pandas_plink-2.0.0.tar.gz

# pandas-plink [](https://travis-ci.com/limix/pandas-plink) [](https://ci.appveyor.com/project/Horta/pandas-plink) [](https://pandas-plink.readthedocs.io/) Pandas-plink is a Python package for reading [PLINK binary file format](https://www.cog-genomics.org/plink2/formats) and (since version 2.0.0) PLINK and GCTA realized relationship matrices. The file reading is taken place via [lazy loading](https://en.wikipedia.org/wiki/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](CHANGELOG.md). ## Install It be installed using [pip](https://pypi.python.org/pypi/pip): ```bash pip install pandas-plink ``` Alternatively it can be intalled via [conda](http://conda.pydata.org/docs/index.html): ```bash conda install -c conda-forge pandas-plink ``` ## Usage It is as simple as ```python >>> 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: ```python >>> 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](https://pandas-plink.readthedocs.io/) for more information. ## Authors * [Danilo Horta](https://github.com/horta) ## License This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/pandas-plink/master/LICENSE.md).