Merge branch 'cw-7238' into 'dev'
SQANTI3 results in report [CW-7238] See merge request epi2melabs/workflows/wf-transcriptomes!271
This commit is contained in:
Kiah McIntosh
commit
50b971604e
@ -160,7 +160,7 @@ docker-run:
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NF_WORKFLOW_OPTS: "--fastq test_data/smoke/reads.fastq --sample sampleA --ref_genome test_data/smoke/reference.fa --ref_annotation test_data/smoke/annotation.gtf --direct_rna"
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AFTER_NEXTFLOW_CMD: >
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test -f ${CI_PROJECT_NAME}/samples/sampleA/alignment/reads.bam &&
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test -f ${CI_PROJECT_NAME}/samples/sampleA/sampleA_sqanti/classification_summary.tsv
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test -f ${CI_PROJECT_NAME}/samples/sampleA/sqanti/classification_summary.tsv
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# Smoke: end-to-end DE/DTU wiring and expected contrast output files.
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- if: $MATRIX_NAME == "smoke_de"
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10
README.md
10
README.md
@ -220,7 +220,7 @@ Each sample is aligned to the supplied reference genome with
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[`minimap2`](https://github.com/lh3/minimap2), then sorted and indexed with
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[`samtools`](https://www.htslib.org/). The aligned BAMs under
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`samples/<alias>/alignment/` are the main alignment files used for transcriptome
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analysis, optional `SQANTI3` QC, and optional IGV viewing.
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analysis, optional [`SQANTI3`](https://github.com/conesalab/SQANTI3) QC, and optional IGV viewing.
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### 4. Cohort transcriptome construction
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@ -245,8 +245,8 @@ for DE/DTU.
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Transcript FASTA files are derived from GTF plus genome using `gffread`.
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When `--skip_sqanti` is not set, `SQANTI3` classifies the cohort and per-sample
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transcriptomes and produces structural QC summaries. The cohort `SQANTI3`
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results live under `cohort/sqanti_cohort/`, while per-sample `SQANTI3`
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directories are published under `samples/<alias>/<alias>_sqanti/`.
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results live under `cohort/sqanti/`, while per-sample `SQANTI3`
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directories are published under `samples/<alias>/sqanti/`.
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### 7. Optional DE and DTU analysis
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@ -368,13 +368,13 @@ Output files may be aggregated including information for all samples or provided
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| Cohort transcript counts | cohort/transcript_counts.tsv | Transcript-level count matrix produced by bambu. | aggregated |
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| Cohort gene counts | cohort/gene_counts.tsv | Gene-level count matrix derived from bambu output. | aggregated |
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| Cohort transcript metadata | cohort/transcript_metadata.tsv | Transcript annotations and bambu transcript classes for the cohort model. | aggregated |
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| Cohort SQANTI3 summary | cohort/sqanti_cohort/classification_summary.tsv | SQANTI3 classification summary for the cohort transcriptome when SQANTI3 QC is enabled. | aggregated |
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| Cohort SQANTI3 summary | cohort/sqanti/classification_summary.tsv | SQANTI3 classification summary for the cohort transcriptome when SQANTI3 QC is enabled. | aggregated |
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| Per-sample transcriptome GTF | samples/{{ alias }}/transcripts.gtf | Independent bambu transcript model for an individual sample. | per-sample |
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| Per-sample transcriptome FASTA | samples/{{ alias }}/{{ alias }}.transcriptome.fa | Transcript sequences derived from the per-sample GTF. | per-sample |
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| Per-sample transcript counts | samples/{{ alias }}/transcript_counts.tsv | Transcript-level abundance estimates for the per-sample bambu model. | per-sample |
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| Per-sample gene counts | samples/{{ alias }}/gene_counts.tsv | Gene-level abundance estimates for the per-sample bambu model. | per-sample |
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| Per-sample transcript metadata | samples/{{ alias }}/transcript_metadata.tsv | Transcript annotations and bambu transcript classes for the per-sample model. | per-sample |
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| Per-sample SQANTI3 summary | samples/{{ alias }}/{{ alias }}_sqanti/classification_summary.tsv | SQANTI3 classification summary for the per-sample transcriptome when SQANTI3 QC is enabled. | per-sample |
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| Per-sample SQANTI3 summary | samples/{{ alias }}/sqanti/classification_summary.tsv | SQANTI3 classification summary for the per-sample transcriptome when SQANTI3 QC is enabled. | per-sample |
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| Differential gene expression results | de_analysis/{{ contrast }}/results_dge.tsv | DESeq2 gene-level differential expression results for one contrast. | aggregated |
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| Differential gene expression plots | de_analysis/{{ contrast }}/results_dge.pdf | PDF plots generated during DESeq2 analysis for one contrast. | aggregated |
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| Differential transcript usage results | de_analysis/{{ contrast }}/results_dtu_transcript.tsv | Transcript-level DTU results for one contrast. | aggregated |
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@ -2,10 +2,13 @@
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import json
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import math
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import os
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from pathlib import Path
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from bokeh.resources import INLINE as BOKEH_INLINE
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from dominate.tags import br, div, h3, h4, p, pre, script, strong, style as dom_style
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from dominate.tags import (
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br, div, h3, h4, p, pre, script, small, strong, style as dom_style
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)
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from dominate.util import raw
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from ezcharts.components import fastcat
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from ezcharts.components.ezchart import EZChart
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@ -21,6 +24,35 @@ from .util import get_named_logger, wf_parser # noqa: ABS101
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from .volcano import volcano # noqa: ABS101
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classification_categories = {
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"Full splice match": (
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"Reference and query isoforms have the same number of exons and "
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"all internal junctions agree."
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),
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"Incomplete splice match": (
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"Query isoform has fewer 5′ exons than the reference, with "
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"matching internal junctions."
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),
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"Novel in catalog": (
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"No full or incomplete splice match, but uses a "
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"combination of known donor/acceptor splice sites."
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),
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"Novel not in catalog": (
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"No full or incomplete splice match, with at least "
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"one unannotated donor or acceptor splice site."
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),
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"Antisense": (
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"No same-strand reference overlap, but antisense to an "
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"annotated gene."
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),
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"Genic intron": (
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"Query isoform is fully contained within an annotated intron."
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),
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"Genic": "Query isoform overlaps introns and exons.",
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"Intergenic": "Query isoform lies in an intergenic region.",
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}
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def get_bokeh_widgets_js():
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"""Return the inline Bokeh widgets JavaScript bundle."""
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widgets_index = BOKEH_INLINE.components_for("js").index("bokeh-widgets")
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@ -69,6 +101,11 @@ def _format_ratio_value(value):
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return f"{numeric:.2f}x"
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def _format_classification_label(name):
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"""Return canonical report label for a summary classification value."""
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return str(name).strip().replace("-", "_").replace("_", " ").capitalize()
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def _transcriptome_summary(transcriptome_dir):
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"""Return transcriptome metrics and transcript class counts DataFrames."""
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tx_meta = _read_table(Path(transcriptome_dir) / "transcript_metadata.tsv")
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@ -122,13 +159,40 @@ def _sample_summaries(samples_dir):
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return summaries
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def _sqanti_tables(sqanti_dir):
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"""Return a dict of SQANTI3 classification summary DataFrames keyed by label."""
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tables = {}
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for summary in sorted(Path(sqanti_dir).rglob("classification_summary.tsv")):
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label = summary.parent.name
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tables[label] = _read_table(summary)
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return tables
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def _sqanti_table(sqanti_dir):
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"""Return a SQANTI3 classification summary DataFrame."""
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rows = []
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summaries = []
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for root, _, files in os.walk(sqanti_dir, followlinks=True):
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if "classification_summary.tsv" in files:
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summaries.append(Path(root) / "classification_summary.tsv")
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for summary in sorted(summaries):
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table = _read_table(summary)
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if table is None or table.empty:
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continue
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sample = summary.parent.name
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sample_counts = {"Sample": sample}
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for _, row in table.iterrows():
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feature = _format_classification_label(row["structural_category"])
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count = _coerce_float(row["count"])
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sample_counts[feature] = int(round(count)) if count is not None else 0
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rows.append(sample_counts)
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if not rows:
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return None
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sqanti_df = pd.DataFrame(rows).fillna(0)
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feature_cols = list(classification_categories.keys())
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for col in feature_cols:
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if col not in sqanti_df.columns:
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sqanti_df[col] = 0
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sqanti_df[col] = sqanti_df[col].astype(int)
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sqanti_df = sqanti_df[["Sample"] + feature_cols]
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is_cohort = sqanti_df["Sample"].str.lower().eq("cohort")
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sqanti_df = sqanti_df.assign(_is_cohort=is_cohort)
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return sqanti_df.sort_values(["_is_cohort", "Sample"]).drop(columns="_is_cohort")
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def _contrast_results(de_dir, filename, n=None):
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@ -686,13 +750,15 @@ def main(args):
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with tabs.add_tab(stats_file.stem.replace(".flagstat", "")):
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pre(stats_file.read_text())
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sqanti_tables = _sqanti_tables(args.sqanti_dir)
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if sqanti_tables:
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sqanti_table = _sqanti_table(args.sqanti_dir)
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if sqanti_table is not None and not sqanti_table.empty:
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with report.add_section("SQANTI3 classification", "SQANTI3"):
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tabs = Tabs()
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for label, table in sqanti_tables.items():
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with tabs.add_tab(label):
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DataTable.from_pandas(table, use_index=False)
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p("Summary of structural classification of isoforms using SQANTI3.")
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DataTable.from_pandas(sqanti_table, use_index=False)
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with p():
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for category, description in classification_categories.items():
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small(strong(f"{category}: "))
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small(raw(f"{description}<br>"))
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if args.de_dir and Path(args.de_dir).exists():
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# Load DE QC statistics
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@ -65,7 +65,7 @@ Each sample is aligned to the supplied reference genome with
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[`minimap2`](https://github.com/lh3/minimap2), then sorted and indexed with
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[`samtools`](https://www.htslib.org/). The aligned BAMs under
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`samples/<alias>/alignment/` are the main alignment files used for transcriptome
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analysis, optional `SQANTI3` QC, and optional IGV viewing.
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analysis, optional [`SQANTI3`](https://github.com/conesalab/SQANTI3) QC, and optional IGV viewing.
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### 4. Cohort transcriptome construction
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@ -90,8 +90,8 @@ for DE/DTU.
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Transcript FASTA files are derived from GTF plus genome using `gffread`.
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When `--skip_sqanti` is not set, `SQANTI3` classifies the cohort and per-sample
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transcriptomes and produces structural QC summaries. The cohort `SQANTI3`
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results live under `cohort/sqanti_cohort/`, while per-sample `SQANTI3`
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directories are published under `samples/<alias>/<alias>_sqanti/`.
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results live under `cohort/sqanti/`, while per-sample `SQANTI3`
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directories are published under `samples/<alias>/sqanti/`.
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### 7. Optional DE and DTU analysis
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@ -13,13 +13,13 @@ Output files may be aggregated including information for all samples or provided
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| Cohort transcript counts | cohort/transcript_counts.tsv | Transcript-level count matrix produced by bambu. | aggregated |
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| Cohort gene counts | cohort/gene_counts.tsv | Gene-level count matrix derived from bambu output. | aggregated |
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| Cohort transcript metadata | cohort/transcript_metadata.tsv | Transcript annotations and bambu transcript classes for the cohort model. | aggregated |
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| Cohort SQANTI3 summary | cohort/sqanti_cohort/classification_summary.tsv | SQANTI3 classification summary for the cohort transcriptome when SQANTI3 QC is enabled. | aggregated |
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| Cohort SQANTI3 summary | cohort/sqanti/classification_summary.tsv | SQANTI3 classification summary for the cohort transcriptome when SQANTI3 QC is enabled. | aggregated |
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| Per-sample transcriptome GTF | samples/{{ alias }}/transcripts.gtf | Independent bambu transcript model for an individual sample. | per-sample |
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| Per-sample transcriptome FASTA | samples/{{ alias }}/{{ alias }}.transcriptome.fa | Transcript sequences derived from the per-sample GTF. | per-sample |
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| Per-sample transcript counts | samples/{{ alias }}/transcript_counts.tsv | Transcript-level abundance estimates for the per-sample bambu model. | per-sample |
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| Per-sample gene counts | samples/{{ alias }}/gene_counts.tsv | Gene-level abundance estimates for the per-sample bambu model. | per-sample |
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| Per-sample transcript metadata | samples/{{ alias }}/transcript_metadata.tsv | Transcript annotations and bambu transcript classes for the per-sample model. | per-sample |
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| Per-sample SQANTI3 summary | samples/{{ alias }}/{{ alias }}_sqanti/classification_summary.tsv | SQANTI3 classification summary for the per-sample transcriptome when SQANTI3 QC is enabled. | per-sample |
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| Per-sample SQANTI3 summary | samples/{{ alias }}/sqanti/classification_summary.tsv | SQANTI3 classification summary for the per-sample transcriptome when SQANTI3 QC is enabled. | per-sample |
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| Differential gene expression results | de_analysis/{{ contrast }}/results_dge.tsv | DESeq2 gene-level differential expression results for one contrast. | aggregated |
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| Differential gene expression plots | de_analysis/{{ contrast }}/results_dge.pdf | PDF plots generated during DESeq2 analysis for one contrast. | aggregated |
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| Differential transcript usage results | de_analysis/{{ contrast }}/results_dtu_transcript.tsv | Transcript-level DTU results for one contrast. | aggregated |
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2
main.nf
2
main.nf
@ -225,8 +225,6 @@ workflow pipeline {
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.concat(transcriptome.sample_gene_rds.map { meta, rds -> [rds, "samples/${meta.alias}"] })
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.concat(transcriptome.sample_metadata.map { meta, metadata -> [metadata, "samples/${meta.alias}"] })
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.concat(generated_alignment_outputs)
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.concat(transcriptome.joint_sqanti_dir.map { [it, "cohort"] })
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.concat(transcriptome.sample_sqanti_dirs.map { meta, sqanti_dir -> [sqanti_dir, "samples/${meta.alias}"] })
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if (params.de_analysis) {
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results = results.concat(de_dir.map { [it, null] })
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@ -89,7 +89,7 @@
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"type": "aggregated"
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},
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"cohort-sqanti-summary": {
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"filepath": "cohort/sqanti_cohort/classification_summary.tsv",
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"filepath": "cohort/sqanti/classification_summary.tsv",
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"title": "Cohort SQANTI3 summary",
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"description": "SQANTI3 classification summary for the cohort transcriptome when SQANTI3 QC is enabled.",
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"mime-type": "text/tab-separated-values",
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@ -137,7 +137,7 @@
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"type": "per-sample"
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},
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"sample-sqanti-summary": {
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"filepath": "samples/{{ alias }}/{{ alias }}_sqanti/classification_summary.tsv",
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"filepath": "samples/{{ alias }}/sqanti/classification_summary.tsv",
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"title": "Per-sample SQANTI3 summary",
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"description": "SQANTI3 classification summary for the per-sample transcriptome when SQANTI3 QC is enabled.",
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"mime-type": "text/tab-separated-values",
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@ -140,18 +140,21 @@ process runJointSqanti {
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label "wf_transcriptomes_sqanti"
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cpus { params.threads ?: 4 }
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memory "24 GB"
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publishDir "${params.out_dir}/cohort",
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mode: "copy",
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saveAs: { "sqanti" }
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input:
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path gtf
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path annotation, stageAs: "annotation/*"
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tuple path(reference, stageAs: "reference/reference.fa"), path(ref_fai, stageAs: "reference/reference.fai")
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output:
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path "sqanti_cohort", emit: dir
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path "sqanti_cohort/classification_summary.tsv", emit: summary
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path "cohort", emit: dir
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path "cohort/classification_summary.tsv", emit: summary
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script:
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String extra = params.sqanti_extra_args ?: ""
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String skip_orf = params.sqanti_skip_orf ? "--skipORF" : ""
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"""
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mkdir sqanti_cohort
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mkdir cohort
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sqanti3_qc.py \
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--isoforms "${gtf}" \
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--refGTF "${annotation}" \
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@ -160,11 +163,11 @@ process runJointSqanti {
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--force_id_ignore \
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--report skip \
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-t ${task.cpus} \
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-d sqanti_cohort \
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-d cohort \
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-o cohort \
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${extra}
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workflow-glue summarise_sqanti --sqanti_dir sqanti_cohort \
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--output sqanti_cohort/classification_summary.tsv
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workflow-glue summarise_sqanti --sqanti_dir cohort \
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--output cohort/classification_summary.tsv
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"""
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}
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@ -173,18 +176,21 @@ process runPerSampleSqanti {
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label "wf_transcriptomes_sqanti"
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cpus { params.threads ?: 4 }
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memory "24 GB"
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publishDir "${params.out_dir}/samples/${meta.alias}",
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mode: "copy",
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saveAs: { "sqanti"}
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input:
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tuple val(meta), path(gtf)
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path annotation, stageAs: "annotation/*"
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tuple path(reference, stageAs: "reference/reference.fa"), path(ref_fai, stageAs: "reference/reference.fai")
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output:
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tuple val(meta), path("${meta.alias}_sqanti"), emit: dir
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tuple val(meta), path("${meta.alias}_sqanti/classification_summary.tsv"), emit: summary
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tuple val(meta), path("${meta.alias}"), emit: dir
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tuple val(meta), path("${meta.alias}/classification_summary.tsv"), emit: summary
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script:
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String extra = params.sqanti_extra_args ?: ""
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String skip_orf = params.sqanti_skip_orf ? "--skipORF" : ""
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"""
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mkdir "${meta.alias}_sqanti"
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mkdir "${meta.alias}"
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sqanti3_qc.py \
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--isoforms "${gtf}" \
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--refGTF "${annotation}" \
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@ -193,11 +199,11 @@ process runPerSampleSqanti {
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--force_id_ignore \
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--report skip \
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-t ${task.cpus} \
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-d "${meta.alias}_sqanti" \
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-d "${meta.alias}" \
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-o "${meta.alias}" \
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${extra}
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workflow-glue summarise_sqanti --sqanti_dir "${meta.alias}_sqanti" \
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--output "${meta.alias}_sqanti/classification_summary.tsv"
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workflow-glue summarise_sqanti --sqanti_dir "${meta.alias}" \
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--output "${meta.alias}/classification_summary.tsv"
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"""
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}
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