Plotting PCA¶

In [1]:

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

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

loading files¶

In [2]:

pca = pd.read_table("../05_PCA/plink_results_projected.sscore",sep="\t")
pca

pca = pd.read_table("../05_PCA/plink_results_projected.sscore",sep="\t") pca

Out[2]:

	#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
0	HG00403	HG00403	390256	390256	0.002903	-0.024865	0.010041	0.009576	0.006943	-0.002223	0.008223	-0.001149	0.003352	0.004375
1	HG00404	HG00404	390696	390696	-0.000141	-0.027965	0.025389	-0.005825	-0.002747	0.006585	0.011380	0.007777	0.015998	0.017893
2	HG00406	HG00406	388524	388524	0.007074	-0.031545	-0.004370	-0.001262	-0.011493	-0.005395	-0.006202	0.004524	-0.000871	-0.002280
3	HG00407	HG00407	388808	388808	0.006840	-0.025073	-0.006527	0.006797	-0.011600	-0.010233	0.013957	0.006187	0.013806	0.008253
4	HG00409	HG00409	391646	391646	0.000399	-0.029033	-0.018935	-0.001360	0.029044	0.009428	-0.017119	-0.012964	0.025360	0.022907
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
495	NA19087	NA19087	390232	390232	-0.082261	0.033163	0.045499	-0.011398	0.000027	-0.006525	0.012446	-0.006743	-0.016312	0.023022
496	NA19088	NA19088	391510	391510	-0.087183	0.043433	0.040188	0.003610	-0.000165	0.002317	0.000117	0.007430	-0.011886	0.007730
497	NA19089	NA19089	391462	391462	-0.084082	0.036118	-0.036355	0.008738	-0.037523	0.004110	0.008653	-0.000563	-0.001599	0.015941
498	NA19090	NA19090	392880	392880	-0.073580	0.026163	-0.032193	0.006599	-0.039060	0.000687	0.012213	-0.000485	-0.000336	-0.031283
499	NA19091	NA19091	389664	389664	-0.081632	0.041455	-0.032200	0.003717	-0.046712	0.015191	0.003119	-0.004906	-0.001811	-0.020752

500 rows × 14 columns

In [6]:

ped = pd.read_table("../01_Dataset/integrated_call_samples_v3.20130502.ALL.panel",sep="\t")
ped

ped = pd.read_table("../01_Dataset/integrated_call_samples_v3.20130502.ALL.panel",sep="\t") ped

Out[6]:

	sample	pop	super_pop	gender	Unnamed: 4	Unnamed: 5
0	HG00096	GBR	EUR	male	NaN	NaN
1	HG00097	GBR	EUR	female	NaN	NaN
2	HG00099	GBR	EUR	female	NaN	NaN
3	HG00100	GBR	EUR	female	NaN	NaN
4	HG00101	GBR	EUR	male	NaN	NaN
...	...	...	...	...	...	...
2499	NA21137	GIH	SAS	female	NaN	NaN
2500	NA21141	GIH	SAS	female	NaN	NaN
2501	NA21142	GIH	SAS	female	NaN	NaN
2502	NA21143	GIH	SAS	female	NaN	NaN
2503	NA21144	GIH	SAS	female	NaN	NaN

2504 rows × 6 columns

Merge PCA and population information¶

In [7]:

pcaped=pd.merge(pca,ped,right_on="sample",left_on="IID",how="inner")
pcaped

pcaped=pd.merge(pca,ped,right_on="sample",left_on="IID",how="inner") pcaped

Out[7]:

	#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
0	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
1	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
2	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
3	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
4	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
495	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
496	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
497	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
498	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
499	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

500 rows × 20 columns

Plotting¶

In [8]:

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

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

Out[8]:

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