bio-single-cell-splicing

name	bio-single-cell-splicing
description	Analyzes alternative splicing at single-cell resolution using BRIE2 for probabilistic PSI estimation or leafcutter2 for cluster-based analysis with NMD detection. Identifies cell-type-specific splicing patterns. Use when analyzing isoform usage in scRNA-seq or finding splicing differences between cell populations.
tool_type	python
primary_tool	BRIE2

Version Compatibility

Reference examples tested with: anndata 0.10+, numpy 1.26+, pandas 2.2+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Single-Cell Splicing Analysis

Analyze alternative splicing at single-cell resolution.

Tool Selection

Tool	Approach	Strengths
BRIE2	Probabilistic PSI	Handles sparsity, regulatory features
leafcutter2	Intron clustering	NMD detection, novel junctions

Note: Avoid Whippet.jl (Julia 1.6.7 only, incompatible with Julia 1.9+)

BRIE2 Analysis

Goal: Estimate per-cell PSI values for splicing events with uncertainty quantification.

Approach: Prepare splicing events from annotation, count reads per cell barcode, then fit a Bayesian variational inference model for probabilistic PSI estimation.

"Analyze splicing in single-cell data" -> Estimate per-cell inclusion levels for splicing events with uncertainty.

• Python: BRIE2 (probabilistic PSI, handles sparsity)
• Python/R: leafcutter2 (intron clustering, NMD detection)

import brie
import scanpy as sc
import anndata as ad

# Load single-cell data
adata = sc.read_h5ad('scrnaseq.h5ad')

# Prepare splicing events from annotation
# BRIE2 uses pre-defined splicing events
brie.preprocessing.get_events(
    gtf_file='annotation.gtf',
    out_file='splicing_events.gff3'
)

# Count reads for splicing events from BAM files
# Requires cell barcodes and UMIs
brie.preprocessing.count(
    bam_file='possorted_genome_bam.bam',
    gff_file='splicing_events.gff3',
    out_dir='brie_counts/',
    cell_file='barcodes.tsv'  # Filtered cell barcodes
)

# Load BRIE count data
adata_splice = brie.read_h5ad('brie_counts/brie_count.h5ad')

# Run BRIE2 model for PSI estimation
# Uses variational inference for probabilistic estimates
brie.fit(
    adata_splice,
    layer='raw',
    n_epochs=400,
    batch_size=512
)

# PSI estimates stored in adata_splice.layers['Psi']
# Uncertainty in adata_splice.layers['Psi_var']

Cell-Type Specific Splicing

Goal: Identify splicing events that vary between cell types.

Approach: Compute mean PSI per cell type from BRIE2 output and rank events by cross-cell-type variance.

import numpy as np
import pandas as pd

# Add cell type annotations
adata_splice.obs['cell_type'] = adata.obs['cell_type']

# Calculate mean PSI per cell type
cell_types = adata_splice.obs['cell_type'].unique()
psi_matrix = adata_splice.layers['Psi']

mean_psi = pd.DataFrame(index=adata_splice.var_names)
for ct in cell_types:
    mask = adata_splice.obs['cell_type'] == ct
    mean_psi[ct] = np.nanmean(psi_matrix[mask, :], axis=0)

# Find cell-type specific splicing events
# Events with high variance across cell types
psi_var = mean_psi.var(axis=1)
variable_events = psi_var.nlargest(100)
print('Top variable splicing events:')
print(variable_events)

leafcutter2 Analysis

Goal: Detect differential intron usage in single-cell data with NMD-inducing splicing detection.

Approach: Extract junctions from 10X BAMs with cell barcodes, cluster introns, and run differential analysis between cell groups.

import subprocess

# leafcutter2 (April 2025): Adds NMD-inducing splicing detection

# Step 1: Extract junctions from BAM
# Works with 10X BAMs with cell barcodes
subprocess.run([
    'python', 'scripts/bam2junc.py',
    '-b', 'possorted_genome_bam.bam',
    '-o', 'junctions/',
    '--cb_tag', 'CB',  # Cell barcode tag
    '--umi_tag', 'UB'   # UMI tag
], check=True)

# Step 2: Cluster introns
subprocess.run([
    'python', 'clustering/leafcutter_cluster.py',
    '-j', 'junction_files.txt',
    '-o', 'leafcutter_sc',
    '-m', '10',  # Min reads per junction
    '-l', '500000'  # Max intron length
], check=True)

# Step 3: Differential splicing between clusters
# Pseudobulk approach for statistical power
subprocess.run([
    'Rscript', 'scripts/leafcutter_ds.R',
    'leafcutter_sc_perind_numers.counts.gz',
    'groups.txt',
    '-o', 'differential_splicing',
    '-e', 'annotation_exons.txt.gz'
], check=True)

Pseudobulk Approach

Goal: Increase statistical power for splicing analysis by aggregating single cells into pseudobulk samples.

Approach: Sum junction counts within cell type groups, then apply bulk differential splicing methods to the aggregated counts.

import pandas as pd
import numpy as np

# For better statistical power, aggregate cells by type
def pseudobulk_junctions(junction_counts, cell_metadata, groupby='cell_type'):
    '''Aggregate junction counts by cell group.'''
    groups = cell_metadata.groupby(groupby).groups

    pseudobulk = {}
    for group, cells in groups.items():
        cell_mask = junction_counts.index.isin(cells)
        pseudobulk[group] = junction_counts.loc[cell_mask].sum()

    return pd.DataFrame(pseudobulk)

# Run differential splicing on pseudobulk
# Use leafcutter or rMATS on aggregated counts

Interpretation Considerations

Challenge	Mitigation
Sparse data	BRIE2 probabilistic model, pseudobulk
Low reads per cell	Aggregate similar cells
3' bias (10X)	Use 5' kit or full-length methods
Doublets	Filter before splicing analysis

Quality Thresholds

Metric	Recommendation
Min cells per event	>= 50 with reads
Min reads per junction	>= 5 per cell with coverage
PSI confidence	Variance < 0.1

Related Skills

• single-cell/preprocessing - QC before splicing analysis
• single-cell/clustering - Cell type annotation
• splicing-quantification - Bulk RNA-seq comparison