N E X T F L O W ~ version 24.10.4 Launching `https://github.com/nf-core/riboseq` [riboseq_15] DSL2 - revision: 235e008eda [1.1.0] ------------------------------------------------------ ,--./,-. ___ __ __ __ ___ /,-._.--~' |\ | |__ __ / ` / \ |__) |__ } { | \| | \__, \__/ | \ |___ \`-._,-`-, `._,._,' nf-core/riboseq 1.1.0 ------------------------------------------------------ Input/output options input : samplesheet.csv contrasts : contrasts.csv outdir : riboseq Reference genome options fasta : /data/user/mbansal/Mapping_and_fastq_files/MAPPING_MINIMAP2/reference_genome_files/GRCh38.p13.genome.fa gtf : /data/user/mbansal/Mapping_and_fastq_files/MAPPING_MINIMAP2/reference_genome_files/gencode.v44.annotation.gtf Alignment options min_mapped_reads : 5 Riboseq-specific options extra_ribotricer_detectorfs_args: --phase_score_cutoff 0.2 extra_anota2seq_run_args : --nPermutations 1000 --useRVM TRUE Generic options trace_report_suffix : 2025-06-05_10-28-22 Core Nextflow options revision : 1.1.0 runName : riboseq_15 containerEngine : singularity launchDir : /data/project/ding_lab/riboseq workDir : /data/project/ding_lab/riboseq/work projectDir : /home/mbansal/.nextflow/assets/nf-core/riboseq userName : mbansal profile : singularity configFiles : /home/mbansal/.nextflow/assets/nf-core/riboseq/nextflow.config !! Only displaying parameters that differ from the pipeline defaults !! ------------------------------------------------------ * The pipeline https://doi.org/10.5281/zenodo.10966364 * The nf-core framework https://doi.org/10.1038/s41587-020-0439-x * Software dependencies https://github.com/nf-core/riboseq/blob/master/CITATIONS.md WARN: The following invalid input values have been detected: * --run_anota2seq: true WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group [- ] NFC…:PREPARE_GENOME:GTF_FILTER - [- ] NFC…OME:MAKE_TRANSCRIPTS_FASTA - [- ] NFC…:PREPARE_GENOME:GTF_FILTER - [- ] NFC…OME:MAKE_TRANSCRIPTS_FASTA - [- ] NFC…ENOME:CUSTOM_GETCHROMSIZES - [- ] NFC…ARE_GENOME:SORTMERNA_INDEX - [- ] NFC…GENOME:STAR_GENOMEGENERATE - [- ] NFC…_SETSTRANDEDNESS:CAT_FASTQ - [- ] NFC…ER_SETSTRANDEDNESS:FQ_LINT - [- ] NFC…UMITOOLS_TRIMGALORE:FASTQC - [- ] NFC…OOLS_TRIMGALORE:TRIMGALORE - WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` [- ] NFC…:PREPARE_GENOME:GTF_FILTER - [- ] NFC…OME:MAKE_TRANSCRIPTS_FASTA - [- ] NFC…ENOME:CUSTOM_GETCHROMSIZES - [- ] NFC…ARE_GENOME:SORTMERNA_INDEX - [- ] NFC…GENOME:STAR_GENOMEGENERATE - [- ] NFC…_SETSTRANDEDNESS:CAT_FASTQ - [- ] NFC…ER_SETSTRANDEDNESS:FQ_LINT - [- ] NFC…UMITOOLS_TRIMGALORE:FASTQC - [- ] NFC…OOLS_TRIMGALORE:TRIMGALORE - [- ] NFC…ESS:FQ_LINT_AFTER_TRIMMING - [- ] NFC…RANDEDNESS:SORTMERNA_INDEX - [- ] NFC…_SETSTRANDEDNESS:SORTMERNA - [- ] NFC…SS:FQ_LINT_AFTER_SORTMERNA - [- ] NFC…PLE_FQ_SALMON:SALMON_INDEX - [- ] NFC…PLE_FQ_SALMON:FQ_SUBSAMPLE - [- ] NFC…PLE_FQ_SALMON:SALMON_QUANT - [- ] NFC…ASTQ_ALIGN_STAR:STAR_ALIGN - [- ] NFC…TOOLS_GENOME:SAMTOOLS_SORT - [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition [- ] NFC…:PREPARE_GENOME:GTF_FILTER - [- ] NFC…OME:MAKE_TRANSCRIPTS_FASTA - [- ] NFC…ENOME:CUSTOM_GETCHROMSIZES - [- ] NFC…ARE_GENOME:SORTMERNA_INDEX - [- ] NFC…GENOME:STAR_GENOMEGENERATE - [- ] NFC…_SETSTRANDEDNESS:CAT_FASTQ - [- ] NFC…ER_SETSTRANDEDNESS:FQ_LINT - [- ] NFC…UMITOOLS_TRIMGALORE:FASTQC - [- ] NFC…OOLS_TRIMGALORE:TRIMGALORE - [- ] NFC…ESS:FQ_LINT_AFTER_TRIMMING - [- ] NFC…RANDEDNESS:SORTMERNA_INDEX - [- ] NFC…_SETSTRANDEDNESS:SORTMERNA - [- ] NFC…SS:FQ_LINT_AFTER_SORTMERNA - [- ] NFC…PLE_FQ_SALMON:SALMON_INDEX - [- ] NFC…PLE_FQ_SALMON:FQ_SUBSAMPLE - [- ] NFC…PLE_FQ_SALMON:SALMON_QUANT - [- ] NFC…ASTQ_ALIGN_STAR:STAR_ALIGN - [- ] NFC…TOOLS_GENOME:SAMTOOLS_SORT - [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - Plus 13 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition [- ] NFC…:PREPARE_GENOME:GTF_FILTER - [- ] NFC…OME:MAKE_TRANSCRIPTS_FASTA - [- ] NFC…ENOME:CUSTOM_GETCHROMSIZES - [- ] NFC…ARE_GENOME:SORTMERNA_INDEX - [- ] NFC…GENOME:STAR_GENOMEGENERATE - [- ] NFC…_SETSTRANDEDNESS:CAT_FASTQ - [- ] NFC…ER_SETSTRANDEDNESS:FQ_LINT - [- ] NFC…UMITOOLS_TRIMGALORE:FASTQC - [- ] NFC…OOLS_TRIMGALORE:TRIMGALORE - [- ] NFC…ESS:FQ_LINT_AFTER_TRIMMING - [- ] NFC…RANDEDNESS:SORTMERNA_INDEX - [- ] NFC…_SETSTRANDEDNESS:SORTMERNA - [- ] NFC…SS:FQ_LINT_AFTER_SORTMERNA - [- ] NFC…PLE_FQ_SALMON:SALMON_INDEX - [- ] NFC…PLE_FQ_SALMON:FQ_SUBSAMPLE - [- ] NFC…PLE_FQ_SALMON:SALMON_QUANT - [- ] NFC…ASTQ_ALIGN_STAR:STAR_ALIGN - [- ] NFC…TOOLS_GENOME:SAMTOOLS_SORT - [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - Plus 20 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 [- ] NFC…OME:MAKE_TRANSCRIPTS_FASTA - [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 [- ] NFC…ARE_GENOME:SORTMERNA_INDEX - [- ] NFC…GENOME:STAR_GENOMEGENERATE - [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 [- ] NFC…ER_SETSTRANDEDNESS:FQ_LINT - [- ] NFC…UMITOOLS_TRIMGALORE:FASTQC - [- ] NFC…OOLS_TRIMGALORE:TRIMGALORE - [- ] NFC…ESS:FQ_LINT_AFTER_TRIMMING - [- ] NFC…RANDEDNESS:SORTMERNA_INDEX - [- ] NFC…_SETSTRANDEDNESS:SORTMERNA - [- ] NFC…SS:FQ_LINT_AFTER_SORTMERNA - [- ] NFC…PLE_FQ_SALMON:SALMON_INDEX - [- ] NFC…PLE_FQ_SALMON:FQ_SUBSAMPLE - [- ] NFC…PLE_FQ_SALMON:SALMON_QUANT - [- ] NFC…ASTQ_ALIGN_STAR:STAR_ALIGN - [- ] NFC…TOOLS_GENOME:SAMTOOLS_SORT - [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - Plus 21 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [- ] NFC…ARE_GENOME:SORTMERNA_INDEX - [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [- ] NFC…RANDEDNESS:SORTMERNA_INDEX - [- ] NFC…_SETSTRANDEDNESS:SORTMERNA - [- ] NFC…SS:FQ_LINT_AFTER_SORTMERNA - [- ] NFC…PLE_FQ_SALMON:SALMON_INDEX - [- ] NFC…PLE_FQ_SALMON:FQ_SUBSAMPLE - [- ] NFC…PLE_FQ_SALMON:SALMON_QUANT - [- ] NFC…ASTQ_ALIGN_STAR:STAR_ALIGN - [- ] NFC…TOOLS_GENOME:SAMTOOLS_SORT - [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ Plus 20 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [- ] NFC…_SETSTRANDEDNESS:SORTMERNA - [- ] NFC…SS:FQ_LINT_AFTER_SORTMERNA - [- ] NFC…PLE_FQ_SALMON:SALMON_INDEX - [- ] NFC…PLE_FQ_SALMON:FQ_SUBSAMPLE - [- ] NFC…PLE_FQ_SALMON:SALMON_QUANT - [- ] NFC…ASTQ_ALIGN_STAR:STAR_ALIGN - [- ] NFC…TOOLS_GENOME:SAMTOOLS_SORT - [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ Plus 20 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 [- ] NFC…OOLS_GENOME:SAMTOOLS_INDEX - [- ] NFC…TS_SAMTOOLS:SAMTOOLS_STATS - [f0/ea3800] NFC…PTOME:SAMTOOLS_SORT (WT_1) | 3 of 3, cached: 3 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [10/9cc11f] NFC…SALMON:SALMON_QUANT (WT_1) | 3 of 3, cached: 3 Plus 18 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition executor > local (2) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 0 of 1 Plus 7 more processes waiting for tasks… WARN: Found the following unidentified headers in /data/project/ding_lab/riboseq/samplesheet.csv: - sample_description - pair - treatment - sample_group WARN: Access to undefined parameter `salmon_index` -- Initialise it to a default value eg. `params.salmon_index = some_value` WARN: Access to undefined parameter `skip_pseudo_alignment` -- Initialise it to a default value eg. `params.skip_pseudo_alignment = some_value` WARN: Access to undefined parameter `pseudo_aligner` -- Initialise it to a default value eg. `params.pseudo_aligner = some_value` WARN: Access to undefined parameter `sortmerna_index` -- Initialise it to a default value eg. `params.sortmerna_index = some_value` WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition WARN: The operator `first` is useless when applied to a value channel which returns a single value by definition executor > local (3) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 0 of 1 Plus 6 more processes waiting for tasks… executor > local (3) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ Plus 6 more processes waiting for tasks… executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 0 of 1 [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 0 of 1 [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 0 of 1 [73/064188] NFC…E_TRANSCRIPT (all_samples) | 0 of 1 [f5/e60bea] NFC…_knockout, batch:, pair:]) | 0 of 1 Plus 1 more processes waiting for tasks… executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 0 of 1 [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 0 of 1 [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 0 of 1 [73/064188] NFC…E_TRANSCRIPT (all_samples) | 0 of 1 [f5/e60bea] NFC…_knockout, batch:, pair:]) | 0 of 1 Plus 1 more processes waiting for tasks… ERROR ~ Error executing process > 'NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])' Caused by: Process `NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])` terminated with an error exit status (1) Command executed [/home/mbansal/.nextflow/assets/nf-core/riboseq/./workflows/riboseq/../../modules/nf-core/anota2seq/anota2seqrun/templates/anota2seqrun.r]: #!/usr/bin/env Rscript ################################################ ################################################ ## Functions ## ################################################ ################################################ #' Check for Non-Empty, Non-Whitespace String #' #' This function checks if the input is non-NULL and contains more than just whitespace. #' It returns TRUE if the input is a non-empty, non-whitespace string, and FALSE otherwise. #' #' @param input A variable to check. #' @return A logical value: TRUE if the input is a valid, non-empty, non-whitespace string; FALSE otherwise. #' @examples #' is_valid_string("Hello World") # Returns TRUE #' is_valid_string(" ") # Returns FALSE #' is_valid_string(NULL) # Returns FALSE is_valid_string <- function(input) { !is.null(input) && nzchar(trimws(input)) } #' Parse out options from a string without recourse to optparse #' #' @param x Long-form argument list like --opt1 val1 --opt2 val2 #' #' @return named list of options and values similar to optparse parse_args <- function(x){ args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1] args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE)) # Ensure the option vectors are length 2 (key/ value) to catch empty ones args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z}) parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1])) parsed_args[! is.na(parsed_args)] } #' Flexibly read CSV or TSV files #' #' @param file Input file #' @param header Passed to read.delim() #' @param row.names Passed to read.delim() #' #' @return output Data frame read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = TRUE){ ext <- tolower(tail(strsplit(basename(file), split = "\\.")[[1]], 1)) if (ext == "tsv" || ext == "txt") { separator <- "\t" } else if (ext == "csv") { separator <- "," } else { stop(paste("Unknown separator for", ext)) } read.delim( file, sep = separator, header = header, row.names = row.names, check.names = check.names ) } #' Round numeric dataframe columns to fixed decimal places by applying #' formatting and converting back to numerics #' #' @param dataframe A data frame #' @param columns Which columns to round (assumes all of them by default) #' @param digits How many decimal places to round to? #' #' @return output Data frame round_dataframe_columns <- function(df, columns = NULL, digits = 8){ if (is.null(columns)){ columns <- colnames(df) } df[,columns] <- format( data.frame(df[, columns], check.names = FALSE), nsmall = digits ) # Convert columns back to numeric for (c in columns) { df[[c]][grep("^ *NA$", df[[c]])] <- NA df[[c]] <- as.numeric(df[[c]]) } df } ################################################ ################################################ ## PARSE PARAMETERS FROM NEXTFLOW ## ################################################ ################################################ # I've defined these in a single array like this so that we could go back to an # optparse-driven method in future with module bin/ directories, rather than # the template # Set defaults and classes opt <- list( output_prefix = ifelse('null' == 'null', 'SNORD133_vs_control', 'null'), count_file = 'salmon.merged.gene_counts_length_scaled.tsv', sample_file = 'samplesheet.csv', sample_treatment_col = 'sample_group', reference_level = 'control', target_level = 'SNORD133_knockout', sample_id_col = "sample", samples_pairing_col = NULL, samples_batch_col = NULL, subset_to_contrast_samples = FALSE, exclude_samples_col = NULL, exclude_samples_values = NULL, dataType = "RNAseq", # for anota2seqDataSetFromMatrix normalize = TRUE, # for anota2seqDataSetFromMatrix transformation = "TMM-log2", # for anota2seqDataSetFromMatrix filterZeroGenes = TRUE, # for anota2seqDataSetFromMatrix varCutOff = NULL, # for anota2seqDataSetFromMatrix performQC = TRUE, # for anota2seqRun onlyGroup = FALSE, # for anota2seqRun performROT = TRUE, # for anota2seqRun generateSingleGenePlots = FALSE, # for anota2seqRun analyzeBuffering = TRUE, # for anota2seqRun analyzemRNA = TRUE, # for anota2seqRun useRVM = TRUE, # for anota2seqRun correctionMethod = "BH", # for anota2seqRun useProgBar = FALSE, # for anota2seqRun getRVM = TRUE, # for anota2seqGetOutput output = 'full' # for anota2seqGetOutput ) opt_types <- lapply(opt, class) # Apply parameter overrides args_opt <- parse_args('--subset_to_contrast_samples TRUE') for ( ao in names(args_opt)){ if (! ao %in% names(opt)){ stop(paste("Invalid option:", ao)) }else{ # Preserve classes from defaults where possible if (! is.null(opt[[ao]])){ args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]]) } opt[[ao]] <- args_opt[[ao]] } } # Check if required parameters have been provided required_opts <- c('sample_treatment_col', 'reference_level', 'target_level', 'output_prefix') missing <- required_opts[!unlist(lapply(opt[required_opts], is_valid_string)) | !required_opts %in% names(opt)] if (length(missing) > 0){ stop(paste("Missing required options:", paste(missing, collapse=', '))) } # Check file inputs are valid for (file_input in c('count_file', 'sample_file')){ if (! is_valid_string(opt[[file_input]])) { stop(paste("Please provide", file_input), call. = FALSE) } if (! file.exists(opt[[file_input]])){ stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file')) } } ################################################ ################################################ ## Finish loading libraries ## ################################################ ################################################ library(anota2seq) ################################################ ################################################ ## READ IN COUNTS FILE AND SAMPLE METADATA ## ################################################ ################################################ count.table <- read_delim_flexible( file = opt$count_file, header = TRUE, row.names = opt$gene_id_col, check.names = FALSE ) sample.sheet <- read_delim_flexible(file = opt$sample_file) # Deal with spaces that may be in sample column opt$sample_id_col <- make.names(opt$sample_id_col) if (! opt$sample_id_col %in% colnames(sample.sheet)){ stop(paste0("Specified sample ID column '", opt$sample_id_col, "' is not in the sample sheet")) } # Sample sheet can have duplicate rows for multiple sequencing runs, so uniqify # before assigning row names sample.sheet <- sample.sheet[! duplicated(sample.sheet[[opt$sample_id_col]]), ] rownames(sample.sheet) <- sample.sheet[[opt$sample_id_col]] # Check that all samples specified in the input sheet are present in the counts # table. Assuming they are, subset and sort the count table to match the sample # sheet missing_samples <- sample.sheet[!rownames(sample.sheet) %in% colnames(count.table), opt$sample_id_col] if (length(missing_samples) > 0) { stop(paste( length(missing_samples), 'specified samples missing from count table:', paste(missing_samples, collapse = ',') )) } else{ # Save any non-count data, will gene metadata etc we might need later noncount.table <- count.table[, !colnames(count.table) %in% rownames(sample.sheet), drop = FALSE] count.table <- count.table[, rownames(sample.sheet)] } ################################################ ################################################ ## CHECK CONTRAST SPECIFICATION ## ################################################ ################################################ sample_treatment_col <- make.names(opt$sample_treatment_col) if (!sample_treatment_col %in% colnames(sample.sheet)) { stop( paste0( 'Chosen contrast variable \"', sample_treatment_col, '\" not in sample sheet' ) ) } else if (any(!c(opt$reflevel, opt$treatlevel) %in% sample.sheet[[sample_treatment_col]])) { stop( paste( 'Please choose reference and treatment levels that are present in the', sample_treatment_col, 'column of the sample sheet' ) ) } # Optionally, subset to only the samples involved in the contrast if (opt$subset_to_contrast_samples){ sample_selector <- sample.sheet[[sample_treatment_col]] %in% c(opt$target_level, opt$reference_level) selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } # Optionally, remove samples with specified values in a given field (probably # don't use this as well as the above) if ((is_valid_string(opt$exclude_samples_col)) && (is_valid_string(opt$exclude_samples_values))){ exclude_values = unlist(strsplit(opt$exclude_samples_values, split = ';')) if (! opt$exclude_samples_col %in% colnames(sample.sheet)){ stop(paste(opt$exclude_samples_col, ' specified to subset samples is not a valid sample sheet column')) } print(paste0('Excluding samples with values of ', opt$exclude_samples_values, ' in ', opt$exclude_samples_col)) sample_selector <- ! sample.sheet[[opt$exclude_samples_col]] %in% exclude_values selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } ################################################ ################################################ ## Run anota2seqRun() ## ################################################ ################################################ # Separate matrix into riboseq and rnaseq data riboseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'riboseq'] rnaseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'rnaseq'] if (! is.null(opt$samples_pairing_col)){ riboseq_samples <- riboseq_samples[order(sample.sheet[riboseq_samples, opt$samples_pairing_col])] rnaseq_samples <- rnaseq_samples[order(sample.sheet[rnaseq_samples, opt$samples_pairing_col])] } riboseq_data <- count.table[,riboseq_samples] rnaseq_data <- count.table[,rnaseq_samples] # Make the anota2seqDataSet anota2seqDataSetFromMatrix_args <- list( dataP = riboseq_data, dataT = rnaseq_data, phenoVec = sample.sheet[rnaseq_samples, opt$sample_treatment_col], dataType = opt$dataType, normalize = opt$normalize, transformation = opt$transformation, filterZeroGenes = opt$filterZeroGenes, varCutOff = opt$varCutOff ) if (! is.null(opt$samples_batch_col)){ anota2seqDataSetFromMatrix_args$batchVec <- samples[rnaseq_samples, opt$samples_batch_col] } ads <- do.call(anota2seqDataSetFromMatrix, anota2seqDataSetFromMatrix_args) # Run anota2seqRun contrast_matrix <- matrix( c(-1, 1), nrow = 2, dimnames = list(sort(c(opt$reference_level, opt$target_level)), NULL) ) ads <- anota2seqRun( ads, contrasts = contrast_matrix, performQC = opt$performQC, onlyGroup = opt$onlyGroup, performROT = opt$performROT, generateSingleGenePlots = opt$generateSingleGenePlots, analyzeBuffering = opt$analyzeBuffering, analyzemRNA = opt$analyzemRNA, useRVM = opt$useRVM, correctionMethod = opt$correctionMethod, useProgBar = FALSE ) ################################################ ################################################ ## Generate outputs ## ################################################ ################################################ contrast.name <- paste(opt$target_level, opt$reference_level, sep = "_vs_") cat("Saving results for ", contrast.name, " ... ", sep = "") for (analysis in c("translated mRNA", "total mRNA", "translation", "buffering", "mRNA abundance")){ output <- anota2seqGetOutput( ads, analysis = analysis, output = opt$output, getRVM = opt$getRVM, selContrast = 1 ) write.table( output, file = paste(opt$output_prefix, sub(' ', '_', analysis), 'anota2seq.results.tsv', sep = '.'), col.names = TRUE, row.names = FALSE, sep = ' ', quote = FALSE ) } # Fold change plot png( file = paste(opt$output_prefix, 'fold_change.png', sep = '.'), width = 720, height = 720 ) anota2seqPlotFC( ads, visualizeRegModes = "all", selContrast = 1, contrastName = contrast.name, plotToFile = FALSE ) dev.off() # R object for other processes to use saveRDS(ads, file = paste(opt$output_prefix, 'Anota2seqDataSet.rds', sep = '.')) # Rename files files output by anota2seqPerformQC() sapply(list.files(pattern = "^ANOTA2SEQ_"), function(f) file.rename(f, sub("^ANOTA2SEQ_", paste0(opt$output_prefix, '.'), f))) ################################################ ################################################ ## R SESSION INFO ## ################################################ ################################################ sink(paste(opt$output_prefix, "R_sessionInfo.log", sep = '.')) print(sessionInfo()) sink() ################################################ ################################################ ## VERSIONS FILE ## ################################################ ################################################ r.version <- strsplit(version[['version.string']], ' ')[[1]][3] anota2seq.version <- as.character(packageVersion('anota2seq')) writeLines( c( '"NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN":', paste(' bioconductor-anota2seq:', anota2seq.version) ), 'versions.yml') ################################################ ################################################ ################################################ ################################################ Command exit status: 1 Command output: (empty) Command error: Error in anota2seqCheckInput(dataP, dataT, phenoVec, batchVec, NULL, "BH", : Number of columns for dataT and dataP must be identical. Calls: do.call -> -> anota2seqCheckInput Execution halted Work dir: /data/project/ding_lab/riboseq/work/f5/e60beabdc92c21be951212d1085ca4 Container: /data/project/ding_lab/riboseq/work/singularity/depot.galaxyproject.org-singularity-bioconductor-anota2seq-1.24.0--r43hdfd78af_0.img Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named `.command.sh` -- Check '.nextflow.log' file for details executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 0 of 1 [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 0 of 1 [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 0 of 1 [73/064188] NFC…E_TRANSCRIPT (all_samples) | 0 of 1 [f5/e60bea] NFC…_knockout, batch:, pair:]) | 1 of 1, failed: 1 ✘ Plus 1 more processes waiting for tasks… Execution cancelled -- Finishing pending tasks before exit ERROR ~ Error executing process > 'NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])' Caused by: Process `NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])` terminated with an error exit status (1) Command executed [/home/mbansal/.nextflow/assets/nf-core/riboseq/./workflows/riboseq/../../modules/nf-core/anota2seq/anota2seqrun/templates/anota2seqrun.r]: #!/usr/bin/env Rscript ################################################ ################################################ ## Functions ## ################################################ ################################################ #' Check for Non-Empty, Non-Whitespace String #' #' This function checks if the input is non-NULL and contains more than just whitespace. #' It returns TRUE if the input is a non-empty, non-whitespace string, and FALSE otherwise. #' #' @param input A variable to check. #' @return A logical value: TRUE if the input is a valid, non-empty, non-whitespace string; FALSE otherwise. #' @examples #' is_valid_string("Hello World") # Returns TRUE #' is_valid_string(" ") # Returns FALSE #' is_valid_string(NULL) # Returns FALSE is_valid_string <- function(input) { !is.null(input) && nzchar(trimws(input)) } #' Parse out options from a string without recourse to optparse #' #' @param x Long-form argument list like --opt1 val1 --opt2 val2 #' #' @return named list of options and values similar to optparse parse_args <- function(x){ args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1] args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE)) # Ensure the option vectors are length 2 (key/ value) to catch empty ones args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z}) parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1])) parsed_args[! is.na(parsed_args)] } #' Flexibly read CSV or TSV files #' #' @param file Input file #' @param header Passed to read.delim() #' @param row.names Passed to read.delim() #' #' @return output Data frame read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = TRUE){ ext <- tolower(tail(strsplit(basename(file), split = "\\.")[[1]], 1)) if (ext == "tsv" || ext == "txt") { separator <- "\t" } else if (ext == "csv") { separator <- "," } else { stop(paste("Unknown separator for", ext)) } read.delim( file, sep = separator, header = header, row.names = row.names, check.names = check.names ) } #' Round numeric dataframe columns to fixed decimal places by applying #' formatting and converting back to numerics #' #' @param dataframe A data frame #' @param columns Which columns to round (assumes all of them by default) #' @param digits How many decimal places to round to? #' #' @return output Data frame round_dataframe_columns <- function(df, columns = NULL, digits = 8){ if (is.null(columns)){ columns <- colnames(df) } df[,columns] <- format( data.frame(df[, columns], check.names = FALSE), nsmall = digits ) # Convert columns back to numeric for (c in columns) { df[[c]][grep("^ *NA$", df[[c]])] <- NA df[[c]] <- as.numeric(df[[c]]) } df } ################################################ ################################################ ## PARSE PARAMETERS FROM NEXTFLOW ## ################################################ ################################################ # I've defined these in a single array like this so that we could go back to an # optparse-driven method in future with module bin/ directories, rather than # the template # Set defaults and classes opt <- list( output_prefix = ifelse('null' == 'null', 'SNORD133_vs_control', 'null'), count_file = 'salmon.merged.gene_counts_length_scaled.tsv', sample_file = 'samplesheet.csv', sample_treatment_col = 'sample_group', reference_level = 'control', target_level = 'SNORD133_knockout', sample_id_col = "sample", samples_pairing_col = NULL, samples_batch_col = NULL, subset_to_contrast_samples = FALSE, exclude_samples_col = NULL, exclude_samples_values = NULL, dataType = "RNAseq", # for anota2seqDataSetFromMatrix normalize = TRUE, # for anota2seqDataSetFromMatrix transformation = "TMM-log2", # for anota2seqDataSetFromMatrix filterZeroGenes = TRUE, # for anota2seqDataSetFromMatrix varCutOff = NULL, # for anota2seqDataSetFromMatrix performQC = TRUE, # for anota2seqRun onlyGroup = FALSE, # for anota2seqRun performROT = TRUE, # for anota2seqRun generateSingleGenePlots = FALSE, # for anota2seqRun analyzeBuffering = TRUE, # for anota2seqRun analyzemRNA = TRUE, # for anota2seqRun useRVM = TRUE, # for anota2seqRun correctionMethod = "BH", # for anota2seqRun useProgBar = FALSE, # for anota2seqRun getRVM = TRUE, # for anota2seqGetOutput output = 'full' # for anota2seqGetOutput ) opt_types <- lapply(opt, class) # Apply parameter overrides args_opt <- parse_args('--subset_to_contrast_samples TRUE') for ( ao in names(args_opt)){ if (! ao %in% names(opt)){ stop(paste("Invalid option:", ao)) }else{ # Preserve classes from defaults where possible if (! is.null(opt[[ao]])){ args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]]) } opt[[ao]] <- args_opt[[ao]] } } # Check if required parameters have been provided required_opts <- c('sample_treatment_col', 'reference_level', 'target_level', 'output_prefix') missing <- required_opts[!unlist(lapply(opt[required_opts], is_valid_string)) | !required_opts %in% names(opt)] if (length(missing) > 0){ stop(paste("Missing required options:", paste(missing, collapse=', '))) } # Check file inputs are valid for (file_input in c('count_file', 'sample_file')){ if (! is_valid_string(opt[[file_input]])) { stop(paste("Please provide", file_input), call. = FALSE) } if (! file.exists(opt[[file_input]])){ stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file')) } } ################################################ ################################################ ## Finish loading libraries ## ################################################ ################################################ library(anota2seq) ################################################ ################################################ ## READ IN COUNTS FILE AND SAMPLE METADATA ## ################################################ ################################################ count.table <- read_delim_flexible( file = opt$count_file, header = TRUE, row.names = opt$gene_id_col, check.names = FALSE ) sample.sheet <- read_delim_flexible(file = opt$sample_file) # Deal with spaces that may be in sample column opt$sample_id_col <- make.names(opt$sample_id_col) if (! opt$sample_id_col %in% colnames(sample.sheet)){ stop(paste0("Specified sample ID column '", opt$sample_id_col, "' is not in the sample sheet")) } # Sample sheet can have duplicate rows for multiple sequencing runs, so uniqify # before assigning row names sample.sheet <- sample.sheet[! duplicated(sample.sheet[[opt$sample_id_col]]), ] rownames(sample.sheet) <- sample.sheet[[opt$sample_id_col]] # Check that all samples specified in the input sheet are present in the counts # table. Assuming they are, subset and sort the count table to match the sample # sheet missing_samples <- sample.sheet[!rownames(sample.sheet) %in% colnames(count.table), opt$sample_id_col] if (length(missing_samples) > 0) { stop(paste( length(missing_samples), 'specified samples missing from count table:', paste(missing_samples, collapse = ',') )) } else{ # Save any non-count data, will gene metadata etc we might need later noncount.table <- count.table[, !colnames(count.table) %in% rownames(sample.sheet), drop = FALSE] count.table <- count.table[, rownames(sample.sheet)] } ################################################ ################################################ ## CHECK CONTRAST SPECIFICATION ## ################################################ ################################################ sample_treatment_col <- make.names(opt$sample_treatment_col) if (!sample_treatment_col %in% colnames(sample.sheet)) { stop( paste0( 'Chosen contrast variable \"', sample_treatment_col, '\" not in sample sheet' ) ) } else if (any(!c(opt$reflevel, opt$treatlevel) %in% sample.sheet[[sample_treatment_col]])) { stop( paste( 'Please choose reference and treatment levels that are present in the', sample_treatment_col, 'column of the sample sheet' ) ) } # Optionally, subset to only the samples involved in the contrast if (opt$subset_to_contrast_samples){ sample_selector <- sample.sheet[[sample_treatment_col]] %in% c(opt$target_level, opt$reference_level) selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } # Optionally, remove samples with specified values in a given field (probably # don't use this as well as the above) if ((is_valid_string(opt$exclude_samples_col)) && (is_valid_string(opt$exclude_samples_values))){ exclude_values = unlist(strsplit(opt$exclude_samples_values, split = ';')) if (! opt$exclude_samples_col %in% colnames(sample.sheet)){ stop(paste(opt$exclude_samples_col, ' specified to subset samples is not a valid sample sheet column')) } print(paste0('Excluding samples with values of ', opt$exclude_samples_values, ' in ', opt$exclude_samples_col)) sample_selector <- ! sample.sheet[[opt$exclude_samples_col]] %in% exclude_values selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } ################################################ ################################################ ## Run anota2seqRun() ## ################################################ ################################################ # Separate matrix into riboseq and rnaseq data riboseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'riboseq'] rnaseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'rnaseq'] if (! is.null(opt$samples_pairing_col)){ riboseq_samples <- riboseq_samples[order(sample.sheet[riboseq_samples, opt$samples_pairing_col])] rnaseq_samples <- rnaseq_samples[order(sample.sheet[rnaseq_samples, opt$samples_pairing_col])] } riboseq_data <- count.table[,riboseq_samples] rnaseq_data <- count.table[,rnaseq_samples] # Make the anota2seqDataSet anota2seqDataSetFromMatrix_args <- list( dataP = riboseq_data, dataT = rnaseq_data, phenoVec = sample.sheet[rnaseq_samples, opt$sample_treatment_col], dataType = opt$dataType, normalize = opt$normalize, transformation = opt$transformation, filterZeroGenes = opt$filterZeroGenes, varCutOff = opt$varCutOff ) if (! is.null(opt$samples_batch_col)){ anota2seqDataSetFromMatrix_args$batchVec <- samples[rnaseq_samples, opt$samples_batch_col] } ads <- do.call(anota2seqDataSetFromMatrix, anota2seqDataSetFromMatrix_args) # Run anota2seqRun contrast_matrix <- matrix( c(-1, 1), nrow = 2, dimnames = list(sort(c(opt$reference_level, opt$target_level)), NULL) ) ads <- anota2seqRun( ads, contrasts = contrast_matrix, performQC = opt$performQC, onlyGroup = opt$onlyGroup, performROT = opt$performROT, generateSingleGenePlots = opt$generateSingleGenePlots, analyzeBuffering = opt$analyzeBuffering, analyzemRNA = opt$analyzemRNA, useRVM = opt$useRVM, correctionMethod = opt$correctionMethod, useProgBar = FALSE ) ################################################ ################################################ ## Generate outputs ## ################################################ ################################################ contrast.name <- paste(opt$target_level, opt$reference_level, sep = "_vs_") cat("Saving results for ", contrast.name, " ... ", sep = "") for (analysis in c("translated mRNA", "total mRNA", "translation", "buffering", "mRNA abundance")){ output <- anota2seqGetOutput( ads, analysis = analysis, output = opt$output, getRVM = opt$getRVM, selContrast = 1 ) write.table( output, file = paste(opt$output_prefix, sub(' ', '_', analysis), 'anota2seq.results.tsv', sep = '.'), col.names = TRUE, row.names = FALSE, sep = ' ', quote = FALSE ) } # Fold change plot png( file = paste(opt$output_prefix, 'fold_change.png', sep = '.'), width = 720, height = 720 ) anota2seqPlotFC( ads, visualizeRegModes = "all", selContrast = 1, contrastName = contrast.name, plotToFile = FALSE ) dev.off() # R object for other processes to use saveRDS(ads, file = paste(opt$output_prefix, 'Anota2seqDataSet.rds', sep = '.')) # Rename files files output by anota2seqPerformQC() sapply(list.files(pattern = "^ANOTA2SEQ_"), function(f) file.rename(f, sub("^ANOTA2SEQ_", paste0(opt$output_prefix, '.'), f))) ################################################ ################################################ ## R SESSION INFO ## ################################################ ################################################ sink(paste(opt$output_prefix, "R_sessionInfo.log", sep = '.')) print(sessionInfo()) sink() ################################################ ################################################ ## VERSIONS FILE ## ################################################ ################################################ r.version <- strsplit(version[['version.string']], ' ')[[1]][3] anota2seq.version <- as.character(packageVersion('anota2seq')) writeLines( c( '"NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN":', paste(' bioconductor-anota2seq:', anota2seq.version) ), 'versions.yml') ################################################ ################################################ ################################################ ################################################ Command exit status: 1 Command output: (empty) Command error: Error in anota2seqCheckInput(dataP, dataT, phenoVec, batchVec, NULL, "BH", : Number of columns for dataT and dataP must be identical. Calls: do.call -> -> anota2seqCheckInput Execution halted Work dir: /data/project/ding_lab/riboseq/work/f5/e60beabdc92c21be951212d1085ca4 Container: /data/project/ding_lab/riboseq/work/singularity/depot.galaxyproject.org-singularity-bioconductor-anota2seq-1.24.0--r43hdfd78af_0.img Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named `.command.sh` -- Check '.nextflow.log' file for details ERROR ~ Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting -- Check '.nextflow.log' file for details executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 1 of 1 ✔ [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 0 of 1 [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 0 of 1 [73/064188] NFC…E_TRANSCRIPT (all_samples) | 0 of 1 [f5/e60bea] NFC…_knockout, batch:, pair:]) | 1 of 1, failed: 1 ✘ Plus 1 more processes waiting for tasks… Execution cancelled -- Finishing pending tasks before exit ERROR ~ Error executing process > 'NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])' Caused by: Process `NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])` terminated with an error exit status (1) Command executed [/home/mbansal/.nextflow/assets/nf-core/riboseq/./workflows/riboseq/../../modules/nf-core/anota2seq/anota2seqrun/templates/anota2seqrun.r]: #!/usr/bin/env Rscript ################################################ ################################################ ## Functions ## ################################################ ################################################ #' Check for Non-Empty, Non-Whitespace String #' #' This function checks if the input is non-NULL and contains more than just whitespace. #' It returns TRUE if the input is a non-empty, non-whitespace string, and FALSE otherwise. #' #' @param input A variable to check. #' @return A logical value: TRUE if the input is a valid, non-empty, non-whitespace string; FALSE otherwise. #' @examples #' is_valid_string("Hello World") # Returns TRUE #' is_valid_string(" ") # Returns FALSE #' is_valid_string(NULL) # Returns FALSE is_valid_string <- function(input) { !is.null(input) && nzchar(trimws(input)) } #' Parse out options from a string without recourse to optparse #' #' @param x Long-form argument list like --opt1 val1 --opt2 val2 #' #' @return named list of options and values similar to optparse parse_args <- function(x){ args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1] args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE)) # Ensure the option vectors are length 2 (key/ value) to catch empty ones args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z}) parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1])) parsed_args[! is.na(parsed_args)] } #' Flexibly read CSV or TSV files #' #' @param file Input file #' @param header Passed to read.delim() #' @param row.names Passed to read.delim() #' #' @return output Data frame read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = TRUE){ ext <- tolower(tail(strsplit(basename(file), split = "\\.")[[1]], 1)) if (ext == "tsv" || ext == "txt") { separator <- "\t" } else if (ext == "csv") { separator <- "," } else { stop(paste("Unknown separator for", ext)) } read.delim( file, sep = separator, header = header, row.names = row.names, check.names = check.names ) } #' Round numeric dataframe columns to fixed decimal places by applying #' formatting and converting back to numerics #' #' @param dataframe A data frame #' @param columns Which columns to round (assumes all of them by default) #' @param digits How many decimal places to round to? #' #' @return output Data frame round_dataframe_columns <- function(df, columns = NULL, digits = 8){ if (is.null(columns)){ columns <- colnames(df) } df[,columns] <- format( data.frame(df[, columns], check.names = FALSE), nsmall = digits ) # Convert columns back to numeric for (c in columns) { df[[c]][grep("^ *NA$", df[[c]])] <- NA df[[c]] <- as.numeric(df[[c]]) } df } ################################################ ################################################ ## PARSE PARAMETERS FROM NEXTFLOW ## ################################################ ################################################ # I've defined these in a single array like this so that we could go back to an # optparse-driven method in future with module bin/ directories, rather than # the template # Set defaults and classes opt <- list( output_prefix = ifelse('null' == 'null', 'SNORD133_vs_control', 'null'), count_file = 'salmon.merged.gene_counts_length_scaled.tsv', sample_file = 'samplesheet.csv', sample_treatment_col = 'sample_group', reference_level = 'control', target_level = 'SNORD133_knockout', sample_id_col = "sample", samples_pairing_col = NULL, samples_batch_col = NULL, subset_to_contrast_samples = FALSE, exclude_samples_col = NULL, exclude_samples_values = NULL, dataType = "RNAseq", # for anota2seqDataSetFromMatrix normalize = TRUE, # for anota2seqDataSetFromMatrix transformation = "TMM-log2", # for anota2seqDataSetFromMatrix filterZeroGenes = TRUE, # for anota2seqDataSetFromMatrix varCutOff = NULL, # for anota2seqDataSetFromMatrix performQC = TRUE, # for anota2seqRun onlyGroup = FALSE, # for anota2seqRun performROT = TRUE, # for anota2seqRun generateSingleGenePlots = FALSE, # for anota2seqRun analyzeBuffering = TRUE, # for anota2seqRun analyzemRNA = TRUE, # for anota2seqRun useRVM = TRUE, # for anota2seqRun correctionMethod = "BH", # for anota2seqRun useProgBar = FALSE, # for anota2seqRun getRVM = TRUE, # for anota2seqGetOutput output = 'full' # for anota2seqGetOutput ) opt_types <- lapply(opt, class) # Apply parameter overrides args_opt <- parse_args('--subset_to_contrast_samples TRUE') for ( ao in names(args_opt)){ if (! ao %in% names(opt)){ stop(paste("Invalid option:", ao)) }else{ # Preserve classes from defaults where possible if (! is.null(opt[[ao]])){ args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]]) } opt[[ao]] <- args_opt[[ao]] } } # Check if required parameters have been provided required_opts <- c('sample_treatment_col', 'reference_level', 'target_level', 'output_prefix') missing <- required_opts[!unlist(lapply(opt[required_opts], is_valid_string)) | !required_opts %in% names(opt)] if (length(missing) > 0){ stop(paste("Missing required options:", paste(missing, collapse=', '))) } # Check file inputs are valid for (file_input in c('count_file', 'sample_file')){ if (! is_valid_string(opt[[file_input]])) { stop(paste("Please provide", file_input), call. = FALSE) } if (! file.exists(opt[[file_input]])){ stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file')) } } ################################################ ################################################ ## Finish loading libraries ## ################################################ ################################################ library(anota2seq) ################################################ ################################################ ## READ IN COUNTS FILE AND SAMPLE METADATA ## ################################################ ################################################ count.table <- read_delim_flexible( file = opt$count_file, header = TRUE, row.names = opt$gene_id_col, check.names = FALSE ) sample.sheet <- read_delim_flexible(file = opt$sample_file) # Deal with spaces that may be in sample column opt$sample_id_col <- make.names(opt$sample_id_col) if (! opt$sample_id_col %in% colnames(sample.sheet)){ stop(paste0("Specified sample ID column '", opt$sample_id_col, "' is not in the sample sheet")) } # Sample sheet can have duplicate rows for multiple sequencing runs, so uniqify # before assigning row names sample.sheet <- sample.sheet[! duplicated(sample.sheet[[opt$sample_id_col]]), ] rownames(sample.sheet) <- sample.sheet[[opt$sample_id_col]] # Check that all samples specified in the input sheet are present in the counts # table. Assuming they are, subset and sort the count table to match the sample # sheet missing_samples <- sample.sheet[!rownames(sample.sheet) %in% colnames(count.table), opt$sample_id_col] if (length(missing_samples) > 0) { stop(paste( length(missing_samples), 'specified samples missing from count table:', paste(missing_samples, collapse = ',') )) } else{ # Save any non-count data, will gene metadata etc we might need later noncount.table <- count.table[, !colnames(count.table) %in% rownames(sample.sheet), drop = FALSE] count.table <- count.table[, rownames(sample.sheet)] } ################################################ ################################################ ## CHECK CONTRAST SPECIFICATION ## ################################################ ################################################ sample_treatment_col <- make.names(opt$sample_treatment_col) if (!sample_treatment_col %in% colnames(sample.sheet)) { stop( paste0( 'Chosen contrast variable \"', sample_treatment_col, '\" not in sample sheet' ) ) } else if (any(!c(opt$reflevel, opt$treatlevel) %in% sample.sheet[[sample_treatment_col]])) { stop( paste( 'Please choose reference and treatment levels that are present in the', sample_treatment_col, 'column of the sample sheet' ) ) } # Optionally, subset to only the samples involved in the contrast if (opt$subset_to_contrast_samples){ sample_selector <- sample.sheet[[sample_treatment_col]] %in% c(opt$target_level, opt$reference_level) selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } # Optionally, remove samples with specified values in a given field (probably # don't use this as well as the above) if ((is_valid_string(opt$exclude_samples_col)) && (is_valid_string(opt$exclude_samples_values))){ exclude_values = unlist(strsplit(opt$exclude_samples_values, split = ';')) if (! opt$exclude_samples_col %in% colnames(sample.sheet)){ stop(paste(opt$exclude_samples_col, ' specified to subset samples is not a valid sample sheet column')) } print(paste0('Excluding samples with values of ', opt$exclude_samples_values, ' in ', opt$exclude_samples_col)) sample_selector <- ! sample.sheet[[opt$exclude_samples_col]] %in% exclude_values selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } ################################################ ################################################ ## Run anota2seqRun() ## ################################################ ################################################ # Separate matrix into riboseq and rnaseq data riboseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'riboseq'] rnaseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'rnaseq'] if (! is.null(opt$samples_pairing_col)){ riboseq_samples <- riboseq_samples[order(sample.sheet[riboseq_samples, opt$samples_pairing_col])] rnaseq_samples <- rnaseq_samples[order(sample.sheet[rnaseq_samples, opt$samples_pairing_col])] } riboseq_data <- count.table[,riboseq_samples] rnaseq_data <- count.table[,rnaseq_samples] # Make the anota2seqDataSet anota2seqDataSetFromMatrix_args <- list( dataP = riboseq_data, dataT = rnaseq_data, phenoVec = sample.sheet[rnaseq_samples, opt$sample_treatment_col], dataType = opt$dataType, normalize = opt$normalize, transformation = opt$transformation, filterZeroGenes = opt$filterZeroGenes, varCutOff = opt$varCutOff ) if (! is.null(opt$samples_batch_col)){ anota2seqDataSetFromMatrix_args$batchVec <- samples[rnaseq_samples, opt$samples_batch_col] } ads <- do.call(anota2seqDataSetFromMatrix, anota2seqDataSetFromMatrix_args) # Run anota2seqRun contrast_matrix <- matrix( c(-1, 1), nrow = 2, dimnames = list(sort(c(opt$reference_level, opt$target_level)), NULL) ) ads <- anota2seqRun( ads, contrasts = contrast_matrix, performQC = opt$performQC, onlyGroup = opt$onlyGroup, performROT = opt$performROT, generateSingleGenePlots = opt$generateSingleGenePlots, analyzeBuffering = opt$analyzeBuffering, analyzemRNA = opt$analyzemRNA, useRVM = opt$useRVM, correctionMethod = opt$correctionMethod, useProgBar = FALSE ) ################################################ ################################################ ## Generate outputs ## ################################################ ################################################ contrast.name <- paste(opt$target_level, opt$reference_level, sep = "_vs_") cat("Saving results for ", contrast.name, " ... ", sep = "") for (analysis in c("translated mRNA", "total mRNA", "translation", "buffering", "mRNA abundance")){ output <- anota2seqGetOutput( ads, analysis = analysis, output = opt$output, getRVM = opt$getRVM, selContrast = 1 ) write.table( output, file = paste(opt$output_prefix, sub(' ', '_', analysis), 'anota2seq.results.tsv', sep = '.'), col.names = TRUE, row.names = FALSE, sep = ' ', quote = FALSE ) } # Fold change plot png( file = paste(opt$output_prefix, 'fold_change.png', sep = '.'), width = 720, height = 720 ) anota2seqPlotFC( ads, visualizeRegModes = "all", selContrast = 1, contrastName = contrast.name, plotToFile = FALSE ) dev.off() # R object for other processes to use saveRDS(ads, file = paste(opt$output_prefix, 'Anota2seqDataSet.rds', sep = '.')) # Rename files files output by anota2seqPerformQC() sapply(list.files(pattern = "^ANOTA2SEQ_"), function(f) file.rename(f, sub("^ANOTA2SEQ_", paste0(opt$output_prefix, '.'), f))) ################################################ ################################################ ## R SESSION INFO ## ################################################ ################################################ sink(paste(opt$output_prefix, "R_sessionInfo.log", sep = '.')) print(sessionInfo()) sink() ################################################ ################################################ ## VERSIONS FILE ## ################################################ ################################################ r.version <- strsplit(version[['version.string']], ' ')[[1]][3] anota2seq.version <- as.character(packageVersion('anota2seq')) writeLines( c( '"NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN":', paste(' bioconductor-anota2seq:', anota2seq.version) ), 'versions.yml') ################################################ ################################################ ################################################ ################################################ Command exit status: 1 Command output: (empty) Command error: Error in anota2seqCheckInput(dataP, dataT, phenoVec, batchVec, NULL, "BH", : Number of columns for dataT and dataP must be identical. Calls: do.call -> -> anota2seqCheckInput Execution halted Work dir: /data/project/ding_lab/riboseq/work/f5/e60beabdc92c21be951212d1085ca4 Container: /data/project/ding_lab/riboseq/work/singularity/depot.galaxyproject.org-singularity-bioconductor-anota2seq-1.24.0--r43hdfd78af_0.img Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named `.command.sh` -- Check '.nextflow.log' file for details ERROR ~ Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting -- Check '.nextflow.log' file for details executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 1 of 1 ✔ [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 1 of 1 ✔ [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 0 of 1 [73/064188] NFC…E_TRANSCRIPT (all_samples) | 0 of 1 [f5/e60bea] NFC…_knockout, batch:, pair:]) | 1 of 1, failed: 1 ✘ Plus 1 more processes waiting for tasks… Execution cancelled -- Finishing pending tasks before exit ERROR ~ Error executing process > 'NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])' Caused by: Process `NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])` terminated with an error exit status (1) Command executed [/home/mbansal/.nextflow/assets/nf-core/riboseq/./workflows/riboseq/../../modules/nf-core/anota2seq/anota2seqrun/templates/anota2seqrun.r]: #!/usr/bin/env Rscript ################################################ ################################################ ## Functions ## ################################################ ################################################ #' Check for Non-Empty, Non-Whitespace String #' #' This function checks if the input is non-NULL and contains more than just whitespace. #' It returns TRUE if the input is a non-empty, non-whitespace string, and FALSE otherwise. #' #' @param input A variable to check. #' @return A logical value: TRUE if the input is a valid, non-empty, non-whitespace string; FALSE otherwise. #' @examples #' is_valid_string("Hello World") # Returns TRUE #' is_valid_string(" ") # Returns FALSE #' is_valid_string(NULL) # Returns FALSE is_valid_string <- function(input) { !is.null(input) && nzchar(trimws(input)) } #' Parse out options from a string without recourse to optparse #' #' @param x Long-form argument list like --opt1 val1 --opt2 val2 #' #' @return named list of options and values similar to optparse parse_args <- function(x){ args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1] args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE)) # Ensure the option vectors are length 2 (key/ value) to catch empty ones args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z}) parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1])) parsed_args[! is.na(parsed_args)] } #' Flexibly read CSV or TSV files #' #' @param file Input file #' @param header Passed to read.delim() #' @param row.names Passed to read.delim() #' #' @return output Data frame read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = TRUE){ ext <- tolower(tail(strsplit(basename(file), split = "\\.")[[1]], 1)) if (ext == "tsv" || ext == "txt") { separator <- "\t" } else if (ext == "csv") { separator <- "," } else { stop(paste("Unknown separator for", ext)) } read.delim( file, sep = separator, header = header, row.names = row.names, check.names = check.names ) } #' Round numeric dataframe columns to fixed decimal places by applying #' formatting and converting back to numerics #' #' @param dataframe A data frame #' @param columns Which columns to round (assumes all of them by default) #' @param digits How many decimal places to round to? #' #' @return output Data frame round_dataframe_columns <- function(df, columns = NULL, digits = 8){ if (is.null(columns)){ columns <- colnames(df) } df[,columns] <- format( data.frame(df[, columns], check.names = FALSE), nsmall = digits ) # Convert columns back to numeric for (c in columns) { df[[c]][grep("^ *NA$", df[[c]])] <- NA df[[c]] <- as.numeric(df[[c]]) } df } ################################################ ################################################ ## PARSE PARAMETERS FROM NEXTFLOW ## ################################################ ################################################ # I've defined these in a single array like this so that we could go back to an # optparse-driven method in future with module bin/ directories, rather than # the template # Set defaults and classes opt <- list( output_prefix = ifelse('null' == 'null', 'SNORD133_vs_control', 'null'), count_file = 'salmon.merged.gene_counts_length_scaled.tsv', sample_file = 'samplesheet.csv', sample_treatment_col = 'sample_group', reference_level = 'control', target_level = 'SNORD133_knockout', sample_id_col = "sample", samples_pairing_col = NULL, samples_batch_col = NULL, subset_to_contrast_samples = FALSE, exclude_samples_col = NULL, exclude_samples_values = NULL, dataType = "RNAseq", # for anota2seqDataSetFromMatrix normalize = TRUE, # for anota2seqDataSetFromMatrix transformation = "TMM-log2", # for anota2seqDataSetFromMatrix filterZeroGenes = TRUE, # for anota2seqDataSetFromMatrix varCutOff = NULL, # for anota2seqDataSetFromMatrix performQC = TRUE, # for anota2seqRun onlyGroup = FALSE, # for anota2seqRun performROT = TRUE, # for anota2seqRun generateSingleGenePlots = FALSE, # for anota2seqRun analyzeBuffering = TRUE, # for anota2seqRun analyzemRNA = TRUE, # for anota2seqRun useRVM = TRUE, # for anota2seqRun correctionMethod = "BH", # for anota2seqRun useProgBar = FALSE, # for anota2seqRun getRVM = TRUE, # for anota2seqGetOutput output = 'full' # for anota2seqGetOutput ) opt_types <- lapply(opt, class) # Apply parameter overrides args_opt <- parse_args('--subset_to_contrast_samples TRUE') for ( ao in names(args_opt)){ if (! ao %in% names(opt)){ stop(paste("Invalid option:", ao)) }else{ # Preserve classes from defaults where possible if (! is.null(opt[[ao]])){ args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]]) } opt[[ao]] <- args_opt[[ao]] } } # Check if required parameters have been provided required_opts <- c('sample_treatment_col', 'reference_level', 'target_level', 'output_prefix') missing <- required_opts[!unlist(lapply(opt[required_opts], is_valid_string)) | !required_opts %in% names(opt)] if (length(missing) > 0){ stop(paste("Missing required options:", paste(missing, collapse=', '))) } # Check file inputs are valid for (file_input in c('count_file', 'sample_file')){ if (! is_valid_string(opt[[file_input]])) { stop(paste("Please provide", file_input), call. = FALSE) } if (! file.exists(opt[[file_input]])){ stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file')) } } ################################################ ################################################ ## Finish loading libraries ## ################################################ ################################################ library(anota2seq) ################################################ ################################################ ## READ IN COUNTS FILE AND SAMPLE METADATA ## ################################################ ################################################ count.table <- read_delim_flexible( file = opt$count_file, header = TRUE, row.names = opt$gene_id_col, check.names = FALSE ) sample.sheet <- read_delim_flexible(file = opt$sample_file) # Deal with spaces that may be in sample column opt$sample_id_col <- make.names(opt$sample_id_col) if (! opt$sample_id_col %in% colnames(sample.sheet)){ stop(paste0("Specified sample ID column '", opt$sample_id_col, "' is not in the sample sheet")) } # Sample sheet can have duplicate rows for multiple sequencing runs, so uniqify # before assigning row names sample.sheet <- sample.sheet[! duplicated(sample.sheet[[opt$sample_id_col]]), ] rownames(sample.sheet) <- sample.sheet[[opt$sample_id_col]] # Check that all samples specified in the input sheet are present in the counts # table. Assuming they are, subset and sort the count table to match the sample # sheet missing_samples <- sample.sheet[!rownames(sample.sheet) %in% colnames(count.table), opt$sample_id_col] if (length(missing_samples) > 0) { stop(paste( length(missing_samples), 'specified samples missing from count table:', paste(missing_samples, collapse = ',') )) } else{ # Save any non-count data, will gene metadata etc we might need later noncount.table <- count.table[, !colnames(count.table) %in% rownames(sample.sheet), drop = FALSE] count.table <- count.table[, rownames(sample.sheet)] } ################################################ ################################################ ## CHECK CONTRAST SPECIFICATION ## ################################################ ################################################ sample_treatment_col <- make.names(opt$sample_treatment_col) if (!sample_treatment_col %in% colnames(sample.sheet)) { stop( paste0( 'Chosen contrast variable \"', sample_treatment_col, '\" not in sample sheet' ) ) } else if (any(!c(opt$reflevel, opt$treatlevel) %in% sample.sheet[[sample_treatment_col]])) { stop( paste( 'Please choose reference and treatment levels that are present in the', sample_treatment_col, 'column of the sample sheet' ) ) } # Optionally, subset to only the samples involved in the contrast if (opt$subset_to_contrast_samples){ sample_selector <- sample.sheet[[sample_treatment_col]] %in% c(opt$target_level, opt$reference_level) selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } # Optionally, remove samples with specified values in a given field (probably # don't use this as well as the above) if ((is_valid_string(opt$exclude_samples_col)) && (is_valid_string(opt$exclude_samples_values))){ exclude_values = unlist(strsplit(opt$exclude_samples_values, split = ';')) if (! opt$exclude_samples_col %in% colnames(sample.sheet)){ stop(paste(opt$exclude_samples_col, ' specified to subset samples is not a valid sample sheet column')) } print(paste0('Excluding samples with values of ', opt$exclude_samples_values, ' in ', opt$exclude_samples_col)) sample_selector <- ! sample.sheet[[opt$exclude_samples_col]] %in% exclude_values selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } ################################################ ################################################ ## Run anota2seqRun() ## ################################################ ################################################ # Separate matrix into riboseq and rnaseq data riboseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'riboseq'] rnaseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'rnaseq'] if (! is.null(opt$samples_pairing_col)){ riboseq_samples <- riboseq_samples[order(sample.sheet[riboseq_samples, opt$samples_pairing_col])] rnaseq_samples <- rnaseq_samples[order(sample.sheet[rnaseq_samples, opt$samples_pairing_col])] } riboseq_data <- count.table[,riboseq_samples] rnaseq_data <- count.table[,rnaseq_samples] # Make the anota2seqDataSet anota2seqDataSetFromMatrix_args <- list( dataP = riboseq_data, dataT = rnaseq_data, phenoVec = sample.sheet[rnaseq_samples, opt$sample_treatment_col], dataType = opt$dataType, normalize = opt$normalize, transformation = opt$transformation, filterZeroGenes = opt$filterZeroGenes, varCutOff = opt$varCutOff ) if (! is.null(opt$samples_batch_col)){ anota2seqDataSetFromMatrix_args$batchVec <- samples[rnaseq_samples, opt$samples_batch_col] } ads <- do.call(anota2seqDataSetFromMatrix, anota2seqDataSetFromMatrix_args) # Run anota2seqRun contrast_matrix <- matrix( c(-1, 1), nrow = 2, dimnames = list(sort(c(opt$reference_level, opt$target_level)), NULL) ) ads <- anota2seqRun( ads, contrasts = contrast_matrix, performQC = opt$performQC, onlyGroup = opt$onlyGroup, performROT = opt$performROT, generateSingleGenePlots = opt$generateSingleGenePlots, analyzeBuffering = opt$analyzeBuffering, analyzemRNA = opt$analyzemRNA, useRVM = opt$useRVM, correctionMethod = opt$correctionMethod, useProgBar = FALSE ) ################################################ ################################################ ## Generate outputs ## ################################################ ################################################ contrast.name <- paste(opt$target_level, opt$reference_level, sep = "_vs_") cat("Saving results for ", contrast.name, " ... ", sep = "") for (analysis in c("translated mRNA", "total mRNA", "translation", "buffering", "mRNA abundance")){ output <- anota2seqGetOutput( ads, analysis = analysis, output = opt$output, getRVM = opt$getRVM, selContrast = 1 ) write.table( output, file = paste(opt$output_prefix, sub(' ', '_', analysis), 'anota2seq.results.tsv', sep = '.'), col.names = TRUE, row.names = FALSE, sep = ' ', quote = FALSE ) } # Fold change plot png( file = paste(opt$output_prefix, 'fold_change.png', sep = '.'), width = 720, height = 720 ) anota2seqPlotFC( ads, visualizeRegModes = "all", selContrast = 1, contrastName = contrast.name, plotToFile = FALSE ) dev.off() # R object for other processes to use saveRDS(ads, file = paste(opt$output_prefix, 'Anota2seqDataSet.rds', sep = '.')) # Rename files files output by anota2seqPerformQC() sapply(list.files(pattern = "^ANOTA2SEQ_"), function(f) file.rename(f, sub("^ANOTA2SEQ_", paste0(opt$output_prefix, '.'), f))) ################################################ ################################################ ## R SESSION INFO ## ################################################ ################################################ sink(paste(opt$output_prefix, "R_sessionInfo.log", sep = '.')) print(sessionInfo()) sink() ################################################ ################################################ ## VERSIONS FILE ## ################################################ ################################################ r.version <- strsplit(version[['version.string']], ' ')[[1]][3] anota2seq.version <- as.character(packageVersion('anota2seq')) writeLines( c( '"NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN":', paste(' bioconductor-anota2seq:', anota2seq.version) ), 'versions.yml') ################################################ ################################################ ################################################ ################################################ Command exit status: 1 Command output: (empty) Command error: Error in anota2seqCheckInput(dataP, dataT, phenoVec, batchVec, NULL, "BH", : Number of columns for dataT and dataP must be identical. Calls: do.call -> -> anota2seqCheckInput Execution halted Work dir: /data/project/ding_lab/riboseq/work/f5/e60beabdc92c21be951212d1085ca4 Container: /data/project/ding_lab/riboseq/work/singularity/depot.galaxyproject.org-singularity-bioconductor-anota2seq-1.24.0--r43hdfd78af_0.img Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named `.command.sh` -- Check '.nextflow.log' file for details ERROR ~ Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting -- Check '.nextflow.log' file for details executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 1 of 1 ✔ [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 1 of 1 ✔ [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 1 of 1 ✔ [73/064188] NFC…E_TRANSCRIPT (all_samples) | 0 of 1 [f5/e60bea] NFC…_knockout, batch:, pair:]) | 1 of 1, failed: 1 ✘ Plus 1 more processes waiting for tasks… Execution cancelled -- Finishing pending tasks before exit ERROR ~ Error executing process > 'NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])' Caused by: Process `NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])` terminated with an error exit status (1) Command executed [/home/mbansal/.nextflow/assets/nf-core/riboseq/./workflows/riboseq/../../modules/nf-core/anota2seq/anota2seqrun/templates/anota2seqrun.r]: #!/usr/bin/env Rscript ################################################ ################################################ ## Functions ## ################################################ ################################################ #' Check for Non-Empty, Non-Whitespace String #' #' This function checks if the input is non-NULL and contains more than just whitespace. #' It returns TRUE if the input is a non-empty, non-whitespace string, and FALSE otherwise. #' #' @param input A variable to check. #' @return A logical value: TRUE if the input is a valid, non-empty, non-whitespace string; FALSE otherwise. #' @examples #' is_valid_string("Hello World") # Returns TRUE #' is_valid_string(" ") # Returns FALSE #' is_valid_string(NULL) # Returns FALSE is_valid_string <- function(input) { !is.null(input) && nzchar(trimws(input)) } #' Parse out options from a string without recourse to optparse #' #' @param x Long-form argument list like --opt1 val1 --opt2 val2 #' #' @return named list of options and values similar to optparse parse_args <- function(x){ args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1] args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE)) # Ensure the option vectors are length 2 (key/ value) to catch empty ones args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z}) parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1])) parsed_args[! is.na(parsed_args)] } #' Flexibly read CSV or TSV files #' #' @param file Input file #' @param header Passed to read.delim() #' @param row.names Passed to read.delim() #' #' @return output Data frame read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = TRUE){ ext <- tolower(tail(strsplit(basename(file), split = "\\.")[[1]], 1)) if (ext == "tsv" || ext == "txt") { separator <- "\t" } else if (ext == "csv") { separator <- "," } else { stop(paste("Unknown separator for", ext)) } read.delim( file, sep = separator, header = header, row.names = row.names, check.names = check.names ) } #' Round numeric dataframe columns to fixed decimal places by applying #' formatting and converting back to numerics #' #' @param dataframe A data frame #' @param columns Which columns to round (assumes all of them by default) #' @param digits How many decimal places to round to? #' #' @return output Data frame round_dataframe_columns <- function(df, columns = NULL, digits = 8){ if (is.null(columns)){ columns <- colnames(df) } df[,columns] <- format( data.frame(df[, columns], check.names = FALSE), nsmall = digits ) # Convert columns back to numeric for (c in columns) { df[[c]][grep("^ *NA$", df[[c]])] <- NA df[[c]] <- as.numeric(df[[c]]) } df } ################################################ ################################################ ## PARSE PARAMETERS FROM NEXTFLOW ## ################################################ ################################################ # I've defined these in a single array like this so that we could go back to an # optparse-driven method in future with module bin/ directories, rather than # the template # Set defaults and classes opt <- list( output_prefix = ifelse('null' == 'null', 'SNORD133_vs_control', 'null'), count_file = 'salmon.merged.gene_counts_length_scaled.tsv', sample_file = 'samplesheet.csv', sample_treatment_col = 'sample_group', reference_level = 'control', target_level = 'SNORD133_knockout', sample_id_col = "sample", samples_pairing_col = NULL, samples_batch_col = NULL, subset_to_contrast_samples = FALSE, exclude_samples_col = NULL, exclude_samples_values = NULL, dataType = "RNAseq", # for anota2seqDataSetFromMatrix normalize = TRUE, # for anota2seqDataSetFromMatrix transformation = "TMM-log2", # for anota2seqDataSetFromMatrix filterZeroGenes = TRUE, # for anota2seqDataSetFromMatrix varCutOff = NULL, # for anota2seqDataSetFromMatrix performQC = TRUE, # for anota2seqRun onlyGroup = FALSE, # for anota2seqRun performROT = TRUE, # for anota2seqRun generateSingleGenePlots = FALSE, # for anota2seqRun analyzeBuffering = TRUE, # for anota2seqRun analyzemRNA = TRUE, # for anota2seqRun useRVM = TRUE, # for anota2seqRun correctionMethod = "BH", # for anota2seqRun useProgBar = FALSE, # for anota2seqRun getRVM = TRUE, # for anota2seqGetOutput output = 'full' # for anota2seqGetOutput ) opt_types <- lapply(opt, class) # Apply parameter overrides args_opt <- parse_args('--subset_to_contrast_samples TRUE') for ( ao in names(args_opt)){ if (! ao %in% names(opt)){ stop(paste("Invalid option:", ao)) }else{ # Preserve classes from defaults where possible if (! is.null(opt[[ao]])){ args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]]) } opt[[ao]] <- args_opt[[ao]] } } # Check if required parameters have been provided required_opts <- c('sample_treatment_col', 'reference_level', 'target_level', 'output_prefix') missing <- required_opts[!unlist(lapply(opt[required_opts], is_valid_string)) | !required_opts %in% names(opt)] if (length(missing) > 0){ stop(paste("Missing required options:", paste(missing, collapse=', '))) } # Check file inputs are valid for (file_input in c('count_file', 'sample_file')){ if (! is_valid_string(opt[[file_input]])) { stop(paste("Please provide", file_input), call. = FALSE) } if (! file.exists(opt[[file_input]])){ stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file')) } } ################################################ ################################################ ## Finish loading libraries ## ################################################ ################################################ library(anota2seq) ################################################ ################################################ ## READ IN COUNTS FILE AND SAMPLE METADATA ## ################################################ ################################################ count.table <- read_delim_flexible( file = opt$count_file, header = TRUE, row.names = opt$gene_id_col, check.names = FALSE ) sample.sheet <- read_delim_flexible(file = opt$sample_file) # Deal with spaces that may be in sample column opt$sample_id_col <- make.names(opt$sample_id_col) if (! opt$sample_id_col %in% colnames(sample.sheet)){ stop(paste0("Specified sample ID column '", opt$sample_id_col, "' is not in the sample sheet")) } # Sample sheet can have duplicate rows for multiple sequencing runs, so uniqify # before assigning row names sample.sheet <- sample.sheet[! duplicated(sample.sheet[[opt$sample_id_col]]), ] rownames(sample.sheet) <- sample.sheet[[opt$sample_id_col]] # Check that all samples specified in the input sheet are present in the counts # table. Assuming they are, subset and sort the count table to match the sample # sheet missing_samples <- sample.sheet[!rownames(sample.sheet) %in% colnames(count.table), opt$sample_id_col] if (length(missing_samples) > 0) { stop(paste( length(missing_samples), 'specified samples missing from count table:', paste(missing_samples, collapse = ',') )) } else{ # Save any non-count data, will gene metadata etc we might need later noncount.table <- count.table[, !colnames(count.table) %in% rownames(sample.sheet), drop = FALSE] count.table <- count.table[, rownames(sample.sheet)] } ################################################ ################################################ ## CHECK CONTRAST SPECIFICATION ## ################################################ ################################################ sample_treatment_col <- make.names(opt$sample_treatment_col) if (!sample_treatment_col %in% colnames(sample.sheet)) { stop( paste0( 'Chosen contrast variable \"', sample_treatment_col, '\" not in sample sheet' ) ) } else if (any(!c(opt$reflevel, opt$treatlevel) %in% sample.sheet[[sample_treatment_col]])) { stop( paste( 'Please choose reference and treatment levels that are present in the', sample_treatment_col, 'column of the sample sheet' ) ) } # Optionally, subset to only the samples involved in the contrast if (opt$subset_to_contrast_samples){ sample_selector <- sample.sheet[[sample_treatment_col]] %in% c(opt$target_level, opt$reference_level) selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } # Optionally, remove samples with specified values in a given field (probably # don't use this as well as the above) if ((is_valid_string(opt$exclude_samples_col)) && (is_valid_string(opt$exclude_samples_values))){ exclude_values = unlist(strsplit(opt$exclude_samples_values, split = ';')) if (! opt$exclude_samples_col %in% colnames(sample.sheet)){ stop(paste(opt$exclude_samples_col, ' specified to subset samples is not a valid sample sheet column')) } print(paste0('Excluding samples with values of ', opt$exclude_samples_values, ' in ', opt$exclude_samples_col)) sample_selector <- ! sample.sheet[[opt$exclude_samples_col]] %in% exclude_values selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } ################################################ ################################################ ## Run anota2seqRun() ## ################################################ ################################################ # Separate matrix into riboseq and rnaseq data riboseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'riboseq'] rnaseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'rnaseq'] if (! is.null(opt$samples_pairing_col)){ riboseq_samples <- riboseq_samples[order(sample.sheet[riboseq_samples, opt$samples_pairing_col])] rnaseq_samples <- rnaseq_samples[order(sample.sheet[rnaseq_samples, opt$samples_pairing_col])] } riboseq_data <- count.table[,riboseq_samples] rnaseq_data <- count.table[,rnaseq_samples] # Make the anota2seqDataSet anota2seqDataSetFromMatrix_args <- list( dataP = riboseq_data, dataT = rnaseq_data, phenoVec = sample.sheet[rnaseq_samples, opt$sample_treatment_col], dataType = opt$dataType, normalize = opt$normalize, transformation = opt$transformation, filterZeroGenes = opt$filterZeroGenes, varCutOff = opt$varCutOff ) if (! is.null(opt$samples_batch_col)){ anota2seqDataSetFromMatrix_args$batchVec <- samples[rnaseq_samples, opt$samples_batch_col] } ads <- do.call(anota2seqDataSetFromMatrix, anota2seqDataSetFromMatrix_args) # Run anota2seqRun contrast_matrix <- matrix( c(-1, 1), nrow = 2, dimnames = list(sort(c(opt$reference_level, opt$target_level)), NULL) ) ads <- anota2seqRun( ads, contrasts = contrast_matrix, performQC = opt$performQC, onlyGroup = opt$onlyGroup, performROT = opt$performROT, generateSingleGenePlots = opt$generateSingleGenePlots, analyzeBuffering = opt$analyzeBuffering, analyzemRNA = opt$analyzemRNA, useRVM = opt$useRVM, correctionMethod = opt$correctionMethod, useProgBar = FALSE ) ################################################ ################################################ ## Generate outputs ## ################################################ ################################################ contrast.name <- paste(opt$target_level, opt$reference_level, sep = "_vs_") cat("Saving results for ", contrast.name, " ... ", sep = "") for (analysis in c("translated mRNA", "total mRNA", "translation", "buffering", "mRNA abundance")){ output <- anota2seqGetOutput( ads, analysis = analysis, output = opt$output, getRVM = opt$getRVM, selContrast = 1 ) write.table( output, file = paste(opt$output_prefix, sub(' ', '_', analysis), 'anota2seq.results.tsv', sep = '.'), col.names = TRUE, row.names = FALSE, sep = ' ', quote = FALSE ) } # Fold change plot png( file = paste(opt$output_prefix, 'fold_change.png', sep = '.'), width = 720, height = 720 ) anota2seqPlotFC( ads, visualizeRegModes = "all", selContrast = 1, contrastName = contrast.name, plotToFile = FALSE ) dev.off() # R object for other processes to use saveRDS(ads, file = paste(opt$output_prefix, 'Anota2seqDataSet.rds', sep = '.')) # Rename files files output by anota2seqPerformQC() sapply(list.files(pattern = "^ANOTA2SEQ_"), function(f) file.rename(f, sub("^ANOTA2SEQ_", paste0(opt$output_prefix, '.'), f))) ################################################ ################################################ ## R SESSION INFO ## ################################################ ################################################ sink(paste(opt$output_prefix, "R_sessionInfo.log", sep = '.')) print(sessionInfo()) sink() ################################################ ################################################ ## VERSIONS FILE ## ################################################ ################################################ r.version <- strsplit(version[['version.string']], ' ')[[1]][3] anota2seq.version <- as.character(packageVersion('anota2seq')) writeLines( c( '"NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN":', paste(' bioconductor-anota2seq:', anota2seq.version) ), 'versions.yml') ################################################ ################################################ ################################################ ################################################ Command exit status: 1 Command output: (empty) Command error: Error in anota2seqCheckInput(dataP, dataT, phenoVec, batchVec, NULL, "BH", : Number of columns for dataT and dataP must be identical. Calls: do.call -> -> anota2seqCheckInput Execution halted Work dir: /data/project/ding_lab/riboseq/work/f5/e60beabdc92c21be951212d1085ca4 Container: /data/project/ding_lab/riboseq/work/singularity/depot.galaxyproject.org-singularity-bioconductor-anota2seq-1.24.0--r43hdfd78af_0.img Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named `.command.sh` -- Check '.nextflow.log' file for details ERROR ~ Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting -- Check '.nextflow.log' file for details executor > local (8) [cc/176183] NFC…TER (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [d0/fe276c] NFC…rsem/GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [24/2067e7] NFC…ZES (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [4f/e23c8c] NFC…ENOME:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [17/7bf8e8] NFC…ATE (GRCh38.p13.genome.fa) | 1 of 1, cached: 1 ✔ [00/40f94b] NFC…ANDEDNESS:CAT_FASTQ (WT_1) | 4 of 4, cached: 4 ✔ [90/10fd19] NFC…LINT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [92/5f03ac] NFC…S_TRIMGALORE:FASTQC (WT_2) | 4 of 4, cached: 4 ✔ [54/ec00ba] NFC…LORE (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [d4/8a9f00] NFC…LINT_AFTER_TRIMMING (WT_1) | 4 of 4, cached: 4 ✔ [17/83feaf] NFC…DNESS:SORTMERNA_INDEX ([]) | 1 of 1, cached: 1 ✔ [e3/d986e9] NFC…ANDEDNESS:SORTMERNA (WT_1) | 4 of 4, cached: 4 ✔ [09/b60b0e] NFC…ERNA (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [2c/499b77] NFC…EX (genome.transcripts.fa) | 1 of 1, cached: 1 ✔ [e1/e25e99] NFC…SALMON:FQ_SUBSAMPLE (WT_1) | 4 of 4, cached: 4 ✔ [ce/efe70b] NFC…UANT (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [8c/17d592] NFC…IGN_STAR:STAR_ALIGN (WT_2) | 4 of 4, cached: 4 ✔ [66/74aaab] NFC…ENOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [bd/df7ad7] NFC…NDEX (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [99/790404] NFC…TATS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [35/3fb05d] NFC…S:SAMTOOLS_FLAGSTAT (WT_2) | 4 of 4, cached: 4 ✔ [31/9a578d] NFC…TATS (SNORD133_knockout_2) | 4 of 4, cached: 4 ✔ [f1/921709] NFC…PTOME:SAMTOOLS_SORT (WT_2) | 4 of 4, cached: 4 ✔ [5f/dcc9a7] NFC…TOME:SAMTOOLS_INDEX (WT_2) | 4 of 4, cached: 4 ✔ [d2/8beea5] NFC…OOLS:SAMTOOLS_STATS (WT_2) | 4 of 4, cached: 4 ✔ [0a/2990a6] NFC…STAT (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [1a/0ab511] NFC…S:SAMTOOLS_IDXSTATS (WT_1) | 4 of 4, cached: 4 ✔ [eb/63f99d] NFC…OSEQ (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [27/c909ec] NFC…DUAL (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [66/0601c9] NFC…H_PREDICT_ALL (allsamples) | 0 of 1 [53/e05d31] NFC…OTRICER_PREPAREORFS (null) | 1 of 1, cached: 1 ✔ [84/7c7cdb] NFC…ORFS (SNORD133_knockout_1) | 4 of 4, cached: 4 ✔ [5b/c06f4a] NFC…SALMON:SALMON_QUANT (WT_2) | 4 of 4, cached: 4 ✔ [5c/aa85eb] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔ [2a/d7f607] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔ [a2/b86de2] NFC…LMON:SE_GENE (all_samples) | 1 of 1 ✔ [df/3ff4b8] NFC…ENGTH_SCALED (all_samples) | 1 of 1 ✔ [08/b7b3e4] NFC…_GENE_SCALED (all_samples) | 1 of 1 ✔ [73/064188] NFC…E_TRANSCRIPT (all_samples) | 1 of 1 ✔ [f5/e60bea] NFC…_knockout, batch:, pair:]) | 1 of 1, failed: 1 ✘ Plus 1 more processes waiting for tasks… ERROR ~ Error executing process > 'NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])' Caused by: Process `NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN ([id:SNORD133_vs_control, variable:sample_group, reference:control, target:SNORD133_knockout, batch:, pair:])` terminated with an error exit status (1) Command executed [/home/mbansal/.nextflow/assets/nf-core/riboseq/./workflows/riboseq/../../modules/nf-core/anota2seq/anota2seqrun/templates/anota2seqrun.r]: #!/usr/bin/env Rscript ################################################ ################################################ ## Functions ## ################################################ ################################################ #' Check for Non-Empty, Non-Whitespace String #' #' This function checks if the input is non-NULL and contains more than just whitespace. #' It returns TRUE if the input is a non-empty, non-whitespace string, and FALSE otherwise. #' #' @param input A variable to check. #' @return A logical value: TRUE if the input is a valid, non-empty, non-whitespace string; FALSE otherwise. #' @examples #' is_valid_string("Hello World") # Returns TRUE #' is_valid_string(" ") # Returns FALSE #' is_valid_string(NULL) # Returns FALSE is_valid_string <- function(input) { !is.null(input) && nzchar(trimws(input)) } #' Parse out options from a string without recourse to optparse #' #' @param x Long-form argument list like --opt1 val1 --opt2 val2 #' #' @return named list of options and values similar to optparse parse_args <- function(x){ args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1] args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE)) # Ensure the option vectors are length 2 (key/ value) to catch empty ones args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z}) parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1])) parsed_args[! is.na(parsed_args)] } #' Flexibly read CSV or TSV files #' #' @param file Input file #' @param header Passed to read.delim() #' @param row.names Passed to read.delim() #' #' @return output Data frame read_delim_flexible <- function(file, header = TRUE, row.names = NULL, check.names = TRUE){ ext <- tolower(tail(strsplit(basename(file), split = "\\.")[[1]], 1)) if (ext == "tsv" || ext == "txt") { separator <- "\t" } else if (ext == "csv") { separator <- "," } else { stop(paste("Unknown separator for", ext)) } read.delim( file, sep = separator, header = header, row.names = row.names, check.names = check.names ) } #' Round numeric dataframe columns to fixed decimal places by applying #' formatting and converting back to numerics #' #' @param dataframe A data frame #' @param columns Which columns to round (assumes all of them by default) #' @param digits How many decimal places to round to? #' #' @return output Data frame round_dataframe_columns <- function(df, columns = NULL, digits = 8){ if (is.null(columns)){ columns <- colnames(df) } df[,columns] <- format( data.frame(df[, columns], check.names = FALSE), nsmall = digits ) # Convert columns back to numeric for (c in columns) { df[[c]][grep("^ *NA$", df[[c]])] <- NA df[[c]] <- as.numeric(df[[c]]) } df } ################################################ ################################################ ## PARSE PARAMETERS FROM NEXTFLOW ## ################################################ ################################################ # I've defined these in a single array like this so that we could go back to an # optparse-driven method in future with module bin/ directories, rather than # the template # Set defaults and classes opt <- list( output_prefix = ifelse('null' == 'null', 'SNORD133_vs_control', 'null'), count_file = 'salmon.merged.gene_counts_length_scaled.tsv', sample_file = 'samplesheet.csv', sample_treatment_col = 'sample_group', reference_level = 'control', target_level = 'SNORD133_knockout', sample_id_col = "sample", samples_pairing_col = NULL, samples_batch_col = NULL, subset_to_contrast_samples = FALSE, exclude_samples_col = NULL, exclude_samples_values = NULL, dataType = "RNAseq", # for anota2seqDataSetFromMatrix normalize = TRUE, # for anota2seqDataSetFromMatrix transformation = "TMM-log2", # for anota2seqDataSetFromMatrix filterZeroGenes = TRUE, # for anota2seqDataSetFromMatrix varCutOff = NULL, # for anota2seqDataSetFromMatrix performQC = TRUE, # for anota2seqRun onlyGroup = FALSE, # for anota2seqRun performROT = TRUE, # for anota2seqRun generateSingleGenePlots = FALSE, # for anota2seqRun analyzeBuffering = TRUE, # for anota2seqRun analyzemRNA = TRUE, # for anota2seqRun useRVM = TRUE, # for anota2seqRun correctionMethod = "BH", # for anota2seqRun useProgBar = FALSE, # for anota2seqRun getRVM = TRUE, # for anota2seqGetOutput output = 'full' # for anota2seqGetOutput ) opt_types <- lapply(opt, class) # Apply parameter overrides args_opt <- parse_args('--subset_to_contrast_samples TRUE') for ( ao in names(args_opt)){ if (! ao %in% names(opt)){ stop(paste("Invalid option:", ao)) }else{ # Preserve classes from defaults where possible if (! is.null(opt[[ao]])){ args_opt[[ao]] <- as(args_opt[[ao]], opt_types[[ao]]) } opt[[ao]] <- args_opt[[ao]] } } # Check if required parameters have been provided required_opts <- c('sample_treatment_col', 'reference_level', 'target_level', 'output_prefix') missing <- required_opts[!unlist(lapply(opt[required_opts], is_valid_string)) | !required_opts %in% names(opt)] if (length(missing) > 0){ stop(paste("Missing required options:", paste(missing, collapse=', '))) } # Check file inputs are valid for (file_input in c('count_file', 'sample_file')){ if (! is_valid_string(opt[[file_input]])) { stop(paste("Please provide", file_input), call. = FALSE) } if (! file.exists(opt[[file_input]])){ stop(paste0('Value of ', file_input, ': ', opt[[file_input]], ' is not a valid file')) } } ################################################ ################################################ ## Finish loading libraries ## ################################################ ################################################ library(anota2seq) ################################################ ################################################ ## READ IN COUNTS FILE AND SAMPLE METADATA ## ################################################ ################################################ count.table <- read_delim_flexible( file = opt$count_file, header = TRUE, row.names = opt$gene_id_col, check.names = FALSE ) sample.sheet <- read_delim_flexible(file = opt$sample_file) # Deal with spaces that may be in sample column opt$sample_id_col <- make.names(opt$sample_id_col) if (! opt$sample_id_col %in% colnames(sample.sheet)){ stop(paste0("Specified sample ID column '", opt$sample_id_col, "' is not in the sample sheet")) } # Sample sheet can have duplicate rows for multiple sequencing runs, so uniqify # before assigning row names sample.sheet <- sample.sheet[! duplicated(sample.sheet[[opt$sample_id_col]]), ] rownames(sample.sheet) <- sample.sheet[[opt$sample_id_col]] # Check that all samples specified in the input sheet are present in the counts # table. Assuming they are, subset and sort the count table to match the sample # sheet missing_samples <- sample.sheet[!rownames(sample.sheet) %in% colnames(count.table), opt$sample_id_col] if (length(missing_samples) > 0) { stop(paste( length(missing_samples), 'specified samples missing from count table:', paste(missing_samples, collapse = ',') )) } else{ # Save any non-count data, will gene metadata etc we might need later noncount.table <- count.table[, !colnames(count.table) %in% rownames(sample.sheet), drop = FALSE] count.table <- count.table[, rownames(sample.sheet)] } ################################################ ################################################ ## CHECK CONTRAST SPECIFICATION ## ################################################ ################################################ sample_treatment_col <- make.names(opt$sample_treatment_col) if (!sample_treatment_col %in% colnames(sample.sheet)) { stop( paste0( 'Chosen contrast variable \"', sample_treatment_col, '\" not in sample sheet' ) ) } else if (any(!c(opt$reflevel, opt$treatlevel) %in% sample.sheet[[sample_treatment_col]])) { stop( paste( 'Please choose reference and treatment levels that are present in the', sample_treatment_col, 'column of the sample sheet' ) ) } # Optionally, subset to only the samples involved in the contrast if (opt$subset_to_contrast_samples){ sample_selector <- sample.sheet[[sample_treatment_col]] %in% c(opt$target_level, opt$reference_level) selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } # Optionally, remove samples with specified values in a given field (probably # don't use this as well as the above) if ((is_valid_string(opt$exclude_samples_col)) && (is_valid_string(opt$exclude_samples_values))){ exclude_values = unlist(strsplit(opt$exclude_samples_values, split = ';')) if (! opt$exclude_samples_col %in% colnames(sample.sheet)){ stop(paste(opt$exclude_samples_col, ' specified to subset samples is not a valid sample sheet column')) } print(paste0('Excluding samples with values of ', opt$exclude_samples_values, ' in ', opt$exclude_samples_col)) sample_selector <- ! sample.sheet[[opt$exclude_samples_col]] %in% exclude_values selected_samples <- sample.sheet[sample_selector, opt$sample_id_col] count.table <- count.table[, selected_samples] sample.sheet <- sample.sheet[selected_samples, ] } ################################################ ################################################ ## Run anota2seqRun() ## ################################################ ################################################ # Separate matrix into riboseq and rnaseq data riboseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'riboseq'] rnaseq_samples <- sample.sheet[[opt$sample_id_col]][sample.sheet[['type']] == 'rnaseq'] if (! is.null(opt$samples_pairing_col)){ riboseq_samples <- riboseq_samples[order(sample.sheet[riboseq_samples, opt$samples_pairing_col])] rnaseq_samples <- rnaseq_samples[order(sample.sheet[rnaseq_samples, opt$samples_pairing_col])] } riboseq_data <- count.table[,riboseq_samples] rnaseq_data <- count.table[,rnaseq_samples] # Make the anota2seqDataSet anota2seqDataSetFromMatrix_args <- list( dataP = riboseq_data, dataT = rnaseq_data, phenoVec = sample.sheet[rnaseq_samples, opt$sample_treatment_col], dataType = opt$dataType, normalize = opt$normalize, transformation = opt$transformation, filterZeroGenes = opt$filterZeroGenes, varCutOff = opt$varCutOff ) if (! is.null(opt$samples_batch_col)){ anota2seqDataSetFromMatrix_args$batchVec <- samples[rnaseq_samples, opt$samples_batch_col] } ads <- do.call(anota2seqDataSetFromMatrix, anota2seqDataSetFromMatrix_args) # Run anota2seqRun contrast_matrix <- matrix( c(-1, 1), nrow = 2, dimnames = list(sort(c(opt$reference_level, opt$target_level)), NULL) ) ads <- anota2seqRun( ads, contrasts = contrast_matrix, performQC = opt$performQC, onlyGroup = opt$onlyGroup, performROT = opt$performROT, generateSingleGenePlots = opt$generateSingleGenePlots, analyzeBuffering = opt$analyzeBuffering, analyzemRNA = opt$analyzemRNA, useRVM = opt$useRVM, correctionMethod = opt$correctionMethod, useProgBar = FALSE ) ################################################ ################################################ ## Generate outputs ## ################################################ ################################################ contrast.name <- paste(opt$target_level, opt$reference_level, sep = "_vs_") cat("Saving results for ", contrast.name, " ... ", sep = "") for (analysis in c("translated mRNA", "total mRNA", "translation", "buffering", "mRNA abundance")){ output <- anota2seqGetOutput( ads, analysis = analysis, output = opt$output, getRVM = opt$getRVM, selContrast = 1 ) write.table( output, file = paste(opt$output_prefix, sub(' ', '_', analysis), 'anota2seq.results.tsv', sep = '.'), col.names = TRUE, row.names = FALSE, sep = ' ', quote = FALSE ) } # Fold change plot png( file = paste(opt$output_prefix, 'fold_change.png', sep = '.'), width = 720, height = 720 ) anota2seqPlotFC( ads, visualizeRegModes = "all", selContrast = 1, contrastName = contrast.name, plotToFile = FALSE ) dev.off() # R object for other processes to use saveRDS(ads, file = paste(opt$output_prefix, 'Anota2seqDataSet.rds', sep = '.')) # Rename files files output by anota2seqPerformQC() sapply(list.files(pattern = "^ANOTA2SEQ_"), function(f) file.rename(f, sub("^ANOTA2SEQ_", paste0(opt$output_prefix, '.'), f))) ################################################ ################################################ ## R SESSION INFO ## ################################################ ################################################ sink(paste(opt$output_prefix, "R_sessionInfo.log", sep = '.')) print(sessionInfo()) sink() ################################################ ################################################ ## VERSIONS FILE ## ################################################ ################################################ r.version <- strsplit(version[['version.string']], ' ')[[1]][3] anota2seq.version <- as.character(packageVersion('anota2seq')) writeLines( c( '"NFCORE_RIBOSEQ:RIBOSEQ:ANOTA2SEQ_ANOTA2SEQRUN":', paste(' bioconductor-anota2seq:', anota2seq.version) ), 'versions.yml') ################################################ ################################################ ################################################ ################################################ Command exit status: 1 Command output: (empty) Command error: Error in anota2seqCheckInput(dataP, dataT, phenoVec, batchVec, NULL, "BH", : Number of columns for dataT and dataP must be identical. Calls: do.call -> -> anota2seqCheckInput Execution halted Work dir: /data/project/ding_lab/riboseq/work/f5/e60beabdc92c21be951212d1085ca4 Container: /data/project/ding_lab/riboseq/work/singularity/depot.galaxyproject.org-singularity-bioconductor-anota2seq-1.24.0--r43hdfd78af_0.img Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named `.command.sh` -- Check '.nextflow.log' file for details ERROR ~ Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting -- Check '.nextflow.log' file for details