++ sed 's/,/\t/g;s/"//g' ++ echo USER_NAME=thomasgrnbk,TYPE=RNAseq,SLAM=N,BASE_FOLDER=/faststorage/project/PAN_illumina/results/,FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/,FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK,RUNname=2025-10-17-MTD_vs_TOsK_vs_bamTOsK,TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/,LIB_STORAGE_FOLDER=/faststorage/project/PAN_illumina/data/libSTORAGE/,SINGULARITYdir=/home/thomasgrnbk/AP_singu/,downDIR=/faststorage/project/PAN_illumina/tmp//NGS_downloads/,DEBUG=N,VERSION=r6.63,asmHUBpath=,ASMdir=,ASMname=,BAM=N,fwADAPTOR=,rvADAPTOR=,N_TRIMM=,rawPAIRED=N,onlyPAIRED=N,FASTQout=N,FASTQoutRAW=N,SUBSAMPLE=,MIN_LENGTH=18,MAX_LENGTH=1000,RAW=,TRIMM=Y,FIRST=6,LAST=200,INVERT=N,Ychrom=N,RANDOMmulti=N,MM=3,FILTERING_INPUT=rRNA:tRNA:mito,WIG=Y,WIG_FASTA=,spikeINnorm=N,noNORM=,EXTEND=0,COMPUTING=C,GRIDsystem=SLURM,keepTMP=N,SCRIPT_DIR=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/,BASE_UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/,UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/dmel/dm6/,RELEASE=,VERSION=r6.63,UTILITY_DIR=/faststorage/project/PAN_illumina/backup/scripts/AnnotationPipeline/utility-files/,nFILES=18,FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/files.txt,FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK,GENOME_VERSION=dm6,subCOLOR=0~128~0,FORCE=,SYSTEM=EXTERN,LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/LOGs/,prepareREF=no,nSPLITS=1000000,SE=N,SE2nd=,maxCOUNT=1000000,demuxFASTA=N,autoViewLimits=,only5end=,PingPong=,DGE=Y,GEO=,noSTRANDED=,FORCEimport=,exportBAM=N,exportBAMuncollapsed=N,RATIOtracks=N,GENOMEdir=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/,newCOLLECTION=N,prepANNOTATIONgff=,prepGENOMEfasta=,prepTRANSCRIPTOMEfasta=,prepCDSfasta=,prepNCRNAfasta=,refGENO=ToSK_white,GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white,Nexec=1 + VARI='USER_NAME=thomasgrnbk TYPE=RNAseq SLAM=N BASE_FOLDER=/faststorage/project/PAN_illumina/results/ FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK RUNname=2025-10-17-MTD_vs_TOsK_vs_bamTOsK TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ LIB_STORAGE_FOLDER=/faststorage/project/PAN_illumina/data/libSTORAGE/ SINGULARITYdir=/home/thomasgrnbk/AP_singu/ downDIR=/faststorage/project/PAN_illumina/tmp//NGS_downloads/ DEBUG=N VERSION=r6.63 asmHUBpath= ASMdir= ASMname= BAM=N fwADAPTOR= rvADAPTOR= N_TRIMM= rawPAIRED=N onlyPAIRED=N FASTQout=N FASTQoutRAW=N SUBSAMPLE= MIN_LENGTH=18 MAX_LENGTH=1000 RAW= TRIMM=Y FIRST=6 LAST=200 INVERT=N Ychrom=N RANDOMmulti=N MM=3 FILTERING_INPUT=rRNA:tRNA:mito WIG=Y WIG_FASTA= spikeINnorm=N noNORM= EXTEND=0 COMPUTING=C GRIDsystem=SLURM keepTMP=N SCRIPT_DIR=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/ BASE_UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/ UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/dmel/dm6/ RELEASE= VERSION=r6.63 UTILITY_DIR=/faststorage/project/PAN_illumina/backup/scripts/AnnotationPipeline/utility-files/ nFILES=18 FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/files.txt FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK GENOME_VERSION=dm6 subCOLOR=0~128~0 FORCE= SYSTEM=EXTERN LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/LOGs/ prepareREF=no nSPLITS=1000000 SE=N SE2nd= maxCOUNT=1000000 demuxFASTA=N autoViewLimits= only5end= PingPong= DGE=Y GEO= noSTRANDED= FORCEimport= exportBAM=N exportBAMuncollapsed=N RATIOtracks=N GENOMEdir=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/ newCOLLECTION=N prepANNOTATIONgff= prepGENOMEfasta= prepTRANSCRIPTOMEfasta= prepCDSfasta= prepNCRNAfasta= refGENO=ToSK_white GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white Nexec=1' + eval 'USER_NAME=thomasgrnbk TYPE=RNAseq SLAM=N BASE_FOLDER=/faststorage/project/PAN_illumina/results/ FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK RUNname=2025-10-17-MTD_vs_TOsK_vs_bamTOsK TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ LIB_STORAGE_FOLDER=/faststorage/project/PAN_illumina/data/libSTORAGE/ SINGULARITYdir=/home/thomasgrnbk/AP_singu/ downDIR=/faststorage/project/PAN_illumina/tmp//NGS_downloads/ DEBUG=N VERSION=r6.63 asmHUBpath= ASMdir= ASMname= BAM=N fwADAPTOR= rvADAPTOR= N_TRIMM= rawPAIRED=N onlyPAIRED=N FASTQout=N FASTQoutRAW=N SUBSAMPLE= MIN_LENGTH=18 MAX_LENGTH=1000 RAW= TRIMM=Y FIRST=6 LAST=200 INVERT=N Ychrom=N RANDOMmulti=N MM=3 FILTERING_INPUT=rRNA:tRNA:mito WIG=Y WIG_FASTA= spikeINnorm=N noNORM= EXTEND=0 COMPUTING=C GRIDsystem=SLURM keepTMP=N SCRIPT_DIR=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/ BASE_UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/ UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/dmel/dm6/ RELEASE= VERSION=r6.63 UTILITY_DIR=/faststorage/project/PAN_illumina/backup/scripts/AnnotationPipeline/utility-files/ nFILES=18 FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/files.txt FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK GENOME_VERSION=dm6 subCOLOR=0~128~0 FORCE= SYSTEM=EXTERN LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/LOGs/ prepareREF=no nSPLITS=1000000 SE=N SE2nd= maxCOUNT=1000000 demuxFASTA=N autoViewLimits= only5end= PingPong= DGE=Y GEO= noSTRANDED= FORCEimport= exportBAM=N exportBAMuncollapsed=N RATIOtracks=N GENOMEdir=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/ newCOLLECTION=N prepANNOTATIONgff= prepGENOMEfasta= prepTRANSCRIPTOMEfasta= prepCDSfasta= prepNCRNAfasta= refGENO=ToSK_white GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white Nexec=1' ++ USER_NAME=thomasgrnbk ++ TYPE=RNAseq ++ SLAM=N ++ BASE_FOLDER=/faststorage/project/PAN_illumina/results/ ++ FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ ++ FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK ++ RUNname=2025-10-17-MTD_vs_TOsK_vs_bamTOsK ++ TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ ++ LIB_STORAGE_FOLDER=/faststorage/project/PAN_illumina/data/libSTORAGE/ ++ SINGULARITYdir=/home/thomasgrnbk/AP_singu/ ++ downDIR=/faststorage/project/PAN_illumina/tmp//NGS_downloads/ ++ DEBUG=N ++ VERSION=r6.63 ++ asmHUBpath= ++ ASMdir= ++ ASMname= ++ BAM=N ++ fwADAPTOR= ++ rvADAPTOR= ++ N_TRIMM= ++ rawPAIRED=N ++ onlyPAIRED=N ++ FASTQout=N ++ FASTQoutRAW=N ++ SUBSAMPLE= ++ MIN_LENGTH=18 ++ MAX_LENGTH=1000 ++ RAW= ++ TRIMM=Y ++ FIRST=6 ++ LAST=200 ++ INVERT=N ++ Ychrom=N ++ RANDOMmulti=N ++ MM=3 ++ FILTERING_INPUT=rRNA:tRNA:mito ++ WIG=Y ++ WIG_FASTA= ++ spikeINnorm=N ++ noNORM= ++ EXTEND=0 ++ COMPUTING=C ++ GRIDsystem=SLURM ++ keepTMP=N ++ SCRIPT_DIR=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/ ++ BASE_UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/ ++ UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/dmel/dm6/ ++ RELEASE= ++ VERSION=r6.63 ++ UTILITY_DIR=/faststorage/project/PAN_illumina/backup/scripts/AnnotationPipeline/utility-files/ ++ nFILES=18 ++ FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/files.txt ++ FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK ++ GENOME_VERSION=dm6 ++ subCOLOR=0~128~0 ++ FORCE= ++ SYSTEM=EXTERN ++ LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/LOGs/ ++ prepareREF=no ++ nSPLITS=1000000 ++ SE=N ++ SE2nd= ++ maxCOUNT=1000000 ++ demuxFASTA=N ++ autoViewLimits= ++ only5end= ++ PingPong= ++ DGE=Y ++ GEO= ++ noSTRANDED= ++ FORCEimport= ++ exportBAM=N ++ exportBAMuncollapsed=N ++ RATIOtracks=N ++ GENOMEdir=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/ ++ newCOLLECTION=N ++ prepANNOTATIONgff= ++ prepGENOMEfasta= ++ prepTRANSCRIPTOMEfasta= ++ prepCDSfasta= ++ prepNCRNAfasta= ++ refGENO=ToSK_white ++ GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white ++ Nexec=1 + echo USER_NAME=thomasgrnbk,TYPE=RNAseq,SLAM=N,BASE_FOLDER=/faststorage/project/PAN_illumina/results/,FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/,FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK,RUNname=2025-10-17-MTD_vs_TOsK_vs_bamTOsK,TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/,LIB_STORAGE_FOLDER=/faststorage/project/PAN_illumina/data/libSTORAGE/,SINGULARITYdir=/home/thomasgrnbk/AP_singu/,downDIR=/faststorage/project/PAN_illumina/tmp//NGS_downloads/,DEBUG=N,VERSION=r6.63,asmHUBpath=,ASMdir=,ASMname=,BAM=N,fwADAPTOR=,rvADAPTOR=,N_TRIMM=,rawPAIRED=N,onlyPAIRED=N,FASTQout=N,FASTQoutRAW=N,SUBSAMPLE=,MIN_LENGTH=18,MAX_LENGTH=1000,RAW=,TRIMM=Y,FIRST=6,LAST=200,INVERT=N,Ychrom=N,RANDOMmulti=N,MM=3,FILTERING_INPUT=rRNA:tRNA:mito,WIG=Y,WIG_FASTA=,spikeINnorm=N,noNORM=,EXTEND=0,COMPUTING=C,GRIDsystem=SLURM,keepTMP=N,SCRIPT_DIR=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/,BASE_UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/,UTILITY_LOCATION=/faststorage/project/PAN_illumina/utilities/AnnotationPipeline/dmel/dm6/,RELEASE=,VERSION=r6.63,UTILITY_DIR=/faststorage/project/PAN_illumina/backup/scripts/AnnotationPipeline/utility-files/,nFILES=18,FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/files.txt,FOLDER_NAME=MTD_vs_TOsK_vs_bamTOsK,GENOME_VERSION=dm6,subCOLOR=0~128~0,FORCE=,SYSTEM=EXTERN,LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/LOGs/,prepareREF=no,nSPLITS=1000000,SE=N,SE2nd=,maxCOUNT=1000000,demuxFASTA=N,autoViewLimits=,only5end=,PingPong=,DGE=Y,GEO=,noSTRANDED=,FORCEimport=,exportBAM=N,exportBAMuncollapsed=N,RATIOtracks=N,GENOMEdir=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/,newCOLLECTION=N,prepANNOTATIONgff=,prepGENOMEfasta=,prepTRANSCRIPTOMEfasta=,prepCDSfasta=,prepNCRNAfasta=,refGENO=ToSK_white,GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white,Nexec=1 + sed 's/,/\n/g' ++ date +%s + TIME=1760722147 + TIMEx=1760722147 + [[ SLURM == SLURM ]] + CORES=2 + echo 2 + source /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/tools ++ set -a + Rscript /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/DGE.R TMP=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ refGENO=ToSK_white GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ DGE=Y Nexec=1 + singularity exec --cleanenv -B /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/:/tmp /home/thomasgrnbk/AP_singu/R.simg Rscript /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/script-files/DGE.R TMP=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ refGENO=ToSK_white GENO=BamTosK_white~BamTosK_piwi~MTD_piwi~MTD_white~ToSK_piwi~ToSK_white FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/ DGE=Y Nexec=1 ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ✔ dplyr 1.1.3 ✔ readr 2.1.4 ✔ forcats 1.0.0 ✔ stringr 1.5.0 ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 ✔ lubridate 1.9.3 ✔ tidyr 1.3.0 ✔ purrr 1.0.2 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::combine() masks gridExtra::combine() ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag() ℹ Use the conflicted package () to force all conflicts to become errors Attaching package: ‘reshape2’ The following object is masked from ‘package:tidyr’: smiths Attaching package: ‘cowplot’ The following object is masked from ‘package:lubridate’: stamp Attaching package: ‘plotly’ The following object is masked from ‘package:ggplot2’: last_plot The following object is masked from ‘package:stats’: filter The following object is masked from ‘package:graphics’: layout Loading required package: S4Vectors Loading required package: stats4 Loading required package: BiocGenerics Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:lubridate’: intersect, setdiff, union The following objects are masked from ‘package:dplyr’: combine, intersect, setdiff, union The following object is masked from ‘package:gridExtra’: combine The following objects are masked from ‘package:stats’: IQR, mad, sd, var, xtabs The following objects are masked from ‘package:base’: anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min Attaching package: ‘S4Vectors’ The following object is masked from ‘package:plotly’: rename The following objects are masked from ‘package:lubridate’: second, second<- The following objects are masked from ‘package:dplyr’: first, rename The following object is masked from ‘package:tidyr’: expand The following object is masked from ‘package:utils’: findMatches The following objects are masked from ‘package:base’: expand.grid, I, unname Loading required package: IRanges Attaching package: ‘IRanges’ The following object is masked from ‘package:plotly’: slice The following object is masked from ‘package:lubridate’: %within% The following objects are masked from ‘package:dplyr’: collapse, desc, slice The following object is masked from ‘package:purrr’: reduce Loading required package: GenomicRanges Loading required package: GenomeInfoDb Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: ‘matrixStats’ The following object is masked from ‘package:dplyr’: count Attaching package: ‘MatrixGenerics’ The following objects are masked from ‘package:matrixStats’: colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: ‘Biobase’ The following object is masked from ‘package:MatrixGenerics’: rowMedians The following objects are masked from ‘package:matrixStats’: anyMissing, rowMedians Loading required package: limma Attaching package: ‘limma’ The following object is masked from ‘package:DESeq2’: plotMA The following object is masked from ‘package:BiocGenerics’: plotMA reading in files with read_tsv 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 summarizing abundance summarizing counts summarizing length summarizing inferential replicates Warning messages: 1: In self$trans$transform(x) : NaNs produced 2: Transformation introduced infinite values in continuous y-axis 3: Removed 143015 rows containing non-finite values (`stat_boxplot()`). 4: In self$trans$transform(x) : NaNs produced 5: Transformation introduced infinite values in continuous y-axis 6: Removed 145861 rows containing non-finite values (`stat_boxplot()`). reading in files with read_tsv 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport Warning message: In DESeqDataSet(se, design = design, ignoreRank) : some variables in design formula are characters, converting to factors estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing reading in files with read_tsv 1 2 3 4 5 6 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 959 of 9725 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 552 of 9468 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 905 of 9324 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 6 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 776 of 9851 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 6 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 19 of 9580 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 557 of 9569 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 735 of 9467 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 6 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 1345 of 9887 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 6 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 948 of 9686 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 0 of 8985 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 375 of 9668 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 547 of 9401 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 743 of 9577 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 540 of 9276 genes were filtered out in DESeq2 tests reading in files with read_tsv 1 2 3 4 5 6 summarizing abundance summarizing counts summarizing length summarizing inferential replicates using counts and average transcript lengths from tximport estimating size factors using 'avgTxLength' from assays(dds), correcting for library size estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using 'apeglm' for LFC shrinkage. If used in published research, please cite: Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895 1522 of 9772 genes were filtered out in DESeq2 tests There were 18 warnings (use warnings() to see them) ++ mawk '{ print ($1-$2)/60 }' ++ singularity exec --cleanenv -B /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-10-17-MTD_vs_TOsK_vs_bamTOsK/:/tmp /home/thomasgrnbk/AP_singu/APmaster.simg mawk '{ print ($1-$2)/60 }' +++ date +%s ++ echo -e 1760722322 1760722147 + PROCESSED_TIME=2.91667 + echo 'DGE - processing_time=' 2.91667 + echo 2.91667 + exit