t检验python程序_Python批量做T检验

接上一篇代谢组数据，简单的写了一个T检验的程序，若需要优化，望告知！

#导入python统计包

#import numpy as np

import scipy.stats as stats

#import scipy.optimize as opt

#打开文件

f1 = open("./metabolites.txt", "r")

#a代表追加,不覆盖

f2 = open("./T.txt", "a")

#按照行读取

lines = f1.readlines()

#每行进行读取

i = 1

j = 7

for line in lines [1:]:

LINE_1 = line.strip().split("\t")

for i in range(1, len(LINE), 3):

G1 = LINE_1[i:i+3]

G2 = LINE_1[i+6:i+9]

#转换为浮点数列表

X = [float(f) for f in G1]

Y = [float(y) for y in G2]

#显著相关及P值

#r, p = stats.pearsonr(X, Y)

#T检验,若样本大小、方差与比较组不同，在运用t检验时需要使用Welch's t-test

#即指定ttest_ind中的equal_var=False。

stat_var, p_var = stats.ttest_ind(X, Y, equal_var=False)

print("%s %d %d: %f,%f"%(LINE_1[0], i, i+6, stat_var, p_var))

#print("%s %d %d: %f,%f"%(LINE_1[0],i,i+6,r,p))

#print(X, Y)

#Results_T = LINE[0] + " "+str(i)+" "+str(i+6)+ ":" + str(stat_var) + "," + str(p_var)

f2.write("%s %d %d: %f,%f\n"%(LINE_1[0], i, i+6, stat_var, p_var))

if i >= 28:

break

f1.close()

f2.close()