This script has been used to analysis some gene test result in "tab separated value" format from some lab of ZJU. It saves tons of silly people's labor pasting two rows from two different xlsx to graphpad, click some analysis button and copy result back to the third xlsx. However, there is a drawback of this script: it's not parallel.

py-gene-t-tester.py

#!/usr/bin/env python3
# James' Gene T-tester
# Written by James Swineson <jamesswineson@gmail.com>, 2016-01-27
# All rights reserved.
#
# Install dependencies:
#   pip3 install numpy scipy
#
# Tested to work under Python 3.5.1 on OS X 10.11.3

# import openpyxl
import numpy as np
from scipy.stats import ttest_1samp, ttest_ind

# organized data format
class Data:
    def __init__(this, id=None, name=None, normal_data=None, tumor_data=None):
        this.id = id
        this.name = name
        this.normal_data = normal_data
        this.tumor_data = tumor_data

def p_summary(p):
    # http://graphpad.com/support/faq/what-is-the-meaning-of--or--or--in-reports-of-statistical-significance-from-prism-or-instat/
    # Symbol  Meaning
    # ns	  P > 0.05
    # *	      P ≤ 0.05
    # **	  P ≤ 0.01
    # ***	  P ≤ 0.001
    # ****	  P ≤ 0.0001
    if p > 0.05:    return "ns"
    if p <= 0.0001: return "****"
    if p <= 0.001:  return "***"
    if p <= 0.01:   return "**"
    return "*"

def paired_t_test(data1, data2):
    t_statistic, p_value = ttest_1samp(np.array(data1) - np.array(data2), 0)
    return p_value, p_summary(p_value), "Yes" if p_value < 0.05 else "No"

def unpaired_t_test(data1, data2):
    t_statistic, p_value = ttest_ind(np.array(data1), np.array(data2))
    return p_value, p_summary(p_value), "Yes" if p_value < 0.05 else "No"

if __name__ == "__main__":
    print("James' Gene Data Collector")
    print("Reading data...")
    # Read data
    # txt version:
    with open(r"data/STAD_gene_normal.txt") as f:
        normal_list = [i.strip().split("\t") for i in f.readlines()[1:]]

    with open(r"data/STAD_gene_tumor.txt") as f:
        tumor_list = [i.strip().split("\t") for i in f.readlines()[1:]]

    # dataset = []

    with open(r"data/output_gene.csv", "w") as out:
        out.write(",,P value,P value summary,Significantly different? (P < 0.05)\n")
        # seq = 1
        for n, t in zip(normal_list, tumor_list):
            # id = seq
            # seq += 1
            id = t[1]
            name = n[0]
            print("Collecting #{}: {}".format(id, name))
            data = Data(id, name, [float(x) for x in n[1:]], [float(x) for x in t[2:]])
            p, summary, isSignificant = unpaired_t_test(data.normal_data, data.tumor_data)
            p_text = "<0.0001" if p < 0.0001 else round(p, 4)
            # dataset.append(data)
            out.write("{},{},{},{},{}\n".format(id, name, p_text, summary, isSignificant))