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)

python
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.

python
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.

bash
# 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.

python
# 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