pandas-plink

Pandas-plink is a Python package for reading PLINK binary file format andrealized relationship matrices (PLINK or GCTA). The file reading is taken place via lazy loading, meaning that it saves up memory by actually reading only the genotypes that are actually accessed by the user.

Notable changes can be found at the CHANGELOG.md.

Install

It can be installed using pip:

pip install pandas-plink

Alternatively it can be intalled via conda:

conda install -c conda-forge pandas-plink

Usage

It is as simple as

>>> 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

Portions of the genotype will be read as the user access them.

Covariance matrices can also be read very easily. Example:

>>> 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]]

Please, refer to the pandas-plink documentation for more information.

Authors

• Danilo Horta

License

This project is licensed under the MIT License.