isoform-dominance 2.0.0

Config-driven isoform-usage quantification and discrimination from bulk RNA-seq

isoform-dominance

Isoform-usage quantification and discrimination from bulk RNA-seq — one config away for any gene.

Most isoform analyses stumble on two things: deciding which transcripts form a functional group, and knowing whether short reads can even tell the groups apart. isoform-dominance handles both, then quantifies and tests the comparison end-to-end:

annotate  gene symbol      → proposed isoform groups        (Ensembl, by 3' terminal exon)
identify  config           → are the groups distinguishable by short reads? (k-mer uniqueness)
extract   Salmon quant.sf  → per-donor isoform-group TPM
stats     per-donor TPM    → paired Wilcoxon, per cohort + combined, + figure
qc        marker TPM       → contamination control (is the signal a cell-type artifact?)

Bundled self-test: short LEPR isoform (LepRa) predominates over the long isoform (LepRb) in control human choroid plexus across two independent cohorts; combined n = 11, P = 1×10⁻³.

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Install

pip install -e ".[dev]"        # from a clone
isoform-dominance --version

Verify it works (no downloads, seconds)

isoform-dominance selftest
# or: pytest -q

reproduces the published LEPR result (5/5, 6/6, combined n=11 P=9.8e-4) on a clean machine.

What makes it more than a quantifier

1. Auto isoform grouping (annotate). Give a gene symbol; it pulls the gene's protein-coding transcripts from Ensembl, clusters them by their 3' terminal-exon splice acceptor (the alternative last exon that defines functional isoform classes), and proposes a comparison you review and rename.

isoform-dominance annotate --gene LEPR --out config.json
#   iso_896aa : 7 transcripts   (short / LepRa)
#   iso_1165aa: 2 transcripts   (long / LepRb, canonical)

2. Identifiability guardrail (identify). Short reads can only quantify an isoform group that has unique sequence. This checks per-group unique k-mers and refuses to pretend a group is measurable when it isn't.

isoform-dominance identifiability --config config.json
#   [OK] iso_896aa : 3399 unique k-mers
#   [OK] iso_1165aa: 5369 unique k-mers
#   primary_comparison distinguishable by short reads: True

Full workflow (real data)

# 0) build a decoy-aware index once (Salmon + GENCODE) — see scripts/01_salmon_quant.sbatch
# 1) quantify on an HPC cluster:
sbatch scripts/01_salmon_quant.sbatch                    # -> quant/<donor>/quant.sf
# 2) extract per cohort:
isoform-dominance extract --config config.json --quantdir quant \
    --samplemap example/sample_map_GSE228458.csv --cohort GSE228458 --out perdonor_GSE228458.csv
# 3) stats + figure:
isoform-dominance stats --config config.json --condition control \
    --perdonor GSE228458=perdonor_GSE228458.csv --perdonor GSE137619=perdonor_GSE137619.csv \
    --out results/dominance
# 4) optional contamination control:
isoform-dominance qc --config config.json \
    --markers GSE228458=markers_228.csv --target GSE228458=perdonor_GSE228458.csv --out results/qc

Statistical notes

• Donor-level two-sided exact Wilcoxon signed-rank (scipy.stats.wilcoxon), per cohort + combined.
• Small-n floor: n = 5 cannot reach P < 0.05 in the exact two-sided test (floor 0.0625); report exact P + direction (e.g. 5/5) and combine concordant cohorts for the summary statistic.
• Effect size = median fold-change, reported alongside significance.

Layout

src/isoform_dominance/   annotate · identifiability · extract · stats · contamination · cli · _selftest
tests/                   pytest (offline; reproduces the published result + unit tests)
scripts/01_salmon_quant.sbatch
example/                 config + sample maps
.github/workflows/ci.yml docs/  pyproject.toml  CITATION.cff  LICENSE

Citation

Cite this repository (see CITATION.cff, DOI 10.5281/zenodo.20672052) and Salmon: Patro, R. et al. Nat. Methods 14, 417–419 (2017). https://doi.org/10.1038/nmeth.4197

License

MIT (see LICENSE).