differential-splicing
starsforks
How to install differential-splicing?
gptomics/bioskills/differential-splicing
$npx -y skills add GPTomics/bioSkills --skill differential-splicingDetects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (j
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | Reference examples tested with: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, Shiba 0.5+, STAR 2.7.11+, regtools 1.0+, pandas 2.2+, R 4.4+ |
| 4 | |
| 5 | Before using code patterns, verify installed versions match. If versions differ: |
| 6 | - Python: `pip show <package>` then `help(module.function)` to check signatures |
| 7 | - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters |
| 8 | - CLI: `<tool> --version` then `<tool> --help` to confirm flags |
| 9 | |
| 10 | If code throws ImportError, AttributeError, or TypeError, introspect the installed |
| 11 | package and adapt the example to match the actual API rather than retrying. |
| 12 | |
| 13 | # Differential Splicing |
| 14 | |
| 15 | Detect splicing changes between conditions. Tool choice is a decision about **statistical model**, **annotation dependence**, and **calibration regime** under the specific experimental design — not a preference. Wrong tool for the design produces uncalibrated FDR or systematic effect-size bias. |
| 16 | |
| 17 | ## Statistical Model Taxonomy |
| 18 | |
| 19 | | Tool | Model | Test statistic | Min reps per group | Calibration regime | Fails when | |
| 20 | |------|-------|-----------------|---------------------|---------------------|------------| |
| 21 | | rMATS-turbo | Binomial counts with hierarchical PSI variance | LRT on \|ΔPSI\| > `cutoff` (default 0.0001) | n>=3 | Well-calibrated at n>=3 with adequate junction reads | Junction read imbalance; very low coverage; uncorrected for confounders | |
| 22 | | leafcutter | Dirichlet-multinomial GLM at cluster level | LRT on group factor | n>=2 (n>=3 preferred) | Strong at n>=3; novel-junction-friendly | Undersampled clusters (DM dispersion unstable); cluster topology arbitrariness | |
| 23 | | MAJIQ deltapsi | Beta-binomial bootstrap -> posterior over PSI per LSV | P(\|ΔPSI\| > T) threshold (T=0.2) | n>=3 | Replicate-structured n=3 vs n=3 | Cohorts where between-sample variability dominates between-group | |
| 24 | | MAJIQ HET | Same model, heterogeneity-aware | Per-LSV permutation-based test | n>=10 | n>=10 vs n>=10 cohort designs | Tightly-controlled small replicate experiments | |
| 25 | | SUPPA2 (empirical) | Empirical null from between-replicate ΔPSI | ECDF on \|ΔPSI\| conditioned on TPM | n>=4 | n>=4 vs n>=4 with paired-end deep sequencing | n<=3 vs n<=3 (sparse null collapses) | |
| 26 | | SUPPA2 (classical) | Wilcoxon rank-sum on PSI distributions | Wilcoxon p-value | n>=2 | Small samples; non-parametric backup | Cassette events with tight PSI distributions | |
| 27 | | Shiba (2025) | Beta-binomial with explicit junction-imbalance correction | LRT | n>=2 | n=2-3 vs n=2-3 | Established benchmarks limited (new tool) | |
| 28 | | LeafcutterMD | Dirichlet-multinomial outlier mode | Per-sample p-value | n=1 vs cohort >=20 | Single-patient vs cohort | Too few controls (<20) | |
| 29 | | FRASER 2.0 | Beta-binomial autoencoder on Intron Jaccard Index | Per-sample p-value with delta cutoff | n=1 vs cohort >=20 | n>=20 control cohort, single-patient query | See `outlier-splicing-detection` for this regime | |
| 30 | |
| 31 | The first decision is which **regime** the design falls into: between-group with replicates, heterogeneous cohort, or single-sample-vs-cohort. Within each regime, tool choice is much smaller (1-2 options). |
| 32 | |
| 33 | Comprehensive 2023-2026 benchmarks: Olofsson 2023 *Biochem Biophys Res Commun*; Tran 2025 *WIREs RNA*; Kubota 2025 *NAR*. Methodology evolves — verify benchmarks and tool docs before reporting. Default 2026 recommendation: run **two complementary tools** (rMATS + leafcutter) and require concordance for high-confidence calls. |
| 34 | |
| 35 | ## Decision Tree by Experimental Design |
| 36 | |
| 37 | | Scenario | Recommended tool | Why | Threshold | |
| 38 | |----------|------------------|-----|-----------| |
| 39 | | Standard n=3 vs n=3, GENCODE-annotated | rMATS-turbo + leafcutter (concordance) | Two algorithmic families; concordant hits = high-confidence | FDR<0.05, \|ΔPSI\|>0.10 | |
| 40 | | n=2 vs n=2 small pilot | Shiba | Junction-imbalance correction matters most at low coverage | FDR<0.10, \|ΔPSI\|>0.10 | |
| 41 | | n=10+ vs n=10+ heterogeneous (clinical, GTEx-style) | MAJIQ V3 HET | HET designed for between-sample heterogeneity | P(\|ΔPSI\|>0.2)>0.95 | |
| 42 | | Single rare-disease patient vs panel of n>=20 | FRASER 2.0 (see outlier-splicing-detection) | Outlier detection statistical model is fundamentally different | padj<0.05, \|delta-jaccard\|>=0.1 | |
| 43 | | Time-course / multi-condition design | Custom DEXS |
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