/data/images/proton/rnaSeq-pip6fast-mm10.sh ./pip.sh &> pip.log & awk -f /data/images/proton/link-bam-files-to-sample-ids1.awk samples.txt > make-links.sh ln -s /data/images/proton3/run464/EDlab/tophat_009/sort_uniq.bam ED3R9.bam ln -s /data/images/proton3/run464/EDlab/tophat_010/sort_uniq.bam ED3R10.bam ln -s /data/images/proton3/run464/EDlab/tophat_011/sort_uniq.bam ED3R11.bam ln -s /data/images/proton3/run463/EDlab/tophat_012/sort_uniq.bam ED3R12.bam ln -s /data/images/proton3/run463/EDlab/tophat_013/sort_uniq.bam ED3R13.bam ln -s /data/images/proton3/run463/EDlab/tophat_014/sort_uniq.bam ED3R14.bam ln -s /data/images/proton3/run463/EDlab/tophat_015/sort_uniq.bam ED3R15.bam ln -s /data/images/proton3/run463/EDlab/tophat_016/sort_uniq.bam ED3R16.bam awk -f /data/images/proton/make-metaseqr-targets1.awk samples.txt > targets.txt #v2.1 /data/results/tools/r/R-4.0.0/bin/R library(metaseqR2) buildDir <- file.path(tempdir(),"mm10_anndb") dir.create(buildDir) myDb <- file.path(buildDir,"mm10_annDB.sqlite") organisms <- list(mm10=98) sources <- ifelse(.Platform$OS.type=="unix",c("ensembl","refseq", "ucsc"),"ensembl") buildAnnotationDatabase(organisms,sources,forceDownload=FALSE,db=myDb,rc=0.5) the.path <- "/data/images/proton3/run464/EDlab" the.contrasts.1 <- c( "Cond1_vs_Cond2" ) result <- metaseqr2( #counts = "/home/tzanos/Desktop/HuR/gene_counts_merged_k20_mm10.txt", sampleList=file.path(the.path, "targets.txt"), #excludeList = c("KO01", "KO02", "KO21", "KO22", "KO61", "KO62", "wt01", "wt02", "wt21", "wt22", "wt61", "wt62"), fileType = "bam", contrast=the.contrasts.1, org="mm10", #annotation="embedded", localDb ="/data/results/tools/rnaseq/metaseqr/mm10/annotation.sqlite", refdb = "ensembl", transLevel = "gene", countType="utr", normalization="deseq", statistics=c("deseq","deseq2","edger","noiseq","limma","nbpseq","absseq","dss"), adjustMethod = "fdr", metaP="pandora", figFormat=c("png","pdf", "jpg"), exportWhere=file.path(the.path, "metaseqR2_run464"), restrictCores=0.5, qcPlots=c("mds","biodetection","countsbio","saturation","readnoise", "rnacomp","gcbias", "pairwise","filtered","correl","boxplot","lengthbias","meandiff", "meanvar","boxplot", "filtered", "biodist","volcano","mastat" #, "deheatmap" ), exonFilters=NULL, geneFilters=list( #length=list(length=500), avgReads=list(averagePerBp=100, quantile=0.25), expression=list(median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA), biotype=getDefaults("biotypeFilter", "mm10"), presence = list( frac=0.5, minCount=1, perCondition=TRUE ) ), #pcut=0.05, outList = TRUE, exportWhat=c("annotation","p_value","adj_p_value","meta_p_value", "adj_meta_p_value","fold_change","stats","counts","flags"), exportScale=c("natural","log2","rpgm"), exportValues="normalized", exportStats = c("mean", "median", "sd", "mad", "cv"), exportCountsTable=TRUE, #reportTop=0.05, saveGeneModel = TRUE, createTracks=TRUE, overwrite=TRUE, trackInfo=list( stranded=TRUE, normTo=1e+8, #urlBase="http://epigenomics.fleming.gr/home/tzanos/public_html/DK/HuR/metaseqR2_DK_pandora_tracks/tracks", hubInfo=list( name="EDHub", shortLabel="ED Hub", longLabel="ED hub long", email="reczko@fleming.gr" ) ) ) annotation="embedded", embedCols=list( idCol=4, gcCol=5, nameCol=8, btCol=7 ), # v1.2 library(metaseqR) the.path <- "/data/images/proton3/run464/EDlab" the.contrasts.1 <- c( "Cond1_vs_Cond2" ) # Read transcript data from external file transcript.data <- read.delim("/data/results/tools/rnaseq/metaseqr/mm10/transcript_data_mm10.txt.gz") rownames(transcript.data) <- as.character(transcript.data$transcript_id) # metaseqR related variables outside the pipeline multic <- check.parallel(0.5) # If wish to use multiple cores assign("VERBOSE",TRUE,envir=metaseqR:::meta.env) # Read targets files message("Reading targets file...") targets <- read.targets("targets.txt") # Do the counting based with bam files in the targets file r2c.out <- read2count(targets,transcript.data,has.all.fields=TRUE,multic=multic) # Create a counts table to be passed as "embedded" annotation the.counts <- cbind(r2c.out$mergedann,r2c.out$counts) # Run metaseqR metaseqr( counts=the.counts, sample.list=targets$samples, contrast=the.contrasts.1, annotation="embedded", gene.file="/data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz", id.col=4, # required with embedded annotation gc.col=5, # required with embedded annotation bt.col=8, # required with embedded annotation name.col=7, # required with embedded annotation org="custom", count.type="utr", normalization="deseq", # or whatever supported statistics="deseq", # or whatever supported, more than one also fig.format=c("png","pdf"), export.where=file.path(the.path,"metaseqr_run464"), restrict.cores=0.5, # fraction of available cores to use qc.plots=c( "mds","biodetection","countsbio","saturation","readnoise","filtered", "correl","pairwise","boxplot","gcbias","lengthbias","meandiff", "meanvar","biodist","volcano","deheatmap" ), exon.filters=NULL, gene.filters=list( length=list( length=500 ), avg.reads=list( average.per.bp=100, quantile=0.25 ), expression=list( median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA ), # it's the default anyway biotype=get.defaults("biotype.filter","mm10") ), pcut=0.05, # only for the truncated significant list output, all results are exported anyway export.what=c("annotation","p.value","adj.p.value","fold.change","stats", "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added export.scale=c("log2","rpgm"), export.values="normalized", export.stats="mean" ) sessionInfo() R version 3.5.2 (2018-12-20) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 10 (buster) Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets [8] methods base other attached packages: [1] metaseqR_1.22.1 qvalue_2.14.1 [3] limma_3.38.3 DESeq_1.34.1 [5] lattice_0.20-38 locfit_1.5-9.1 [7] EDASeq_2.16.3 ShortRead_1.40.0 [9] GenomicAlignments_1.18.1 SummarizedExperiment_1.12.0 [11] DelayedArray_0.8.0 matrixStats_0.55.0 [13] Rsamtools_1.34.1 GenomicRanges_1.34.0 [15] GenomeInfoDb_1.18.2 Biostrings_2.50.2 [17] XVector_0.22.0 IRanges_2.16.0 [19] S4Vectors_0.20.1 BiocParallel_1.16.6 [21] Biobase_2.42.0 BiocGenerics_0.28.0 loaded via a namespace (and not attached): [1] bitops_1.0-6 bit64_0.9-7 RColorBrewer_1.1-2 [4] progress_1.2.2 httr_1.4.1 tools_3.5.2 [7] R6_2.4.1 affyio_1.52.0 KernSmooth_2.23-16 [10] DBI_1.1.0 lazyeval_0.2.2 colorspace_1.4-1 [13] tidyselect_1.1.0 prettyunits_1.1.1 preprocessCore_1.44.0 [16] bit_1.1-15.1 compiler_3.5.2 rtracklayer_1.42.2 [19] caTools_1.17 scales_1.1.0 genefilter_1.64.0 [22] affy_1.60.0 stringr_1.4.0 digest_0.6.23 [25] R.utils_2.9.2 NBPSeq_0.3.0 pkgconfig_2.0.3 [28] baySeq_2.16.0 rlang_0.4.10 RSQLite_2.2.0 [31] generics_0.1.0 hwriter_1.3.2 gtools_3.8.1 [34] dplyr_1.0.4 R.oo_1.23.0 RCurl_1.98-1.1 [37] magrittr_1.5 GenomeInfoDbData_1.2.0 Matrix_1.2-18 [40] Rcpp_1.0.3 munsell_0.5.0 abind_1.4-5 [43] lifecycle_0.2.0 R.methodsS3_1.7.1 vsn_3.50.0 [46] stringi_1.4.5 edgeR_3.24.3 zlibbioc_1.28.0 [49] gplots_3.0.1.2 plyr_1.8.5 grid_3.5.2 [52] blob_1.2.1 gdata_2.18.0 crayon_1.3.4 [55] splines_3.5.2 GenomicFeatures_1.34.8 annotate_1.60.1 [58] hms_0.5.3 pillar_1.4.3 log4r_0.3.2 [61] rjson_0.2.20 geneplotter_1.60.0 reshape2_1.4.3 [64] biomaRt_2.38.0 XML_3.99-0.3 glue_1.4.2 [67] latticeExtra_0.6-28 BiocManager_1.30.10 vctrs_0.3.6 [70] gtable_0.3.0 purrr_0.3.3 ggplot2_3.2.1 [73] aroma.light_3.12.0 xtable_1.8-4 survival_3.1-8 [76] tibble_2.1.3 NOISeq_2.26.1 AnnotationDbi_1.44.0 [79] memoise_1.1.0 corrplot_0.84 brew_1.0-6 > #@ metaserq2 log /data/results/tools/r/R-4.0.0/bin/R R version 4.0.0 (2020-04-24) -- "Arbor Day" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > require(metaseqR2) Loading required package: metaseqR2 Loading required package: DESeq2 Loading required package: S4Vectors Loading required package: stats4 Loading required package: BiocGenerics Loading required package: parallel Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:parallel’: clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB The following objects are masked from ‘package:stats’: IQR, mad, sd, var, xtabs The following objects are masked from ‘package:base’: anyDuplicated, 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:base’: expand.grid Loading required package: IRanges Loading required package: GenomicRanges Loading required package: GenomeInfoDb Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats 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 Loading required package: locfit locfit 1.5-9.4 2020-03-24 Loading required package: splines Attaching package: ‘metaseqR2’ The following object is masked from ‘package:limma’: readTargets > sessionInfo() R version 4.0.0 (2020-04-24) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 10 (buster) Matrix products: default BLAS: /mnt/fix/c/solid.data_results.recovered.data/tools/r/R-4.0.0/lib/libRblas.so LAPACK: /mnt/fix/c/solid.data_results.recovered.data/tools/r/R-4.0.0/lib/libRlapack.so locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] splines parallel stats4 stats graphics grDevices utils [8] datasets methods base other attached packages: [1] metaseqR2_1.3.13 locfit_1.5-9.4 [3] limma_3.46.0 DESeq2_1.30.1 [5] SummarizedExperiment_1.20.0 Biobase_2.50.0 [7] MatrixGenerics_1.2.1 matrixStats_0.58.0 [9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2 [11] IRanges_2.24.1 S4Vectors_0.28.1 [13] BiocGenerics_0.36.0 loaded via a namespace (and not attached): [1] R.utils_2.10.1 tidyselect_1.1.0 [3] heatmaply_1.2.1 RSQLite_2.2.3 [5] AnnotationDbi_1.52.0 htmlwidgets_1.5.3 [7] grid_4.0.0 TSP_1.1-10 [9] BiocParallel_1.24.1 baySeq_2.24.0 [11] munsell_0.5.0 preprocessCore_1.52.1 [13] codetools_0.2-16 DT_0.17 [15] colorspace_2.0-0 knitr_1.31 [17] rstudioapi_0.13 GenomeInfoDbData_1.2.4 [19] hwriter_1.3.2 bit64_4.0.5 [21] rhdf5_2.34.0 vctrs_0.3.6 [23] generics_0.1.0 lambda.r_1.2.4 [25] xfun_0.21 BiocFileCache_1.14.0 [27] EDASeq_2.24.0 R6_2.5.0 [29] rmdformats_1.0.1 seriation_1.2-9 [31] NBPSeq_0.3.0 bitops_1.0-6 [33] rhdf5filters_1.2.0 cachem_1.0.4 [35] DelayedArray_0.16.1 assertthat_0.2.1 [37] scales_1.1.1 bsseq_1.26.0 [39] gtable_0.3.0 affy_1.68.0 [41] log4r_0.3.2 rlang_0.4.10 [43] genefilter_1.72.1 rtracklayer_1.50.0 [45] lazyeval_0.2.2 DSS_2.38.0 [47] yaml_2.2.1 BiocManager_1.30.10 [49] reshape2_1.4.4 abind_1.4-5 [51] GenomicFeatures_1.42.1 qvalue_2.22.0 [53] tools_4.0.0 lava_1.6.8.1 [55] bookdown_0.21 ggplot2_3.3.3 [57] affyio_1.60.0 ellipsis_0.3.1 [59] gplots_3.1.1 RColorBrewer_1.1-2 [61] Rcpp_1.0.6 plyr_1.8.6 [63] sparseMatrixStats_1.2.1 progress_1.2.2 [65] zlibbioc_1.36.0 purrr_0.3.4 [67] RCurl_1.98-1.2 prettyunits_1.1.1 [69] openssl_1.4.3 viridis_0.5.1 [71] zoo_1.8-8 magrittr_2.0.1 [73] data.table_1.14.0 futile.options_1.0.1 [75] survcomp_1.40.0 aroma.light_3.20.0 [77] hms_1.0.0 evaluate_0.14 [79] xtable_1.8-4 XML_3.99-0.5 [81] VennDiagram_1.6.20 jpeg_0.1-8.1 [83] gridExtra_2.3 compiler_4.0.0 [85] biomaRt_2.46.3 tibble_3.0.6 [87] KernSmooth_2.23-17 crayon_1.4.1 [89] R.oo_1.24.0 htmltools_0.5.1.1 [91] tidyr_1.1.2 geneplotter_1.68.0 [93] DBI_1.1.1 SuppDists_1.1-9.5 [95] formatR_1.7 corrplot_0.84 [97] dbplyr_2.1.0 MASS_7.3-51.6 [99] rappdirs_0.3.3 ShortRead_1.48.0 [101] Matrix_1.2-18 rmeta_3.0 [103] permute_0.9-5 vsn_3.58.0 [105] R.methodsS3_1.8.1 pkgconfig_2.0.3 [107] GenomicAlignments_1.26.0 registry_0.5-1 [109] plotly_4.9.3 xml2_1.3.2 [111] foreach_1.5.1 annotate_1.68.0 [113] webshot_0.5.2 XVector_0.30.0 [115] prodlim_2019.11.13 stringr_1.4.0 [117] digest_0.6.27 Biostrings_2.58.0 [119] rmarkdown_2.7 harmonicmeanp_3.0 [121] dendextend_1.14.0 edgeR_3.32.1 [123] DelayedMatrixStats_1.12.3 curl_4.3 [125] Rsamtools_2.6.0 gtools_3.8.2 [127] lifecycle_1.0.0 jsonlite_1.7.2 [129] ABSSeq_1.44.0 Rhdf5lib_1.12.1 [131] survivalROC_1.0.3 futile.logger_1.4.3 [133] viridisLite_0.3.0 askpass_1.1 [135] BSgenome_1.58.0 pillar_1.4.7 [137] lattice_0.20-41 fastmap_1.1.0 [139] httr_1.4.2 survival_3.2-3 [141] glue_1.4.2 png_0.1-7 [143] iterators_1.0.13 pander_0.6.3 [145] bit_4.0.4 stringi_1.5.3 [147] HDF5Array_1.18.1 bootstrap_2019.6 [149] blob_1.2.1 latticeExtra_0.6-29 [151] caTools_1.18.1 memoise_2.0.0 [153] FMStable_0.1-2 dplyr_1.0.4 > the.path <- "/data/images/proton3/run464/EDlab" the.contrasts.1 <- c( "Cond1_vs_Cond2" ) result <- metaseqr2( #counts = "/home/tzanos/Desktop/HuR/gene_counts_merged_k20_mm10.txt", sampleList=file.path(the.path, "targets.txt"), #excludeList = c("KO01", "KO02", "KO21", "KO22", "KO61", "KO62", "wt01", "wt02", "wt21", "wt22", "wt61", "wt62"), fileType = "bam", contrast=the.contrasts.1, org="mm10", #annotation="embedded", localDb ="/data/results/tools/rnaseq/metaseqr/mm10/annotation.sqlite", refdb = "ensembl", transLevel = "gene", countType="utr", normalization="deseq", statistics=c("deseq","deseq2","edger","noiseq","limma","nbpseq","absseq","dss"), adjustMethod = "fdr", metaP="pandora", figFormat=c("png","pdf", "jpg"), exportWhere=file.path(the.path, "metaseqR2_run464"), restrictCores=0.5, qcPlots=c("mds","biodetection","countsbio","saturation","readnoise", "rnacomp","gcbias", "pairwise","filtered","correl","boxplot","lengthbias","meandiff", "meanvar","boxplot", "filtered", "biodist","volcano","mastat" #, "deheatmap" ), exonFilters=NULL, geneFilters=list( #length=list(length=500), avgReads=list(averagePerBp=100, quantile=0.25), expression=list(median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA), biotype=getDefaults("biotypeFilter", "mm10"), presence = list( frac=0.5, minCount=1, perCondition=TRUE ) ), #pcut=0.05, outList = TRUE, exportWhat=c("annotation","p_value","adj_p_value","meta_p_value", "adj_meta_p_value","fold_change","stats","counts","flags"), exportScale=c("natural","log2","rpgm"), exportValues="normalized", exportStats = c("mean", "median", "sd", "mad", "cv"), exportCountsTable=TRUE, #reportTop=0.05, saveGeneModel = TRUE, createTracks=TRUE, overwrite=TRUE, trackInfo=list( stranded=TRUE, normTo=1e+8, #urlBase="http://epigenomics.fleming.gr/home/tzanos/public_html/DK/HuR/metaseqR2_DK_pandora_tracks/tracks", hubInfo=list( name="EDHub", shortLabel="ED Hub", longLabel="ED hub long", email="reczko@fleming.gr" ) ) ) > + + > > > + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2021-02-24 16:58:02: Data processing started... Read counts file: imported sam/bam/bed files Conditions: Cond1, Cond2 Samples to include: ED3R9, ED3R10, ED3R11, ED3R12, ED3R13, ED3R14, ED3R15, ED3R16 Samples to exclude: none Requested contrasts: Cond1_vs_Cond2 Organism: mm10 Reference source: ensembl Count type: utr 3' UTR fraction: 1 3' UTR minimum length: 300bps 3' UTR downstream: 50bps Transcriptional level: gene Exon filters: none applied Gene filters: avgReads, expression, biotype, presence avgReads: averagePerBp: 100 quantile: 0.25 expression: median: TRUE mean: FALSE quantile: NA known: NA custom: NA biotype: pseudogene: FALSE snRNA: FALSE protein_coding: FALSE antisense: FALSE miRNA: FALSE snoRNA: FALSE lincRNA: FALSE processed_transcript: FALSE misc_RNA: FALSE rRNA: TRUE sense_intronic: FALSE sense_overlapping: FALSE polymorphic_pseudogene: FALSE IG_C_gene: FALSE IG_J_gene: FALSE IG_D_gene: FALSE IG_LV_gene: FALSE IG_V_gene: FALSE IG_V_pseudogene: TRUE TR_V_gene: FALSE TR_V_pseudogene: TRUE three_prime_overlapping_ncrna: FALSE presence: frac: 0.5 minCount: 1 perCondition: TRUE Filter application: postnorm Normalization algorithm: deseq Normalization arguments: locfunc: [[list(function (x, na.rm = FALSE, ...) UseMethod("median"))locfunc Statistical algorithm(s): deseq, deseq2, edger, noiseq, limma, nbpseq, absseq, dss Statistical arguments: deseq: blind, fit-only, local deseq2: FALSE, parametric, 100, FALSE, NULL, FALSE, 1e-08, TRUE, FALSE, TRUE, 0, greaterAbs, TRUE, 0.1, BH, DataFrame, FALSE, FALSE edger: classic, 5, 10, movingave, NULL, grid, 11, c(-6, 6), NULL, CoxReid, 10000, NULL, auto, NULL, NULL, NULL, NULL, 0.125, NULL, auto, chisq, TRUE, FALSE, c(0.05, 0.1) noiseq: 0.5, n, biological, class, NULL, 0.2, 5, 0.02, 1, 15, 100, 1.5, 0.9, 0, NULL, 500, 1 limma: none nbpseq: nbsmyth, list(nbpseq = "log-linear-rel-mean", nbsmyth = "NBP"), HOA, two.sided absseq: FALSE, NULL, 0.1, 0.3, 100, BH, TRUE, FALSE, FALSE, TRUE, 0.05, 0, FALSE dss: FALSE, FALSE Meta-analysis method: pandora Multiple testing correction: fdr Logarithmic transformation offset: 1 Quality control plots: mds, biodetection, countsbio, saturation, readnoise, rnacomp, gcbias, pairwise, filtered, correl, boxplot, lengthbias, meandiff, meanvar, boxplot, filtered, biodist, volcano, mastat Figure format: png, pdf, jpg Output directory: /data/images/proton3/run464/EDlab/metaseqR2_run464 Output data: annotation, p_value, adj_p_value, meta_p_value, adj_meta_p_value, fold_change, stats, counts, flags Output scale(s): natural, log2, rpgm Output values: normalized Output statistics: mean, median, sd, mad, cv Loading gene annotation... Loading 3' UTR annotation... Resizing transcript 3' UTRs... Reading bam file ED3R9.bam for sample with name ED3R9. This might take some time... Reading bam file ED3R10.bam for sample with name ED3R10. This might take some time... Reading bam file ED3R11.bam for sample with name ED3R11. This might take some time... Reading bam file ED3R12.bam for sample with name ED3R12. This might take some time... Reading bam file ED3R13.bam for sample with name ED3R13. This might take some time... Reading bam file ED3R14.bam for sample with name ED3R14. This might take some time... Reading bam file ED3R15.bam for sample with name ED3R15. This might take some time... Reading bam file ED3R16.bam for sample with name ED3R16. This might take some time... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Counting reads overlapping with given annotation... ...for single-end reads... ...assuming forward sequenced reads... Finished counting! Exporting raw read counts table to /data/images/proton3/run464/EDlab/metaseqR2_run464/lists/raw_counts_table.txt.gz Checking chromosomes in transcript counts and gene annotation... Processing transcripts... Separating transcripts (UTR regions) per gene for ED3R9... Separating transcripts (UTR regions) per gene for ED3R10... Separating transcripts (UTR regions) per gene for ED3R11... Separating transcripts (UTR regions) per gene for ED3R12... Separating transcripts (UTR regions) per gene for ED3R13... Separating transcripts (UTR regions) per gene for ED3R14... Separating transcripts (UTR regions) per gene for ED3R15... Separating transcripts (UTR regions) per gene for ED3R16... Saving gene model to /data/images/proton3/run464/EDlab/metaseqR2_run464/data/gene_model.RData Summarizing count data... Removing genes with zero counts in all samples... Normalizing with: deseq Applying gene filter avgReads... Threshold below which ignored: 0.0871840078967714 Applying gene filter expression... Threshold below which ignored: 13 Applying gene filter biotype... Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene Applying gene filter presence... Threshold below which ignored: 2 40616 genes filtered out 14748 genes remain after filtering Running statistical tests with: deseq Contrast: Cond1_vs_Cond2 Running statistical tests with: deseq2converting counts to integer mode gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates Contrast: Cond1_vs_Cond2 Running statistical tests with: edger Contrast: Cond1_vs_Cond2 Running statistical tests with: noiseq Contrast: Cond1_vs_Cond2Computing Z values... ...k-means clustering done Size of 15 clusters: [1] 59 353 16 174 4 7 2 89 691 8583 2 3269 8 22 1469 Resampling cluster...[1] 1 [1] 2 [1] 3 [1] 4 [1] 5 [1] 6 [1] 7 [1] 8 [1] 9 [1] 10 Size of 15 subclusters of cluster: 10 [1] 820 167 556 250 2181 1430 390 455 473 70 417 597 284 169 324 [1] 11 [1] 12 Size of 15 subclusters of cluster: 12 [1] 79 414 204 142 230 454 100 5 33 547 248 253 200 322 38 [1] 13 [1] 14 [1] 15 Size of 15 subclusters of cluster: 15 [1] 6 105 17 178 31 85 64 148 120 188 255 51 125 83 13 Computing Z for noise... Computing probability of differential expression... p0 = 0.19116960169897 Probability Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.7813 0.9418 0.8042 0.9694 1.0000 Running statistical tests with: limma Contrast: Cond1_vs_Cond2 Running statistical tests with: nbpseq Contrast: Cond1_vs_Cond2 Running statistical tests with: absseq Contrast: Cond1_vs_Cond2eistimating size factors.... calculating parameters and fitting.... Normalizing used user! Calling p-value and adjusted it.... Running statistical tests with: dss Contrast: Cond1_vs_Cond2 Exporting and compressing normalized read counts table to /data/images/proton3/run464/EDlab/metaseqR2_run464/lists/normalized_counts_table.txt Performing meta-analysis with pandora Building output files... Contrast: Cond1_vs_Cond2 Adding non-filtered data... binding annotation... binding p-values... binding FDRs... binding meta p-values... binding adjusted meta p-values... binding natural normalized fold changes... binding log2 normalized fold changes... binding normalized mean counts... binding normalized median counts... binding normalized count sds... binding normalized count MADs... binding normalized count CVs... binding normalized mean counts... binding normalized median counts... binding normalized count sds... binding normalized count MADs... binding normalized count CVs... binding all normalized counts for Cond1... binding all normalized counts for Cond2... binding filtering flags... Writing output... Adding filtered data... binding annotation... binding p-values... binding FDRs... binding meta p-values... binding adjusted meta p-values... binding natural normalized fold changes... binding log2 normalized fold changes... binding normalized mean counts... binding normalized median counts... binding normalized count sds... binding normalized count MADs... binding normalized count CVs... binding normalized mean counts... binding normalized median counts... binding normalized count sds... binding normalized count MADs... binding normalized count CVs... binding all normalized counts for Cond1... binding all normalized counts for Cond2... binding filtering flags... Writing output... Adding report data... binding annotation... binding meta p-values... binding adjusted meta p-values... binding log2 normalized fold changes... binding normalized mean counts... binding normalized mean counts... Creating quality control graphs... Plotting in png format... Plotting mds... Plotting biodetection...Biotypes detection is to be computed for: [1] "ED3R9" "ED3R10" "ED3R11" "ED3R12" "ED3R13" "ED3R14" "ED3R15" "ED3R16" Plotting countsbio...[1] "Count distributions are to be computed for:" [1] "ED3R9" "ED3R10" "ED3R11" "ED3R12" "ED3R13" "ED3R14" "ED3R15" "ED3R16" Plotting saturation... Plotting readnoise... Plotting correl... Plotting rnacomp...[1] "Reference sample is: ED3R9" [1] "Confidence intervals for median of M:" 0.36% 99.64% Diagnostic Test ED3R10 "-0.0416633689487959" "-0.0416633689487959" "FAILED" ED3R11 "-0.113482424609565" "-0.0839112408582283" "FAILED" ED3R12 "0.264067985645339" "0.264067985645339" "FAILED" ED3R13 "-0.359893765237976" "-0.304263169660856" "FAILED" ED3R14 "-0.308622458205424" "-0.266561221688668" "FAILED" ED3R15 "0.195930308174848" "0.195930308174848" "FAILED" ED3R16 "-0.195980915550516" "-0.1454079716629" "FAILED" [1] "Diagnostic test: FAILED. Normalization is required to correct this bias." Plotting gcbias... Plotting boxplot... Plotting lengthbias... Plotting meandiff... Plotting meanvar... Plotting rnacomp...[1] "Warning: 447 features with 0 counts in all samples are to be removed for this analysis." [1] "Reference sample is: ED3R9" [1] "Confidence intervals for median of M:" 0.36% 99.64% Diagnostic Test ED3R10 "0.109156460940412" "0.109156460940412" "FAILED" ED3R11 "0.0423699707747731" "0.0423699707747731" "FAILED" ED3R12 "0.0512397352960047" "0.0512397352960047" "FAILED" ED3R13 "-0.0027241950822482" "-0.0027241950822482" "FAILED" ED3R14 "-0.0770800219591693" "-0.0770800219591693" "FAILED" ED3R15 "0.0559838411003715" "0.0559838411003715" "FAILED" ED3R16 "0.055301874407829" "0.055301874407829" "FAILED" [1] "Diagnostic test: FAILED. Normalization is required to correct this bias." Plotting gcbias... Plotting boxplot... Plotting lengthbias... Plotting meandiff... Plotting meanvar... Plotting biodist... Contrast: Cond1_vs_Cond2[1] "11666 differentially expressed features" Plotting volcano... Contrast: Cond1_vs_Cond2 Plotting mastat... Contrast: Cond1_vs_Cond2 Plotting filtered... Plotting in pdf format... Plotting mds... Plotting biodetection...Biotypes detection is to be computed for: [1] "ED3R9" "ED3R10" "ED3R11" "ED3R12" "ED3R13" "ED3R14" "ED3R15" "ED3R16" Plotting countsbio...[1] "Count distributions are to be computed for:" [1] "ED3R9" "ED3R10" "ED3R11" "ED3R12" "ED3R13" "ED3R14" "ED3R15" "ED3R16" Plotting saturation... Plotting readnoise... Plotting correl... Plotting rnacomp...[1] "Reference sample is: ED3R9" [1] "Confidence intervals for median of M:" 0.36% 99.64% Diagnostic Test ED3R10 "-0.0416633689487959" "-0.0416633689487959" "FAILED" ED3R11 "-0.114887637331618" "-0.0831741349143706" "FAILED" ED3R12 "0.264067985645339" "0.264067985645339" "FAILED" ED3R13 "-0.362367390561438" "-0.304929602986511" "FAILED" ED3R14 "-0.307788512815141" "-0.265392167857765" "FAILED" ED3R15 "0.195930308174848" "0.195930308174848" "FAILED" ED3R16 "-0.195980915550516" "-0.146940337152988" "FAILED" [1] "Diagnostic test: FAILED. Normalization is required to correct this bias." Plotting gcbias... Plotting boxplot... Plotting lengthbias... Plotting meandiff... Plotting meanvar... Plotting rnacomp...[1] "Warning: 447 features with 0 counts in all samples are to be removed for this analysis." [1] "Reference sample is: ED3R9" [1] "Confidence intervals for median of M:" 0.36% 99.64% Diagnostic Test ED3R10 "0.109156460940412" "0.109156460940412" "FAILED" ED3R11 "0.0423699707747731" "0.0423699707747731" "FAILED" ED3R12 "0.0512397352960047" "0.0512397352960047" "FAILED" ED3R13 "-0.0027241950822482" "-0.0027241950822482" "FAILED" ED3R14 "-0.0770800219591693" "-0.0770800219591693" "FAILED" ED3R15 "0.0559838411003715" "0.0559838411003715" "FAILED" ED3R16 "0.055301874407829" "0.055301874407829" "FAILED" [1] "Diagnostic test: FAILED. Normalization is required to correct this bias." Plotting gcbias... Plotting boxplot... Plotting lengthbias... Plotting meandiff... Plotting meanvar... Plotting biodist... Contrast: Cond1_vs_Cond2[1] "11666 differentially expressed features" Plotting volcano... Contrast: Cond1_vs_Cond2 Plotting mastat... Contrast: Cond1_vs_Cond2 Plotting filtered... Plotting in jpg format... Plotting mds... Plotting biodetection...Biotypes detection is to be computed for: [1] "ED3R9" "ED3R10" "ED3R11" "ED3R12" "ED3R13" "ED3R14" "ED3R15" "ED3R16" Plotting countsbio...[1] "Count distributions are to be computed for:" [1] "ED3R9" "ED3R10" "ED3R11" "ED3R12" "ED3R13" "ED3R14" "ED3R15" "ED3R16" Plotting saturation... Plotting readnoise... Plotting correl... Plotting rnacomp...[1] "Reference sample is: ED3R9" [1] "Confidence intervals for median of M:" 0.36% 99.64% Diagnostic Test ED3R10 "-0.0416633689487959" "-0.0416633689487959" "FAILED" ED3R11 "-0.114612419808927" "-0.0844044866002866" "FAILED" ED3R12 "0.264067985645339" "0.264067985645339" "FAILED" ED3R13 "-0.356137620193914" "-0.302203348511567" "FAILED" ED3R14 "-0.31150800150299" "-0.265334079173516" "FAILED" ED3R15 "0.195930308174848" "0.195930308174848" "FAILED" ED3R16 "-0.195980915550516" "-0.145456734673572" "FAILED" [1] "Diagnostic test: FAILED. Normalization is required to correct this bias." Plotting gcbias... Plotting boxplot... Plotting lengthbias... Plotting meandiff... Plotting meanvar... Plotting rnacomp...[1] "Warning: 447 features with 0 counts in all samples are to be removed for this analysis." [1] "Reference sample is: ED3R9" [1] "Confidence intervals for median of M:" 0.36% 99.64% Diagnostic Test ED3R10 "0.109156460940412" "0.109156460940412" "FAILED" ED3R11 "0.0423699707747731" "0.0423699707747731" "FAILED" ED3R12 "0.0512397352960047" "0.0512397352960047" "FAILED" ED3R13 "-0.0027241950822482" "-0.0027241950822482" "FAILED" ED3R14 "-0.0770800219591693" "-0.0770800219591693" "FAILED" ED3R15 "0.0559838411003715" "0.0559838411003715" "FAILED" ED3R16 "0.055301874407829" "0.055301874407829" "FAILED" [1] "Diagnostic test: FAILED. Normalization is required to correct this bias." Plotting gcbias... Plotting boxplot... Plotting lengthbias... Plotting meandiff... Plotting meanvar... Plotting biodist... Contrast: Cond1_vs_Cond2[1] "11666 differentially expressed features" Plotting volcano... Contrast: Cond1_vs_Cond2 Plotting mastat... Contrast: Cond1_vs_Cond2 Plotting filtered...Creating tracks... stranded mode Reading positive strand reads from BAM files to Rle... reading /data/images/proton3/run464/EDlab/ED3R9.bam reading /data/images/proton3/run464/EDlab/ED3R10.bam reading /data/images/proton3/run464/EDlab/ED3R11.bam reading /data/images/proton3/run464/EDlab/ED3R12.bam reading /data/images/proton3/run464/EDlab/ED3R13.bam reading /data/images/proton3/run464/EDlab/ED3R14.bam reading /data/images/proton3/run464/EDlab/ED3R15.bam reading /data/images/proton3/run464/EDlab/ED3R16.bam Reading negative strand reads from BAM files to Rle... reading /data/images/proton3/run464/EDlab/ED3R9.bam reading /data/images/proton3/run464/EDlab/ED3R10.bam reading /data/images/proton3/run464/EDlab/ED3R11.bam reading /data/images/proton3/run464/EDlab/ED3R12.bam reading /data/images/proton3/run464/EDlab/ED3R13.bam reading /data/images/proton3/run464/EDlab/ED3R14.bam reading /data/images/proton3/run464/EDlab/ED3R15.bam reading /data/images/proton3/run464/EDlab/ED3R16.bam Normalizing positive strand... Normalizing negative strand... Exporting bigWig files... exporting + strand for sample ED3R9 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R9_plus.bigWig exporting + strand for sample ED3R10 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R10_plus.bigWig exporting + strand for sample ED3R11 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R11_plus.bigWig exporting + strand for sample ED3R12 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R12_plus.bigWig exporting + strand for sample ED3R13 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R13_plus.bigWig exporting + strand for sample ED3R14 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R14_plus.bigWig exporting + strand for sample ED3R15 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R15_plus.bigWig exporting + strand for sample ED3R16 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R16_plus.bigWig exporting - strand for sample ED3R9 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R9_minus.bigWig exporting - strand for sample ED3R10 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R10_minus.bigWig exporting - strand for sample ED3R11 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R11_minus.bigWig exporting - strand for sample ED3R12 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R12_minus.bigWig exporting - strand for sample ED3R13 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R13_minus.bigWig exporting - strand for sample ED3R14 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R14_minus.bigWig exporting - strand for sample ED3R15 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R15_minus.bigWig exporting - strand for sample ED3R16 to /data/images/proton3/run464/EDlab/metaseqR2_run464/tracks/mm10/ED3R16_minus.bigWig Importing mds... Importing biodetection... Importing countsbio... Importing saturation... Importing readnoise... Importing filtered... Importing boxplot... Importing gcbias... Importing lengthbias... Importing meandif... Importing meanvar... Importing rnacomp... Importing volcano Cond1_vs_Cond2 Cond1_vs_Cond2 Importing mastat Cond1_vs_Cond2 Cond1_vs_Cond2 Importing biodist Cond1_vs_Cond2 Writing plot database in /data/images/proton3/run464/EDlab/metaseqR2_run464/data/reportdb.js Creating HTML report... Compressing figures... adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R10.png (deflated 9%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R11.png (deflated 8%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R12.png (deflated 9%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R13.png (deflated 8%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R14.png (deflated 9%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R15.png (deflated 9%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R16.png (deflated 8%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biodetection_ED3R9.png (deflated 9%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/biotype_saturation.png (deflated 3%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/correlation_correlogram.png (deflated 2%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/correlation_heatmap.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R10.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R11.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R12.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R13.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R14.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R15.png (deflated 10%) adding: data/images/proton3/run464/EDlab/metaseqR2_run464/plots/qc/countsbio_ED3R16.png (deflated 10%) adding: 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|..................................................................... | 98% ordinary text without R code |..................................................................... | 99% label: references |......................................................................| 99% ordinary text without R code |......................................................................| 100% label: bind_event_changes (with options) List of 1 $ engine: chr "js" output file: metaseqr2_report.knit.md /usr/bin/pandoc +RTS -K512m -RTS metaseqr2_report.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /mnt/max/a/genomics_facility/proton3/run464/EDlab/metaseqR2_run464/index.html --lua-filter /home/reczko/R/x86_64-pc-linux-gnu-library/4.0/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /home/reczko/R/x86_64-pc-linux-gnu-library/4.0/rmarkdown/rmarkdown/lua/latex-div.lua +RTS -K2048m -RTS --variable 'material:true' --variable 'lightbox:true' --variable 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Total processing time: 11 minutes 34 seconds Warning messages: 1: "yes" and "no" for read strandedness have been deprecated. Please use "forward", "forward" or "no". Replacing "yes" with "forward"... 2: Using formula(x) is deprecated when x is a character vector of length > 1. Consider formula(paste(x, collapse = " ")) instead. 3: Pairwise sample comparison plot becomes indistinguishable for more than 6 samples! Removing from plots... 4: The p-value threshold when plotType is "deheatmap", "volcano", "biodist", "mastat", "deregulogram", "statvenn" or "foldvenn"! must allow the normal plotting of DEG diagnostic plots! Setting to 0.05... 5: The p-value threshold when plotType is "deheatmap", "volcano", "biodist", "mastat", "deregulogram", "statvenn" or "foldvenn"! must allow the normal plotting of DEG diagnostic plots! 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Please specify in ggplotly() or plot_ly() > > > q() #@ "deheatmap" /data/results/tools/r/R-4.0.0/bin/R require(metaseqR2) the.path <- "/data/images/proton3/run464/EDlab" the.contrasts.1 <- c( "Cond1_vs_Cond2" ) result <- metaseqr2( #counts = "/home/tzanos/Desktop/HuR/gene_counts_merged_k20_mm10.txt", sampleList=file.path(the.path, "targets.txt"), #excludeList = c("KO01", "KO02", "KO21", "KO22", "KO61", "KO62", "wt01", "wt02", "wt21", "wt22", "wt61", "wt62"), fileType = "bam", contrast=the.contrasts.1, org="mm10", #annotation="embedded", localDb ="/data/results/tools/rnaseq/metaseqr/mm10/annotation.sqlite", refdb = "ensembl", transLevel = "gene", countType="utr", normalization="deseq", statistics=c("deseq","deseq2","edger","noiseq","limma","nbpseq","absseq","dss"), adjustMethod = "fdr", metaP="pandora", figFormat=c("png","pdf", "jpg"), exportWhere=file.path(the.path, "metaseqR2_run464v2"), restrictCores=0.5, qcPlots=c("mds","biodetection","countsbio","saturation","readnoise", "rnacomp","gcbias", "pairwise","filtered","correl","boxplot","lengthbias","meandiff", "meanvar","boxplot", "filtered", "biodist","volcano","mastat", "deheatmap" ), exonFilters=NULL, geneFilters=list( #length=list(length=500), avgReads=list(averagePerBp=100, quantile=0.25), expression=list(median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA), biotype=getDefaults("biotypeFilter", "mm10"), presence = list( frac=0.5, minCount=1, perCondition=TRUE ) ), #pcut=0.05, outList = TRUE, exportWhat=c("annotation","p_value","adj_p_value","meta_p_value", "adj_meta_p_value","fold_change","stats","counts","flags"), exportScale=c("natural","log2","rpgm"), exportValues="normalized", exportStats = c("mean", "median", "sd", "mad", "cv"), exportCountsTable=TRUE, #reportTop=0.05, saveGeneModel = TRUE, createTracks=TRUE, overwrite=TRUE, trackInfo=list( stranded=TRUE, normTo=1e+8, #urlBase="http://epigenomics.fleming.gr/home/tzanos/public_html/DK/HuR/metaseqR2_DK_pandora_tracks/tracks", hubInfo=list( name="EDHub", shortLabel="ED Hub", longLabel="ED hub long", email="reczko@fleming.gr" ) ) ) result <- metaseqr2( #counts = "/home/tzanos/Desktop/HuR/gene_counts_merged_k20_mm10.txt", sampleList=file.path(the.path, "targets2.txt"), #excludeList = c("KO01", "KO02", "KO21", "KO22", "KO61", "KO62", "wt01", "wt02", "wt21", "wt22", "wt61", "wt62"), fileType = "bam", contrast=the.contrasts.1, org="mm10", #annotation="embedded", localDb ="/data/results/tools/rnaseq/metaseqr/mm10/annotation.sqlite", refdb = "ensembl", transLevel = "gene", countType="utr", normalization="deseq", statistics=c("deseq","deseq2","edger","noiseq","limma","nbpseq","absseq","dss"), adjustMethod = "fdr", metaP="pandora", figFormat=c("png","pdf", "jpg"), exportWhere=file.path(the.path, "metaseqR2_run464v2-wo-R12-R15"), restrictCores=0.5, qcPlots=c("mds","biodetection","countsbio","saturation","readnoise", "rnacomp","gcbias", "pairwise","filtered","correl","boxplot","lengthbias","meandiff", "meanvar","boxplot", "filtered", "biodist","volcano","mastat", "deheatmap" ), exonFilters=NULL, geneFilters=list( #length=list(length=500), avgReads=list(averagePerBp=100, quantile=0.25), expression=list(median=TRUE, mean=FALSE, quantile=NA, known=NA, custom=NA), biotype=getDefaults("biotypeFilter", "mm10"), presence = list( frac=0.5, minCount=1, perCondition=TRUE ) ), #pcut=0.05, outList = TRUE, exportWhat=c("annotation","p_value","adj_p_value","meta_p_value", "adj_meta_p_value","fold_change","stats","counts","flags"), exportScale=c("natural","log2","rpgm"), exportValues="normalized", exportStats = c("mean", "median", "sd", "mad", "cv"), exportCountsTable=TRUE, #reportTop=0.05, saveGeneModel = TRUE, createTracks=TRUE, overwrite=TRUE, trackInfo=list( stranded=TRUE, normTo=1e+8, #urlBase="http://epigenomics.fleming.gr/home/tzanos/public_html/DK/HuR/metaseqR2_DK_pandora_tracks/tracks", hubInfo=list( name="EDHub", shortLabel="ED Hub", longLabel="ED hub long", email="reczko@fleming.gr" ) ) )