mafViper 0.2

A Python package for processing, analyzing and visualizing MAF files.

mafViper

A Python package for processing, analyzing and visualizing Mutation Annotation Format (MAF) files in genomic research.

Installation

pip install mafviper

Modules and Functions

1. MAF File Reading (read_maf)

from mafviper import read_maf

maf_dataframe = read_maf(
    maf_file='path/to/your/file.maf', 
    skip_rows_until='Hugo_Symbol', 
    sep='\t', 
    use_all=True
)

Parameters:

• maf_file (str): Path to the MAF file
• skip_rows_until (str, optional): Column name to start reading from. Default is 'Hugo_Symbol'
• sep (str, optional): Delimiter used in the MAF file. Default is tab ('\t')
• use_all (bool, optional):
  • If True (default): Includes all mutation types
  • If False: Only keeps variants with 'Mutation_Status' as 'Somatic'

Returns:

• Pandas DataFrame containing validated MAF data

2. MAF Processing (classify_variants, merge_mafs, get_titv)

from mafviper import classify_variants, merge_mafs, get_titv

# Classify variants
non_synonymous_maf = classify_variants(
    maf, 
    discard_synonymous=True
)

# Merge multiple MAF files
merged_maf = merge_mafs(
    maf_list=[maf1, maf2, maf3], 
    discard_synonymous=False
)

# Generate Ti/Tv classification
titv_summary = get_titv(maf_df)

classify_variants

• maf: Input MAF DataFrame
• discard_synonymous (bool, optional):
  • If True: Removes synonymous mutations
  • If False: Keeps all mutation types

merge_mafs

• maf_list: List of MAF DataFrames to merge
• discard_synonymous (bool, optional):
  • If True: Removes synonymous mutations after merging
  • If False: Keeps all mutation types

get_titv

• maf_df: MAF DataFrame with SNP variants
• Returns a summary DataFrame of Transition/Transversion (Ti/Tv) classifications

3. Visualization (get_maf_summary)

from mafviper import get_maf_summary

get_maf_summary(
    maf_df,
    output_pdf='MAF_Summary_Dashboard.pdf',
    add_stat=None,
    show_barcodes=False,
    top_genes=10,
    raw_count=True,
    nonsyn_titv=False,
    use_nonsyn=True
)

Parameters:

• maf_df: Input MAF DataFrame
• output_pdf (str, optional): Output PDF filename. Default is 'MAF_Summary_Dashboard.pdf'
• add_stat (str, optional): Default is median
  • 'mean': Add mean mutation count line
  • 'median': Add median mutation count line
• show_barcodes (bool, optional): Display sample barcodes
• top_genes (int, optional): Number of top mutated genes to display. Default is 10
• raw_count (bool, optional):
  • If True: Display raw mutation counts
  • If False: Display mutation proportions
• nonsyn_titv (bool, optional):
  • If True: Calculate Ti/Tv using non-synonymous mutations
  • If False: Calculate Ti/Tv using all mutations
• use_nonsyn (bool, optional):
  • If True: Use only non-synonymous mutations for summary
  • If False: Use all mutations for summary

Example Workflow

from mafviper import read_maf, merge_mafs, get_maf_summary

# Read MAF files
maf1 = read_maf('sample1.maf')
maf2 = read_maf('sample2.maf')

# Merge MAF files
merged_maf = merge_mafs([maf1, maf2])

# Generate visualization
get_maf_summary(
    merged_maf, 
    output_pdf='my_maf_summary.pdf', 
    top_genes=15, 
    add_stat='mean'
)

Requirements

• pandas
• seaborn
• matplotlib

License

Apache 2.0

Author

Joseph Rosen