pandas plink 다운로드 - pandas plink 소스 코드 다운로드

pandas plink

기타 소스코드

다운로드

팬더 plink

Pandas-plink는 PLINK 바이너리 파일 형식과 실현된 관계 행렬(PLINK 또는 GCTA)을 읽기 위한 Python 패키지입니다. 파일 읽기는 지연 로딩을 통해 수행됩니다. 즉, 사용자가 실제로 액세스하는 유전자형만 실제로 읽어 메모리를 절약합니다.

주목할만한 변경 사항은 CHANGELOG.md에서 확인할 수 있습니다.

설치하다

pip를 사용하여 설치할 수 있습니다.

pip install pandas-plink

또는 conda를 통해 설치할 수도 있습니다.

conda install -c conda-forge pandas-plink

용법

그것은 다음과 같이 간단합니다

 >> > from pandas_plink import read_plink1_bin
>> > G = read_plink1_bin ( "chr11.bed" , "chr11.bim" , "chr11.fam" , verbose = False )
>> > print ( G )
< xarray . DataArray 'genotype' ( sample : 14 , variant : 779 ) >
dask . array < shape = ( 14 , 779 ), dtype = float64 , chunksize = ( 14 , 779 ) >
Coordinates :
  * sample   ( sample ) object 'B001' 'B002' 'B003' ... 'B012' 'B013' 'B014'
  * variant  ( variant ) object '11_316849996' '11_316874359' ... '11_345698259'
    father   ( sample ) < U1 '0' '0' '0' '0' '0' '0' ... '0' '0' '0' '0' '0' '0'
    fid      ( sample ) < U4 'B001' 'B002' 'B003' 'B004' ... 'B012' 'B013' 'B014'
    gender   ( sample ) < U1 '0' '0' '0' '0' '0' '0' ... '0' '0' '0' '0' '0' '0'
    i        ( sample ) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13
    iid      ( sample ) < U4 'B001' 'B002' 'B003' 'B004' ... 'B012' 'B013' 'B014'
    mother   ( sample ) < U1 '0' '0' '0' '0' '0' '0' ... '0' '0' '0' '0' '0' '0'
    trait    ( sample ) < U2 '-9' '-9' '-9' '-9' '-9' ... '-9' '-9' '-9' '-9' '-9'
    a0       ( variant ) < U1 'C' 'G' 'G' 'C' 'C' 'T' ... 'T' 'A' 'C' 'A' 'A' 'T'
    a1       ( variant ) < U1 'T' 'C' 'C' 'T' 'T' 'A' ... 'C' 'G' 'T' 'G' 'C' 'C'
    chrom    ( variant ) < U2 '11' '11' '11' '11' '11' ... '11' '11' '11' '11' '11'
    cm       ( variant ) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    pos      ( variant ) int64 157439 181802 248969 ... 28937375 28961091 29005702
    snp      ( variant ) < U9 '316849996' '316874359' ... '345653648' '345698259'
>> > print ( G . sel ( sample = "B003" , variant = "11_316874359" ). values )
0.0
>> > print ( G . a0 . sel ( variant = "11_316874359" ). values )
G
>> > print ( G . sel ( sample = "B003" , variant = "11_316941526" ). values )
2.0
>> > print ( G . a1 . sel ( variant = "11_316941526" ). values )
C

사용자가 유전자형에 액세스하면 유전자형의 일부가 읽혀집니다.

공분산 행렬은 매우 쉽게 읽을 수도 있습니다. 예:

 >> > from pandas_plink import read_rel
>> > K = read_rel ( "plink2.rel.bin" )
>> > print ( K )
< xarray . DataArray ( sample_0 : 10 , sample_1 : 10 ) >
array ([[ 0.885782 ,  0.233846 , - 0.186339 , - 0.009789 , - 0.138897 ,  0.287779 ,
         0.269977 , - 0.231279 , - 0.095472 , - 0.213979 ],
       [ 0.233846 ,  1.077493 , - 0.452858 ,  0.192877 , - 0.186027 ,  0.171027 ,
         0.406056 , - 0.013149 , - 0.131477 , - 0.134314 ],
       [ - 0.186339 , - 0.452858 ,  1.183312 , - 0.040948 , - 0.146034 , - 0.204510 ,
        - 0.314808 , - 0.042503 ,  0.296828 , - 0.011661 ],
       [ - 0.009789 ,  0.192877 , - 0.040948 ,  0.895360 , - 0.068605 ,  0.012023 ,
         0.057827 , - 0.192152 , - 0.089094 ,  0.174269 ],
       [ - 0.138897 , - 0.186027 , - 0.146034 , - 0.068605 ,  1.183237 ,  0.085104 ,
        - 0.032974 ,  0.103608 ,  0.215769 ,  0.166648 ],
       [ 0.287779 ,  0.171027 , - 0.204510 ,  0.012023 ,  0.085104 ,  0.956921 ,
         0.065427 , - 0.043752 , - 0.091492 , - 0.227673 ],
       [ 0.269977 ,  0.406056 , - 0.314808 ,  0.057827 , - 0.032974 ,  0.065427 ,
         0.714746 , - 0.101254 , - 0.088171 , - 0.063964 ],
       [ - 0.231279 , - 0.013149 , - 0.042503 , - 0.192152 ,  0.103608 , - 0.043752 ,
        - 0.101254 ,  1.423033 , - 0.298255 , - 0.074334 ],
       [ - 0.095472 , - 0.131477 ,  0.296828 , - 0.089094 ,  0.215769 , - 0.091492 ,
        - 0.088171 , - 0.298255 ,  0.910274 , - 0.024663 ],
       [ - 0.213979 , - 0.134314 , - 0.011661 ,  0.174269 ,  0.166648 , - 0.227673 ,
        - 0.063964 , - 0.074334 , - 0.024663 ,  0.914586 ]])
Coordinates :
  * sample_0  ( sample_0 ) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'
  * sample_1  ( sample_1 ) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'
    fid       ( sample_1 ) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'
    iid       ( sample_1 ) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'
>> > print ( K . values )
[[ 0.89  0.23 - 0.19 - 0.01 - 0.14  0.29  0.27 - 0.23 - 0.10 - 0.21 ]
 [ 0.23  1.08 - 0.45  0.19 - 0.19  0.17  0.41 - 0.01 - 0.13 - 0.13 ]
 [ - 0.19 - 0.45  1.18 - 0.04 - 0.15 - 0.20 - 0.31 - 0.04  0.30 - 0.01 ]
 [ - 0.01  0.19 - 0.04  0.90 - 0.07  0.01  0.06 - 0.19 - 0.09  0.17 ]
 [ - 0.14 - 0.19 - 0.15 - 0.07  1.18  0.09 - 0.03  0.10  0.22  0.17 ]
 [ 0.29  0.17 - 0.20  0.01  0.09  0.96  0.07 - 0.04 - 0.09 - 0.23 ]
 [ 0.27  0.41 - 0.31  0.06 - 0.03  0.07  0.71 - 0.10 - 0.09 - 0.06 ]
 [ - 0.23 - 0.01 - 0.04 - 0.19  0.10 - 0.04 - 0.10  1.42 - 0.30 - 0.07 ]
 [ - 0.10 - 0.13  0.30 - 0.09  0.22 - 0.09 - 0.09 - 0.30  0.91 - 0.02 ]
 [ - 0.21 - 0.13 - 0.01  0.17  0.17 - 0.23 - 0.06 - 0.07 - 0.02  0.91 ]]