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Plotting PCA

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

loading files

pca = pd.read_table("../05_PCA/plink_results_projected.sscore",sep="\t")
pca
#FID IID ALLELE_CT NAMED_ALLELE_DOSAGE_SUM PC1_AVG PC2_AVG PC3_AVG PC4_AVG PC5_AVG PC6_AVG PC7_AVG PC8_AVG PC9_AVG PC10_AVG
HG00403 HG00403 390256 390256 0.002903 -0.024865 0.010041 0.009576 0.006943 -0.002223 0.008223 -0.001149 0.003352 0.004375
HG00404 HG00404 390696 390696 -0.000141 -0.027965 0.025389 -0.005825 -0.002747 0.006585 0.011380 0.007777 0.015998 0.017893
HG00406 HG00406 388524 388524 0.007074 -0.031545 -0.004370 -0.001262 -0.011493 -0.005395 -0.006202 0.004524 -0.000871 -0.002280
HG00407 HG00407 388808 388808 0.006840 -0.025073 -0.006527 0.006797 -0.011600 -0.010233 0.013957 0.006187 0.013806 0.008253
HG00409 HG00409 391646 391646 0.000399 -0.029033 -0.018935 -0.001360 0.029044 0.009428 -0.017119 -0.012964 0.025360 0.022907
... ... ... ... ... ... ... ... ... ... ... ... ... ...
NA19087 NA19087 390232 390232 -0.082261 0.033163 0.045499 -0.011398 0.000027 -0.006525 0.012446 -0.006743 -0.016312 0.023022
NA19088 NA19088 391510 391510 -0.087183 0.043433 0.040188 0.003610 -0.000165 0.002317 0.000117 0.007430 -0.011886 0.007730
NA19089 NA19089 391462 391462 -0.084082 0.036118 -0.036355 0.008738 -0.037523 0.004110 0.008653 -0.000563 -0.001599 0.015941
NA19090 NA19090 392880 392880 -0.073580 0.026163 -0.032193 0.006599 -0.039060 0.000687 0.012213 -0.000485 -0.000336 -0.031283
NA19091 NA19091 389664 389664 -0.081632 0.041455 -0.032200 0.003717 -0.046712 0.015191 0.003119 -0.004906 -0.001811 -0.020752 
ped = pd.read_table("../01_Dataset/integrated_call_samples_v3.20130502.ALL.panel",sep="\t")
ped
sample pop super_pop gender Unnamed: 4 Unnamed: 5
HG00096 GBR EUR male NaN NaN
HG00097 GBR EUR female NaN NaN
HG00099 GBR EUR female NaN NaN
HG00100 GBR EUR female NaN NaN
HG00101 GBR EUR male NaN NaN
... ... ... ... ... ...
NA21137 GIH SAS female NaN NaN
NA21141 GIH SAS female NaN NaN
NA21142 GIH SAS female NaN NaN
NA21143 GIH SAS female NaN NaN
NA21144 GIH SAS female NaN NaN

Merge PCA and population information

pcaped=pd.merge(pca,ped,right_on="sample",left_on="IID",how="inner")
pcaped
#FID IID ALLELE_CT NAMED_ALLELE_DOSAGE_SUM PC1_AVG PC2_AVG PC3_AVG PC4_AVG PC5_AVG PC6_AVG PC7_AVG PC8_AVG PC9_AVG PC10_AVG sample pop super_pop gender Unnamed: 4 Unnamed: 5
HG00403 HG00403 390256 390256 0.002903 -0.024865 0.010041 0.009576 0.006943 -0.002223 0.008223 -0.001149 0.003352 0.004375 HG00403 CHS EAS male NaN NaN
HG00404 HG00404 390696 390696 -0.000141 -0.027965 0.025389 -0.005825 -0.002747 0.006585 0.011380 0.007777 0.015998 0.017893 HG00404 CHS EAS female NaN NaN
HG00406 HG00406 388524 388524 0.007074 -0.031545 -0.004370 -0.001262 -0.011493 -0.005395 -0.006202 0.004524 -0.000871 -0.002280 HG00406 CHS EAS male NaN NaN
HG00407 HG00407 388808 388808 0.006840 -0.025073 -0.006527 0.006797 -0.011600 -0.010233 0.013957 0.006187 0.013806 0.008253 HG00407 CHS EAS female NaN NaN
HG00409 HG00409 391646 391646 0.000399 -0.029033 -0.018935 -0.001360 0.029044 0.009428 -0.017119 -0.012964 0.025360 0.022907 HG00409 CHS EAS male NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
NA19087 NA19087 390232 390232 -0.082261 0.033163 0.045499 -0.011398 0.000027 -0.006525 0.012446 -0.006743 -0.016312 0.023022 NA19087 JPT EAS female NaN NaN
NA19088 NA19088 391510 391510 -0.087183 0.043433 0.040188 0.003610 -0.000165 0.002317 0.000117 0.007430 -0.011886 0.007730 NA19088 JPT EAS male NaN NaN
NA19089 NA19089 391462 391462 -0.084082 0.036118 -0.036355 0.008738 -0.037523 0.004110 0.008653 -0.000563 -0.001599 0.015941 NA19089 JPT EAS male NaN NaN
NA19090 NA19090 392880 392880 -0.073580 0.026163 -0.032193 0.006599 -0.039060 0.000687 0.012213 -0.000485 -0.000336 -0.031283 NA19090 JPT EAS female NaN NaN
NA19091 NA19091 389664 389664 -0.081632 0.041455 -0.032200 0.003717 -0.046712 0.015191 0.003119 -0.004906 -0.001811 -0.020752 NA19091 JPT EAS male NaN NaN

Plotting

plt.figure(figsize=(10,10))
sns.scatterplot(data=pcaped,x="PC1_AVG",y="PC2_AVG",hue="pop",s=50)

<Axes: xlabel='PC1_AVG', ylabel='PC2_AVG'>

Output image