Detects 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 (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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Detects 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 (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Differential Splicing
Detect splicing changes between conditions. Tool choice is a decision about , , and under the specific experimental design — not a preference. Wrong tool for the design produces uncalibrated FDR or systematic effect-size bias.
statistical model
annotation dependence
calibration regime
Statistical Model Taxonomy
Tool
Model
Test statistic
Min reps per group
Calibration regime
Fails when
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
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
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
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)
SUPPA2 (classical)
Wilcoxon rank-sum on PSI distributions
Wilcoxon p-value
n>=2
Small samples; non-parametric backup
Cassette events with tight PSI distributions
Shiba (2025)
Beta-binomial with explicit junction-imbalance correction
LRT
n>=2
n=2-3 vs n=2-3
Established benchmarks limited (new tool)
LeafcutterMD
Dirichlet-multinomial outlier mode
Per-sample p-value
n=1 vs cohort >=20
Single-patient vs cohort
Too few controls (<20)
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
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).
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.
Decision Tree by Experimental Design
Scenario
Recommended tool
Why
Threshold
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
n=2 vs n=2 small pilot
Shiba
Junction-imbalance correction matters most at low coverage
FDR<0.10, |ΔPSI|>0.10
n=10+ vs n=10+ heterogeneous (clinical, GTEx-style)
MAJIQ V3 HET
HET designed for between-sample heterogeneity
P(|ΔPSI|>0.2)>0.95
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
Time-course / multi-condition design
Custom DEXSeq or limma on PSI matrix
rMATS/leafcutter primarily 2-group
FDR<0.05 on time:group interaction
Paired tumor-normal
rMATS with --paired-stats
Paired test reduces inter-patient variance
FDR<0.05, paired |ΔPSI|>0.10
Cancer with spliceosomal mutation (SF3B1, U2AF1)
leafcutter or MAJIQ denovo
Cryptic events not in annotation
FDR<0.05; check 3'ss shifts in IGV
TDP-43 loss / ALS post-mortem
leafcutter denovo
Cryptic exons not in annotation
FDR<0.05; expect UNC13A, STMN2
Non-model organism without GENCODE-grade annotation
leafcutter
Annotation-free
FDR<0.05, |ΔPSI|>0.10
Long-read available
rMATS-long, FLAIR diffSplice
See long-read-splicing
Tool-specific
rMATS-turbo Differential Analysis
Goal: Detect statistically significant differential splicing between two groups from BAMs.
Approach: Run rMATS-turbo without --statoff, then filter by FDR + ΔPSI + per-replicate coverage.
--cstat 0.05 tests |ΔPSI| > 0.05; raise to 0.10 for stricter discovery. --novelSS enables novel-junction discovery (recommended with STAR 2-pass). For paired designs, add --paired-stats.
import pandas as pd
import numpy as np
se = pd.read_csv('rmats_output/SE.MATS.JC.txt', sep='\t')
defmin_per_rep(s):
return s.str.split(',').apply(lambda x: min(int(v) for v in x))
se['min_inc'] = min_per_rep(se['IJC_SAMPLE_1']).combine(min_per_rep(se['IJC_SAMPLE_2']), min)
se['min_skip'] = min_per_rep(se['SJC_SAMPLE_1']).combine(min_per_rep(se['SJC_SAMPLE_2']), min)
significant = se[
(se['FDR'] < 0.05) &
(se['IncLevelDifference'].abs() > 0.10) &
((se['min_inc'] + se['min_skip']) >= 10)
].copy()
significant['score'] = -np.log10(significant['FDR']) * significant['IncLevelDifference'].abs()
top = significant.nlargest(50, 'score')
LeafCutter2 (Buen Abad Najar 2025 bioRxiv) extends leafcutter with NMD-aware classification of unproductive splicing — useful when AS-NMD coupling is the question.
MAJIQ V3 Differential Analysis
Goal: Detect differential LSVs with full posterior distributions over ΔPSI; ideal for complex multi-junction events and heterogeneous cohorts.
Approach: Build splice graph -> compute coverage per group -> run deltapsi (replicate-structured) or heterogen (cohort-style).
MAJIQ V3 (Aicher, Slaff, Jewell, Barash bioRxiv 2024; public release 2025) uses Zarr storage (splicegraph.zarr); V2's SQLite splicegraph is deprecated. MAJIQ reports posterior probability P(|ΔPSI| > 0.2); thresholds are interpreted differently from FDR. Use HET for n>=10 vs n>=10 cohort designs (clinical, GTEx-style); deltapsi for tightly controlled n=3 vs n=3.
SUPPA2 Differential Analysis
Goal: Quick differential splicing from existing transcript quantifications, useful as a sanity check or pilot.
Approach: Generate per-condition PSI files from Salmon TPM, then run diffSplice with empirical or classical p-values.
suppa.py generateEvents -i annotation.gtf -o events -f ioe -e SE SS MX RI
for ev in SE A5 A3 MX RI; do
suppa.py psiPerEvent -i events_${ev}_strict.ioe -e ctrl_tpm.tsv -o ctrl_${ev}
suppa.py psiPerEvent -i events_${ev}_strict.ioe -e trt_tpm.tsv -o trt_${ev}
suppa.py diffSplice \
-m empirical \
-gc \
-i events_${ev}_strict.ioe \
-p ctrl_${ev}.psi trt_${ev}.psi \
-e ctrl_tpm.tsv trt_tpm.tsv \
-o diff_${ev}done
For n<=3 designs, switch -m classical (Wilcoxon). Empirical null requires sufficient between-replicate observations to construct.
Shiba for Low-Coverage / Few-Replicate Designs
Goal: Detect differential splicing with explicit junction-imbalance correction — addresses a known false-positive source for rMATS-style methods.
Approach: Shiba is a Snakemake-based pipeline configured via YAML. Install via bioconda, write a config file describing groups + BAMs, then run with snakemake.
conda install -c bioconda shiba
# Edit config.yaml with reference GTF, BAM groups, output dir, thresholds# Then run the Snakemake workflow:
snakemake -s snakeshiba.smk \
--configfile config.yaml \
--cores 8 \
--use-singularity \
--singularity-args "--bind $HOME:$HOME"
Shiba (Kubota 2025 NAR) reportedly outperforms rMATS at n=2 vs n=2 by correcting differential mappability between inclusion and skipping junctions; community calibration still emerging. See https://sika-zheng-lab.github.io/Shiba/ for the full config.yaml schema.
Per-Tool Failure Modes
rMATS: Confounder-Blind LRT
Trigger: Sequencing batch, RIN, library prep date, or sex correlates with the comparison of interest.
Mechanism: rMATS' default LRT does not natively accept covariates the way DESeq2 does; the --paired-stats flag handles paired designs but not arbitrary covariates.
Symptom: Many "significant" hits driven by batch rather than biological condition; PCA on PSI matrix shows samples clustering by batch rather than group.
Fix: Either (a) include batch as a stratification (run rMATS within each batch), (b) regress PSI matrix against batch in R, then test residuals, or (c) switch to leafcutter which accepts a confounders argument in differential_splicing.
leafcutter: Cluster Mis-Topology
Trigger: A cluster spans a complex topology (cassette + alternative donor in same cluster).
Mechanism: Cluster-level p-value reports "something in this cluster differs" but doesn't indicate which intron drove the change; downstream analysis needs per-intron effect sizes.
Symptom: Significant cluster, multiple introns with different ΔPSI directions, ambiguous biological interpretation.
Fix: Inspect cluster in leafviz; report per-intron effect sizes from ds_results_effect_sizes.txt; map cluster topology to canonical SE/A5SS/A3SS via flanking exon coordinates manually.
MAJIQ HET: Power vs Type-1 Tradeoff
Trigger: HET module on n=5-10 cohorts (between regimes).
Mechanism: HET assumes between-sample variability dominates; for moderate-replicate designs (n=5-10), HET is conservative and deltapsi is more powerful.
Symptom: HET reports few hits in n=5-10 designs; deltapsi on the same data reports many.
Fix: Use deltapsi for n=3-5; reserve HET for n>=10 with explicit cohort heterogeneity.
SUPPA2 Empirical: Sparse Null at Low Replicate
Trigger: n<=3 vs n<=3 with --method empirical.
Mechanism: Empirical null is ECDF of |ΔPSI| from between-replicate comparisons within each group, binned by transcript expression. Few replicates -> few null observations -> wide confidence on null distribution.
Symptom: Inflated FDR (15-30%); "significant" hits don't replicate or validate.
Fix: Use -m classical (Wilcoxon) for n<=3 vs n<=3; or switch tool entirely (leafcutter, Shiba).
Reconciliation: When Tools Disagree
The two most common short-read tools answer slightly different questions: rMATS classifies on annotated event templates; leafcutter classifies on observed cluster usage. Disagreement is informative.
Pattern
Likely cause
Action
rMATS sig, leafcutter not sig
rMATS junction imbalance OR rMATS event hits annotation that leafcutter clustered differently
Inspect locus in IGV; check Shiba on the same locus
leafcutter sig, rMATS not sig
Novel junction not in rMATS annotation; rMATS --novelSS may have missed it
Verify --novelSS was on; rerun if not
Both sig, opposite ΔPSI direction
Event class mismatch (rMATS calls SE positive, leafcutter sees A5SS shift in same cluster)
Operational rule: for high-confidence reporting, require concordant detection in two tools from different algorithmic families (event-based + cluster-based, or LSV + isoform-based). Document both calls and any explainable disagreements.
rMATS Output Columns Reference
Column
Meaning
IJC_SAMPLE_1 / SJC_SAMPLE_1
Comma-delimited inclusion / skipping junction counts per replicate, group 1
IJC_SAMPLE_2 / SJC_SAMPLE_2
Same for group 2
IncFormLen / SkipFormLen
Effective lengths normalizing PSI for differential mapping opportunity
upstreamES/EE, downstreamES/EE
Flanking exon coordinates (genomic order; strand-agnostic in column meaning)
exonStart_0base / exonEnd
Cassette exon coordinates (0-based half-open)
PValue
LRT p-value of |ΔPSI| > cutoff
FDR
BH-adjusted PValue within event class
IncLevel1, IncLevel2
Comma-delimited per-replicate PSI values
IncLevelDifference
mean(IncLevel1) - mean(IncLevel2); sign matches --b1 - --b2 order
Replicate Count and Power
Design
Recommended tools
Expected power for ΔPSI=0.2
n=2 vs n=2
leafcutter or Shiba; avoid SUPPA2
Marginal; many real effects missed
n=3 vs n=3
rMATS-turbo + leafcutter
Adequate at moderate coverage; standard
n=5 vs n=5
rMATS or leafcutter, MAJIQ deltapsi
Good; recommended for publication
n=10+ vs n=10+ heterogeneous
MAJIQ-HET
Designed for this scale
Single patient vs n=20+ controls
leafcutterMD or FRASER2
Outlier regime; see outlier-splicing-detection
For an effect-size of |ΔPSI|=0.10 (typical biological signal), power generally requires n>=4 and >=20 junction reads per replicate. Below this, expect to miss most real changes.
Significance and Effect-Size Thresholds
Stringency
|ΔPSI|
FDR
Use case
Lenient
> 0.05
< 0.10
Discovery, exploratory, hypothesis generation
Standard
> 0.10
< 0.05
Publication; default reporting threshold
Stringent
> 0.20
< 0.01
Validation cohort, follow-up targets
For MAJIQ: posterior probability P(|ΔPSI| > 0.2) >= 0.95 is roughly equivalent to standard stringency. Always document tool, threshold, and rationale.
Biologically meaningful ΔPSI varies by context:
A poison exon shift of |ΔPSI|=0.10 can halve functional protein (huge biology, modest number).
A stoichiometric isoform shift of |ΔPSI|=0.10 may be physiologically silent.
Therapeutic ASO target: SMA nusinersen aims for ΔPSI~+0.30 in SMN2 exon 7.
Confounder Handling
rMATS does not natively accept arbitrary covariates. Workarounds:
Stratification: run rMATS within each batch separately and meta-analyze.
PSI residuals (logit-transformed): PSI is bounded [0,1]; raw linear regression near the boundaries is biased. Logit-transform first, regress on confounders, then test residuals.
Switch to leafcutter (R function accepts confounders matrix; CLI accepts confounders as additional columns in the groups file).
import numpy as np
import statsmodels.formula.api as smf
# logit-transform PSI before residualization (PSI is bounded [0,1])
eps = 1e-3
psi['logit_psi'] = np.log((psi['psi'].clip(eps, 1 - eps)) / (1 - psi['psi'].clip(eps, 1 - eps)))
psi['psi_resid'] = smf.ols('logit_psi ~ batch + RIN', data=psi).fit().resid
# then test psi_resid by group via Wilcoxon
leafcutter accepts confounders two ways:
R function: differential_splicing(counts, x, confounders=numeric_matrix) accepts a numeric covariate matrix
CLI script: leafcutter_ds.R reads confounders from additional columns in the groups file (3rd, 4th, ... columns), NOT from a --confounders flag
MAJIQ does not accept arbitrary confounders; use stratification or switch tool.
Always check confounding before reporting: PCA on PSI matrix; if PC1 separates by batch rather than group, the comparison is confounded.
Multi-Group / Multi-Factor Designs
Design
Approach
3 groups (e.g. drug A, drug B, control)
Pairwise rMATS or leafcutter; OR limma/DESeq2 on logit-PSI matrix
Time-course (e.g. 0h, 6h, 24h)
DEXSeq on event counts with time as factor; or limma::lmFit on PSI matrix
2x2 factorial (genotype × treatment)
DEXSeq with interaction term; rMATS pairwise on interaction subsets
Continuous covariate (dose, age)
limma::lmFit on logit-PSI ~ covariate
For complex designs, custom regression on the PSI matrix is more flexible than rMATS/leafcutter pairwise.
Common Errors
Error
Cause
Solution
rMATS: numpy.AxisError
rMATS version mismatch with numpy >=2.0
Pin numpy<2.0 or update rMATS-turbo to >=4.3
leafcutter: zero variance in cluster
Cluster has all-zero counts in a group
Pre-filter with --min_samples_per_intron 5 --min_samples_per_group 3
MAJIQ: out of memory
Default settings on >50-sample cohort
Use --mem-profile flag; chunk samples; consider HET for large cohorts
Comparing tool outputs naively — MAJIQ posteriors and rMATS FDR are different scales; use threshold equivalents (P>0.95 ~ FDR<0.05 in many regimes) but confirm with simulation when reporting.
Forgetting NMD direction — increased PSI of a poison exon decreases protein. Always check whether the alternative form is PTC-introducing using ORF-aware annotation.
Cryptic splicing in TDP-43 loss / SF3B1-mutant samples — annotation-bound tools (rMATS, SUPPA2) miss these; need leafcutter or MAJIQ with denovo mode.
