About this article

--A script that plots the results of principal component analysis (PCA) using genetic statistical analysis software PLINK on a two-dimensional plane. Wrote. --Introduce script input / output files and execution method. --The script is here (link to GitHub)

Input file preparation

1. Preparation of main component load data

file format

Prepare a file containing the family ID in the first column, the personal ID in the second column, and the main component load in the third and subsequent columns. A file in such a format can be obtained by performing principal component analysis using PLINK.

#1 FamID
#2 Individual ID
#3 PC1
#4 PC2
...

Principal component analysis by PLINK

Principal component analysis can be performed with the genetic statistical analysis software PLINK. Principal component analysis is a dimensionality reduction method based on the eigendecomposition of the variance-covariance matrix or correlation matrix. It is used for entanglement adjustment.

$ plink --bfile ${bfile_name} --out ${outfile_name} --pca

As a result of PCA output by PLINK, $ {outfile_name} .eigenvec and $ {outfile_name} .eigenval are obtained. To illustrate the results, use $ {outfile_name} .eigenvec (load of each principal component in each individual).

2. Preparation of group label data

file format

Prepare a file with the family ID in the first column, the individual ID in the second column, and the group label (race, etc.) in the third column. (Let's say populations.txt.)

#1 FamID
#2 Individual ID
#3 Group

How to execute the script

The execution environment is Python3, and pandas and matplotlib are installed. Execute by specifying the following options. --Specify a $ {outfile_name} .eigenvec file for the -e option --Specify a populations.txt file for the -p option --Specify the output directory in the -o option

$ python plot_pca_gwas.py -e ${outfile_name}.eigenvec -p populations.txt -o ${output_directory}/

Check the output result

The following image is obtained as the output result of the script. --pca.png: Plot of the entire population --pca_ {group} .png: Plot for each group

Execution example

Input files include example.eigenvec and [example_population.txt](https: / If you run the script using /github.com/t-yui/bioinformatics_scripts/blob/master/gwas_tools/plinkPCA/plot_examples/example_data/example_population.txt), you will get the following image.