++ echo USER_NAME=thomasgrnbk,TYPE=RNAseq,SLAM=N,BASE_FOLDER=/faststorage/project/PAN_illumina/results/,FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/,FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov,RUNname=2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov,TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/,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=2,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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=23,FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/files.txt,FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov,GENOME_VERSION=dm6,subCOLOR=0~128~0,FORCE=,SYSTEM=EXTERN,LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=w_KD_Ov,GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov,Nexec=1 ++ sed 's/,/\t/g;s/"//g' + VARI='USER_NAME=thomasgrnbk TYPE=RNAseq SLAM=N BASE_FOLDER=/faststorage/project/PAN_illumina/results/ FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov RUNname=2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ 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=2 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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=23 FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/files.txt FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov GENOME_VERSION=dm6 subCOLOR=0~128~0 FORCE= SYSTEM=EXTERN LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=w_KD_Ov GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov 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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov RUNname=2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ 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=2 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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=23 FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/files.txt FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov GENOME_VERSION=dm6 subCOLOR=0~128~0 FORCE= SYSTEM=EXTERN LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=w_KD_Ov GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov 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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ ++ FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov ++ RUNname=2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov ++ TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ ++ 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=2 ++ 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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=23 ++ FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/files.txt ++ FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov ++ GENOME_VERSION=dm6 ++ subCOLOR=0~128~0 ++ FORCE= ++ SYSTEM=EXTERN ++ LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=w_KD_Ov ++ GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov ++ 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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/,FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov,RUNname=2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov,TMPdir=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/,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=2,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/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=23,FILE_CONTAINING_LIBRARIES=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/files.txt,FOLDER_NAME=RNAseq_Thomas_PolyA_HsBam_ov,GENOME_VERSION=dm6,subCOLOR=0~128~0,FORCE=,SYSTEM=EXTERN,LOGs=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/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=w_KD_Ov,GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov,Nexec=1 + sed 's/,/\n/g' ++ date +%s + TIME=1758281502 + TIMEx=1758281502 + [[ SLURM == SLURM ]] + CORES=2 + echo 2 + source /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/script-files/tools ++ set -a + Rscript /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/script-files/DGE.R TMP=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ refGENO=w_KD_Ov GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ DGE=Y Nexec=1 + singularity exec --cleanenv -B /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/:/tmp /home/thomasgrnbk/AP_singu/R.simg Rscript /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/script-files/DGE.R TMP=/faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ refGENO=w_KD_Ov GENO=w_KD_0h~m_KD_0h~m_KD_42h~m_KD_68h~p_KD_0h~p_KD_42h~p_KD_68h~p_KD_OV~w_KD_42h~w_KD_68h~w_KD_Ov FOLDER=/faststorage/project/PAN_illumina/results//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/ 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 17 18 19 20 21 22 23 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 183256 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 189459 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 17 18 19 20 21 22 23 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 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 861 of 11097 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 436 of 11223 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 432 of 11122 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 1084 of 11183 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 653 of 11218 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 427 of 11012 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 0 of 10821 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 1085 of 11185 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 645 of 11074 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 0 of 10761 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 1052 of 10848 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 418 of 10760 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 634 of 10901 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 423 of 10903 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 207 of 10633 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 10399 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 843 of 10869 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 835 of 10763 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 10329 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 1461 of 10764 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 214 of 11005 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 424 of 10928 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 414 of 10670 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 10457 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 1266 of 10884 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 1254 of 10781 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 10397 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 212 of 10901 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 210 of 10808 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 611 of 10503 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 10255 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 1045 of 10780 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 1028 of 10601 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 10213 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 1062 of 10952 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 1036 of 10679 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 10510 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 213 of 10975 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 421 of 10836 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 10439 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 1236 of 10625 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 10466 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 635 of 10905 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 418 of 10769 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 10419 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 9985 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 414 of 10660 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 1013 of 10442 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 9926 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 10451 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 10168 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 174 of 8973 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 1666 of 10742 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 10390 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 10116 genes were filtered out in DESeq2 tests There were 50 or more warnings (use warnings() to see the first 50) ++ mawk '{ print ($1-$2)/60 }' +++ date +%s ++ singularity exec --cleanenv -B /faststorage/project/PAN_illumina/tmp//thomasgrnbk/dm6/RNAseq/2025-09-19-RNAseq_Thomas_PolyA_HsBam_ov/:/tmp /home/thomasgrnbk/AP_singu/APmaster.simg mawk '{ print ($1-$2)/60 }' ++ echo -e 1758281948 1758281502 + PROCESSED_TIME=7.43333 + echo 'DGE - processing_time=' 7.43333 + echo 7.43333 + exit