE [8] base
FALSE
FALSE other attached packages:
FALSE [1] pheatmap_1.0.12 SARTools_1.8.1
FALSE [3] kableExtra_1.3.4 edgeR_3.38.4
FALSE [5] limma_3.52.4 ashr_2.2-54
FALSE [7] EnhancedVolcano_1.14.0 ggrepel_0.9.1
FALSE [9] ggpubr_0.4.0 apeglm_1.18.0
FALSE [11] data.table_1.14.4 DESeq2_1.36.0
FALSE [13] SummarizedExperiment_1.26.1 Biobase_2.56.0
FALSE [15] MatrixGenerics_1.8.1 matrixStats_0.62.0
FALSE [17] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4
FALSE [19] IRanges_2.31.2 S4Vectors_0.34.0
FALSE [21] BiocGenerics_0.42.0 reshape2_1.4.4
FALSE [23] ggthemes_4.2.4 plotly_4.10.0
FALSE [25] DT_0.26 forcats_0.5.2
FALSE [27] stringr_1.4.1 dplyr_1.0.10
FALSE [29] purrr_0.3.5 readr_2.1.3
FALSE [31] tidyr_1.2.1 tibble_3.1.8
FALSE [33] ggplot2_3.3.6 tidyverse_1.3.2
FALSE [35] rmdformats_1.0.4 knitr_1.40
FALSE
FALSE loaded via a namespace (and not attached):
FALSE [1] readxl_1.4.1 backports_1.4.1 workflowr_1.7.0
FALSE [4] systemfonts_1.0.4 plyr_1.8.7 lazyeval_0.2.2
FALSE [7] splines_4.2.1 BiocParallel_1.30.4 digest_0.6.30
FALSE [10] invgamma_1.1 htmltools_0.5.3 SQUAREM_2021.1
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] mvtnorm_1.1-3 GGally_2.1.2 DBI_1.1.3
FALSE [46] rstatix_0.7.0 Rcpp_1.0.9 viridisLite_0.4.1
FALSE [49] xtable_1.8-4 emdbook_1.3.12 bit_4.0.4
FALSE [52] truncnorm_1.0-8 htmlwidgets_1.5.4 httr_1.4.4
FALSE [55] RColorBrewer_1.1-3 ellipsis_0.3.2 farver_2.1.1
FALSE [58] reshape_0.8.9 pkgconfig_2.0.3 XML_3.99-0.11
FALSE [61] sass_0.4.2 dbplyr_2.2.1 locfit_1.5-9.6
FALSE [64] utf8_1.2.2 labeling_0.4.2 tidyselect_1.2.0
FALSE [67] rlang_1.0.6 later_1.3.0 AnnotationDbi_1.58.0
FALSE [70] munsell_0.5.0 cellranger_1.1.0 tools_4.2.1
FALSE [73] cachem_1.0.6 cli_3.4.1 generics_0.1.3
FALSE [76] RSQLite_2.2.18 broom_1.0.1 ggdendro_0.1.23
FALSE [79] evaluate_0.17 fastmap_1.1.0 yaml_2.3.6
FALSE [82] bit64_4.0.5 fs_1.5.2 KEGGREST_1.36.3
FALSE [85] whisker_0.4 formatR_1.12 xml2_1.3.3
FALSE [88] compiler_4.2.1 rstudioapi_0.14 png_0.1-7
FALSE [91] ggsignif_0.6.4 reprex_2.0.2 geneplotter_1.74.0
FALSE [94] bslib_0.4.0 stringi_1.7.8 highr_0.9
FALSE [97] lattice_0.20-45 Matrix_1.5-1 vctrs_0.5.0
FALSE [100] pillar_1.8.1 lifecycle_1.0.3 jquerylib_0.1.4
FALSE [103] irlba_2.3.5.1 bitops_1.0-7 httpuv_1.6.6
FALSE [106] R6_2.5.1 bookdown_0.29 promises_1.2.0.1
FALSE [109] gridExtra_2.3 codetools_0.2-18 MASS_7.3-58.1
FALSE [112] assertthat_0.2.1 rprojroot_2.0.3 withr_2.5.0
FALSE [115] GenomeInfoDbData_1.2.8 parallel_4.2.1 hms_1.1.2
FALSE [118] grid_4.2.1 coda_0.19-4 rmarkdown_2.17
FALSE [121] carData_3.0-5 googledrive_2.0.0 git2r_0.30.1
FALSE [124] mixsqp_0.3-43 bbmle_1.0.25 numDeriv_2016.8-1.1
FALSE [127] lubridate_1.8.0