【ADHD血液診断】次元削減

import time
import pickle

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from scipy.stats import pearsonr
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import umap
from scipy.sparse.csgraph import connected_components

import warnings
warnings.filterwarnings('ignore')


df_sample = pd.read_csv("src/GSE159104_samples.txt", sep="\t")
df_sample

table = {}

for i in range(df_sample.shape[0]):
    if df_sample["ADHD_Status"][i] == "CTRL":
        table[df_sample["Channel"][i]] = 0
    else:
        table[df_sample["Channel"][i]] = 1
        
        
v = [table[s] for s in df_std.index.tolist()]
df_xy = df_std
df_xy["y"] = v


df_xy

x = df_xy.iloc[:, :-1]
x_scaled = (x - x.mean()) / x.std()

pca = PCA()
pca.fit(x_scaled)

score = pd.DataFrame(pca.transform(x_scaled), index=x_scaled.index)

plt.scatter(score.iloc[:, 0], score.iloc[:, 1], c=df_xy.iloc[:, -1], cmap=plt.get_cmap('jet'))
clb = plt.colorbar()
clb.set_label('ADHD Status', labelpad=-20, y=1.1, rotation=0)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.show()

# 寄与率
contribution_ratios = pd.DataFrame(pca.explained_variance_ratio_)
cumulative_contribution_ratios = contribution_ratios.cumsum()

cont_cumcont_ratios = pd.concat([contribution_ratios, cumulative_contribution_ratios], axis=1).T
cont_cumcont_ratios.index = ['contribution_ratio', 'cumulative_contribution_ratio']  # 行の名前を変更
# 寄与率を棒グラフで、累積寄与率を線で入れたプロット図を重ねて描画
x_axis = range(1, contribution_ratios.shape[0] + 1)  # 1 から成分数までの整数が x 軸の値
plt.rcParams['font.size'] = 18
plt.bar(x_axis, contribution_ratios.iloc[:, 0], align='center')  # 寄与率の棒グラフ
plt.plot(x_axis, cumulative_contribution_ratios.iloc[:, 0], 'r.-')  # 累積寄与率の線を入れたプロット図
plt.xlabel('Number of principal components')  # 横軸の名前
plt.ylabel('Contribution ratio(blue),\nCumulative contribution ratio(red)')  # 縦軸の名前。\n で改行しています
plt.show()

tsne = TSNE(n_components=2, random_state=42)
corrdinates = pd.DataFrame(tsne.fit_transform(x_scaled))

plt.scatter(corrdinates.iloc[:, 0], corrdinates.iloc[:, 1], c=df_xy.iloc[:, -1], cmap=plt.get_cmap('jet'))
clb = plt.colorbar()
clb.set_label('ADHD Status', labelpad=-20, y=1.1, rotation=0)
plt.xlabel('tSNE 1')
plt.ylabel('tSNE 2')
plt.show()

embedding = pd.DataFrame(umap.UMAP().fit_transform(x_scaled))

plt.scatter(embedding.iloc[:, 0], embedding.iloc[:, 1], c=df_xy.iloc[:, -1], cmap=plt.get_cmap('jet'))
clb = plt.colorbar()
clb.set_label('ADHD Status', labelpad=-20, y=1.1, rotation=0)
plt.xlabel('UMAP 1')
plt.ylabel('UMAP 2')
plt.show()