Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
Reference examples tested with: STAR 2.7.11+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Detect differential alternative splicing events between experimental conditions.
| Tool | Input | Approach | Strengths |
|---|---|---|---|
| rMATS-turbo |
| BAM |
| Junction counting |
| Novel junctions, statistical model |
| SUPPA2 | TPM | Transcript ratios | Speed, isoform-aware |
| leafcutter | BAM | Intron clustering | Novel events, no annotation bias |
Goal: Detect statistically significant differential splicing events between two conditions from BAM files.
Approach: Run rMATS-turbo on condition-grouped BAMs, then filter results by FDR and delta PSI thresholds.
"Find differential splicing between conditions" -> Compare junction-level inclusion across sample groups with statistical testing.
rmats.py + pandas filtering (rMATS-turbo)suppa.py diffSplice (SUPPA2, TPM-based)leafcutter_ds.R (leafcutter, annotation-free)# Create sample lists (one BAM path per line)
# condition1_bams.txt: /path/to/sample1.bam, /path/to/sample2.bam, ...
# condition2_bams.txt: /path/to/sample3.bam, /path/to/sample4.bam, ...
rmats.py \
--b1 condition1_bams.txt \
--b2 condition2_bams.txt \
--gtf annotation.gtf \
-t paired \
--readLength 150 \
--nthread 8 \
--od rmats_output \
--tmp rmats_tmp
import pandas as pd
# Load results for skipped exons
se = pd.read_csv('rmats_output/SE.MATS.JC.txt', sep='\t')
# Filter significant differential splicing events
# |deltaPSI| > 0.1 (lenient) or > 0.2 (stringent)
# FDR < 0.05
significant = se[
(se['FDR'] < 0.05) &
(se['IncLevelDifference'].abs() > 0.1)
].copy()
print(f'{len(significant)} significant SE events')
print(significant[['GeneID', 'geneSymbol', 'IncLevelDifference', 'FDR']].head(10))
# Additional filtering by junction read support
# Require at least 10 reads supporting each junction type
significant = significant[
(significant['IJC_SAMPLE_1'].str.split(',').apply(lambda x: min(map(int, x))) >= 10) |
(significant['SJC_SAMPLE_1'].str.split(',').apply(lambda x: min(map(int, x))) >= 10)
]
Goal: Identify differential splicing from transcript quantification without alignment.
Approach: Compare per-event PSI distributions between conditions using SUPPA2 empirical p-value calculation.
import subprocess
# Requires PSI files from suppa.py psiPerEvent
# TPM file with samples from both conditions
# Run differential splicing
subprocess.run([
'suppa.py', 'diffSplice',
'-m', 'empirical', # Empirical p-value calculation
'-i', 'events_SE_strict.ioe',
'-p', 'condition1.psi', 'condition2.psi',
'-e', 'condition1.tpm', 'condition2.tpm',
'-o', 'diff_SE'
], check=True)
# Load results
import pandas as pd
diff = pd.read_csv('diff_SE.dpsi', sep='\t', index_col=0)
# SUPPA2 tends to be more stringent
significant = diff[
(diff['p-value'] < 0.05) &
(diff['dPSI'].abs() > 0.1)
]
Goal: Detect differential intron usage without relying on transcript annotation.
Approach: Extract junctions from BAMs, cluster introns by shared splice sites, then test differential usage between groups.
library(leafcutter)
# Convert BAMs to junction files
# leafcutter_bam_to_junc.sh uses regtools
system('for bam in *.bam; do
regtools junctions extract -a 8 -m 50 -s 0 $bam -o ${bam%.bam}.junc
done')
# Create junction file list
writeLines(list.files(pattern = '\\.junc$'), 'juncfiles.txt')
# Cluster introns
system('python leafcutter_cluster_regtools.py -j juncfiles.txt -o leafcutter')
# Run differential analysis
groups <- data.frame(
sample = c('sample1', 'sample2', 'sample3', 'sample4'),
group = c('control', 'control', 'treatment', 'treatment')
)
write.table(groups, 'groups.txt', sep = '\t', quote = FALSE, row.names = FALSE)
# Differential intron usage
system('leafcutter_ds.R --num_threads 4 leafcutter_perind_numers.counts.gz groups.txt')
| Stringency | deltaPSI | FDR | Use Case |
|---|---|---|---|
| Lenient | > 0.1 | < 0.05 | Discovery, exploratory |
| Standard | > 0.15 | < 0.05 | Publication |
| Stringent | > 0.2 | < 0.01 | High-confidence set |
Goal: Rank differential splicing events by combined statistical and biological significance.
Approach: Compute a composite score from FDR and effect size, then select top-scoring events for follow-up.
# Prioritize by effect size and significance
significant['score'] = -np.log10(significant['FDR']) * significant['IncLevelDifference'].abs()
top_events = significant.nlargest(50, 'score')
# Annotate with gene function
# Consider protein domain disruption, NMD sensitivity