Version Compatibility

Reference examples tested with: ggplot2 3.5+, pandas 2.2+

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

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

Sashimi Plot Visualization

Create sashimi plots to visualize splicing events with read coverage and junction counts.

ggsashimi Usage

Goal: Generate sashimi plots showing read coverage and junction counts for a genomic region.

Approach: Define sample groupings in a TSV file, then run ggsashimi with genomic coordinates and annotation.

"Visualize a splicing event" -> Plot RNA-seq coverage tracks with splice junction arcs grouped by condition.

• Python/CLI: ggsashimi.py (ggsashimi)
• CLI: rmats2sashimiplot (rMATS-specific)

import subprocess
import pandas as pd

# Create sample grouping file (TSV: path, group, color)
groups = pd.DataFrame({
    'bam': ['sample1.bam', 'sample2.bam', 'sample3.bam', 'sample4.bam'],
    'group': ['control', 'control', 'treatment', 'treatment'],
    'color': ['#1f77b4', '#1f77b4', '#ff7f0e', '#ff7f0e']
})
groups.to_csv('sashimi_groups.tsv', sep='\t', index=False, header=False)

# Basic sashimi plot for a region
subprocess.run([
    'ggsashimi.py',
    '-b', 'sashimi_groups.tsv',
    '-c', 'chr1:1000000-1010000',  # Genomic coordinates
    '-o', 'sashimi_output',
    '-M', '10',  # Minimum junction reads to show
    '--alpha', '0.25',  # Coverage transparency
    '--height', '3',
    '--width', '8',
    '-g', 'annotation.gtf'
], check=True)

Batch Plotting Significant Events

Goal: Automatically generate sashimi plots for all significant differential splicing events.

Approach: Load rMATS results, filter for significant events, extract flanking coordinates, and iterate ggsashimi over each event.

import subprocess
import pandas as pd

# Load differential splicing results
diff_results = pd.read_csv('rmats_output/SE.MATS.JC.txt', sep='\t')
significant = diff_results[
    (diff_results['FDR'] < 0.05) &
    (diff_results['IncLevelDifference'].abs() > 0.1)
]

# Generate plots for top events
for idx, event in significant.head(20).iterrows():
    chrom = event['chr']
    # Extend region around the exon
    start = event['upstreamES'] - 500
    end = event['downstreamEE'] + 500
    region = f'{chrom}:{start}-{end}'
    gene = event['geneSymbol']

    subprocess.run([
        'ggsashimi.py',
        '-b', 'sashimi_groups.tsv',
        '-c', region,
        '-o', f'sashimi_plots/{gene}_{chrom}_{start}',
        '-M', '5',
        '--shrink',  # Shrink introns for better visualization
        '-g', 'annotation.gtf',
        '--fix-y-scale'  # Same y-axis across groups
    ], check=True)

rmats2sashimiplot

Goal: Create sashimi plots directly from rMATS differential splicing output.

Approach: Point rmats2sashimiplot at rMATS result files and BAM groups with condition labels.

# For rMATS output specifically
rmats2sashimiplot \
    --b1 sample1.bam,sample2.bam \
    --b2 sample3.bam,sample4.bam \
    -t SE \
    -e rmats_output/SE.MATS.JC.txt \
    --l1 Control \
    --l2 Treatment \
    -o sashimi_rmats \
    --exon_s 1 \
    --intron_s 5

Customization Options

Goal: Fine-tune sashimi plot appearance for publication-quality figures.

Approach: Adjust ggsashimi visual parameters including intron shrinking, y-axis scaling, aggregation mode, and output format.

# Advanced ggsashimi options
subprocess.run([
    'ggsashimi.py',
    '-b', 'sashimi_groups.tsv',
    '-c', 'chr1:1000000-1010000',
    '-o', 'custom_sashimi',
    '-g', 'annotation.gtf',

    # Visual options
    '-M', '10',           # Min junction reads
    '--alpha', '0.25',    # Coverage alpha
    '--height', '3',      # Plot height per track
    '--width', '10',      # Plot width
    '--base-size', '14',  # Font size

    # Layout options
    '--shrink',           # Shrink introns
    '--fix-y-scale',      # Same y-axis
    '-A', 'mean',         # Aggregate: mean, median, or none

    # Annotation options
    '--gtf-filter', 'protein_coding',  # Filter GTF features

    # Output format
    '-F', 'pdf'           # pdf, png, svg, eps
], check=True)

Best Practices

Tip	Rationale
Use --shrink for large introns	Keeps exons visible
Set --fix-y-scale for comparisons	Fair visual comparison
Aggregate replicates with -A mean	Reduces clutter
Limit to 3-4 groups	More groups become hard to read
Include flanking exons	Show full splicing context

Troubleshooting

Issue	Solution
No junctions shown	Lower -M threshold
Plot too crowded	Use --shrink, reduce samples
Annotation missing	Check GTF format, gene name field
Memory issues	Plot smaller regions

Related Skills

• differential-splicing - Identify events to plot
• splicing-quantification - Context for PSI values
• data-visualization/ggplot2-fundamentals - Further customization