Doing t-test in batch
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Situation: I want to do t-test between two sets of experiment A and B. Each A and B were run in triplicates, so we have:
data_A_1.dat
data_A_2.dat
data_A_3.dat
data_B_1.dat
data_B_2.dat
data_B_3.dat
How to run t-test between every combination of A and B, as well as all A combined vs all B combined?
Here is the script that I come up with:
import numpy as np
from scipy import stats
import numpy as np
import os
import itertools
import sys
def printf(format, *args):
sys.stdout.write(format % args)
os.chdir("/path/")
expt_A=[
"A_1",
"A_2",
"A_3",
]
expt_B=[
"B_1",
"B_2",
"B_3",
]
# load individual runs
data = dict()
for i in expt_A + expt_B:
with open("data_"+ i +".dat", "r") as f:
data[i] = np.array(f.read().splitlines()).astype(np.float)
# collate 3 runs
for i in ['A','B']:
data[i+'_all']=np.concatenate([data[i+'_1'],
data[i+'_2'],
data[i+'_3']])
# print out t- and p-values
for i,j in list(itertools.product(expt_A, expt_B)) + [('A_all','B_all')]:
print(i,j, end=": ")
t, p = stats.ttest_ind(data[i],data[j])
printf("t = %5.1f, p = %.2f\n", t, p)
The output looks like this (nicely formatted thanks to the printf trick):
A_1 B_1: t = 63.8, p = 0.00
A_1 B_2: t = 93.7, p = 0.00
A_1 B_3: t = 99.7, p = 0.00
A_2 B_1: t = 44.3, p = 0.00
A_2 B_2: t = 71.0, p = 0.00
A_2 B_3: t = 76.6, p = 0.00
A_3 B_1: t = 72.7, p = 0.00
A_3 B_2: t = 104.3, p = 0.00
A_3 B_3: t = 110.5, p = 0.00
A_all B_all: t = 138.0, p = 0.00
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