CanDrA input file 생성을 위한 TCGA somatic mutation 파일 파싱하기

CanDrA는 mis-sense mutation의 효과를 예측해 주는 프로그램이다. TCGA의 데이터의 mis-sense 데이터를 CanDrA가 요구하는 입력 파일 형태로 만들기 위해서는 약간의 가공이 필요하다. 아래는 python의 pandas를 이용하여 TCGA 데이터를 가공하는 예시이다.

Parse the TCGA somatic mutation file for generating CanDrA input file

• I use COAD(colon adenocarcinoma) data here.
• The file format is MAF(mutation annotation format).

---

In [1]:

import pandas as pd
from pandas import Series, DataFrame
import numpy as np

In [2]:

print( "pandas: %s"%pd.__version__ )
print( "numpy: %s"%np.__version__ )

pandas: 0.17.0
numpy: 1.10.1

In [3]:

df = pd.read_table("hgsc.bcm.edu__Illumina_Genome_Analyzer_DNA_Sequencing_level2.maf")

In [4]:

df.columns

Out[4]:

Index([u'Hugo_Symbol', u'Entrez_Gene_Id', u'Center', u'Ncbi_Build', u'Chrom',
       u'Start_Position', u'End_Position', u'Strand',
       u'Variant_Classification', u'Variant_Type', u'Reference_Allele',
       u'Tumor_Seq_Allele1', u'Tumor_Seq_Allele2', u'Dbsnp_Rs',
       u'Dbsnp_Val_Status', u'Tumor_Sample_Barcode',
       u'Matched_Norm_Sample_Barcode', u'Match_Norm_Seq_Allele1',
       u'Match_Norm_Seq_Allele2', u'Tumor_Validation_Allele1',
       u'Tumor_Validation_Allele2', u'Match_Norm_Validation_Allele1',
       u'Match_Norm_Validation_Allele2', u'Verification_Status',
       u'Validation_Status', u'Mutation_Status', u'Sequencing_Phase',
       u'Sequence_Source', u'Validation_Method', u'Score', u'Bam_File',
       u'Sequencer', u'Tumor_Sample_UUID', u'Matched_Norm_Sample_UUID',
       u'File_Name', u'Archive_Name', u'Line_Number'],
      dtype='object')

---

The CanDrA page says as follows:

A input file should be in a tab-delimited format. Columns of an input file are

1. chromosome number
2. genomic_coordinate
3. ref_allele
4. mutated_allele
5. strand.
   

More please refer to demo_input.txt in the package

---

In [5]:

# A subset of the DataFrame, specified by the necessary columns for CanDrA input file format
sdf = df[ ['Chrom', 'Start_Position', 'Reference_Allele', 'Tumor_Seq_Allele2', 'Strand'] ]

In [6]:

sdf[:5]

Out[6]:

	Chrom	Start_Position	Reference_Allele	Tumor_Seq_Allele2	Strand
0	19	58861779	G	A	+
1	19	58862886	G	A	+
2	19	58863691	C	T	+
3	19	58863691	C	T	+
4	19	58863717	C	T	+

In [7]:

# Rename the columns with short words
sdf.columns = ['chrom', 'pos', 'ref', 'mut', 'strand']
sdf[:5]

Out[7]:

	chrom	pos	ref	mut	strand
0	19	58861779	G	A	+
1	19	58862886	G	A	+
2	19	58863691	C	T	+
3	19	58863691	C	T	+
4	19	58863717	C	T	+

In [8]:

sdf.replace('-', np.nan, inplace=True)

C:\Users\dwlee\Anaconda\envs\py27\lib\site-packages\pandas\core\common.py:449: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  mask = arr == x
C:\Users\dwlee\Anaconda\envs\py27\lib\site-packages\ipykernel\__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

In [9]:

sdf.dropna(how='any', inplace=True)

C:\Users\dwlee\Anaconda\envs\py27\lib\site-packages\ipykernel\__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

In [10]:

(sdf == np.nan).any()

Out[10]:

chrom     False
pos       False
ref       False
mut       False
strand    False
dtype: bool

In [11]:

sdf.to_csv("candra_input_tcga_coad.txt", sep='\t', index=False)