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compute_stats.py
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compute_stats.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 9 09:36:16 2022
@author: scottrk
"""
import os
from collections import defaultdict
import scipy.stats as stats
import pandas as pd
from compile_data import summary_import, melt_summary, mut_file_import, \
calc_clone_numbers
from GlobalVars_ import tissue_type, mut_type, mut_type_conv, tissue_type_abbrev, \
R_lib_path
from HelperFuncs_ import statsmodel_summary_to_df
import numpy as np
from statsmodels.stats.multicomp import pairwise_tukeyhsd
import rpy2.robjects as robjects
def Fig1A_D_stats(data, mut_class, output=None):
p_value_dict = defaultdict(lambda:[])
for tissue in tissue_type_abbrev[: -1]:
young_data = data.query("Age=='Young' & Tissue==@tissue & Treatment=='NT'")
old_data = data.query("Age=='Old' & Tissue==@tissue & Treatment=='NT'")
F_stat, p_value = stats.ttest_ind(young_data[mut_class], old_data[mut_class],
equal_var=False)
p_value_dict[tissue].append(p_value)
if output != None:
if os.path.isdir("data/stats/") == False:
os.mkdir("data/stats/")
pvalues = pd.DataFrame(p_value_dict).to_csv(output)
return p_value_dict
def anova_df(df, cohort, mut_class):
data = []
for tissue in tissue_type_abbrev[: -1]:
sub_data = df.query("Age==@cohort & Treatment=='NT' & Tissue==@tissue & Class==@mut_class")
data.append(sub_data['Frequency'])
f_stat, p_val = stats.f_oneway(data[0], data[1], data[2], data[3], data[4],
data[5], data[6], data[7])
tukey_data = df.query("Age==@cohort & Treatment=='NT' & Class==@mut_class")
tukey = pairwise_tukeyhsd(endog=tukey_data['Frequency'],
groups=tukey_data['Tissue'])
return p_val, tukey
def heatmap_stats(data, cohort, mut_class, output=None):
tukey_p_val = defaultdict(lambda:[])
p_val, anova = anova_df(data, cohort, mut_class)
statsmodel_df = statsmodel_summary_to_df(anova)
for tissue1 in tissue_type_abbrev[:-1]:
for tissue2 in tissue_type_abbrev[:-1]:
df = statsmodel_df.query("group1==@tissue1 | group2==@tissue1")
x = df.query("group1==@tissue2 | group2==@tissue2")['p-adj']
if tissue1 != tissue2:
tukey_p_val[tissue1].append(float(x))
else:
tukey_p_val[tissue1].append(np.nan)
pvals_df = pd.DataFrame(tukey_p_val)
pvals_df.index = tissue_type[:-1]
pvals_df = pvals_df.T
if output != None:
if os.path.isdir("data/stats/") == False:
os.mkdir("data/stats/")
pvals_df.to_csv(output)
return pvals_df
def Fig3C_stats(lib_loc):
r_source = robjects.r['source']
if os.path.isdir('data/stats/') == False:
os.mkdir('data/stats/')
#output = open("data/stats/Figure_2_ratio_statistics.csv", 'w')
r_source('fold_changes.R')
#output.close()
def Fig4A_B_stats(data, column_name, output):
clone_percent_pvals = {tissue: 0 for tissue in tissue_type[: -1]}
for tissue in tissue_type_abbrev[: -1]:
old = stats.ttest_ind(data.query("Tissue==@tissue & Cohort=='Old'")[column_name],
data.query("Tissue==@tissue & Cohort=='Young'")[column_name],
equal_var=False)
clone_percent_pvals[tissue] = old.pvalue
pvalues = pd.DataFrame(clone_percent_pvals, columns=tissue_type[:-1],
index=["Clone Percent P-values"])
if os.path.isdir("data/stats/") == False:
os.mkdir("data/stats/")
pvalues.to_csv(output)
return pvalues
def Fig6_stats(lib_loc):
r_source = robjects.r('source')
if os.path.isdir('data/stats/') == False:
os.mkdir('data/stats/')
#output = open("data/stats/Figure_5_Dunnett_statistics.csv", 'w')
r_source('Dunnett_test.R')
#output.close()
def Figure_6_figure_supplement_3_stats(clone_freq_df, output):
pvalue_list = []
for tissue in ['K', 'L', 'Hi', 'C', 'M', 'B']:
test = stats.ttest_ind(clone_freq_df.query("Tissue==@tissue & Cohort=='Old'")['Clone_Freq'],
clone_freq_df.query("Tissue==@tissue & Cohort=='perf'")['Clone_Freq'],
equal_var=False )
pvalue_list.append(test.pvalue)
pvalues = pd.DataFrame(pvalue_list, index=['K', 'L', 'Hi', 'C', 'M','B'], columns=['P value'])
if not os.path.isdir("data/stats/"):
os.mkdir("data/stats/")
pvalues.to_csv(output)
return pvalues
if __name__ == "__main__":
# Import data
if not os.path.isfile("data/imported_data/summary_data_wide.csv"):
if not os.path.isdir("data/imported_data/"):
os.mkdir("data/imported_data/")
summary_data = summary_import('data/Mouse_aging_mtDNA_summary.csv')
summary_data.to_csv("data/imported_data/summary_data_wide.csv")
else:
summary_data = pd.read_csv("data/imported_data/summary_data_wide.csv")
if not os.path.isfile('data/imported_data/summary_data_tidy.csv'):
summary_data_long = melt_summary(summary_data)
summary_data_long.to_csv("data/imported_data/summary_data_tidy.csv")
else:
summary_data_long = pd.read_csv("data/imported_data/summary_data_tidy.csv")
if not os.path.isfile("data/imported_data/mut_file_data.csv"):
mut_data = mut_file_import()
mut_data.to_csv("data/imported_data/mut_file_data.csv")
else:
mut_data = pd.read_csv("data/imported_data/mut_file_data.csv",
index_col=[0, 1])
if not os.path.isfile("data/imported_data/summary_clone_data.csv"):
final_clone_data = calc_clone_numbers(mut_data)
final_clone_data.to_csv("data/imported_data/summary_clone_data.csv")
else:
final_clone_data = pd.read_csv("data/imported_data/summary_clone_data.csv",
index_col=0)
# Figure 1 statistics
Fig1A_D_stats(summary_data, "Total_SNV_Freq", "data/stats/Figure_1A_statistics.csv")
Fig1A_D_stats(summary_data, "Total_InDel_Freq", "data/stats/Figure_1D_statistics.csv")
heatmap_stats(summary_data_long, "Young", 'Total_SNV_Freq', "data/stats/Figure_1A_Young_heatmap_stats.csv")
heatmap_stats(summary_data_long, "Old", 'Total_SNV_Freq', "data/stats/Figure_1A_Old_heatmap_stats.csv")
heatmap_stats(summary_data_long, "Young", 'Total_InDel_Freq', "data/stats/Figure_1D_Young_heatmap_stats.csv")
heatmap_stats(summary_data_long, "Old", 'Total_InDel_Freq', "data/stats/Figure_1D_Old_heatmap_stats.csv")
# Figure 3A-B statistics
for age in ['Young', 'Old']:
for i, mut_class in enumerate(mut_type):
heatmap_stats(summary_data_long, age, mut_class, "data/stats/Figure_3_" +
age + "_" + mut_type_conv[mut_class] + "_heatmap_stats.csv")
# Figure 3C statistics
Fig3C_stats(R_lib_path) # change package location as necessary
# Figure 4 statistics
Fig4A_B_stats(final_clone_data, "Percent_Clone", "data/stats/Figure_4A_statistics.csv")
Fig4A_B_stats(final_clone_data, "Clone_Freq", "data/stats/Figure_4B_statistics.csv")
#Figure 6 statistics
Fig6_stats(R_lib_path)
#Figure 6-figure_supplement 3 statistics
Figure_6_figure_supplement_3_stats(final_clone_data, "data/stats/Figure_6_figure_supplement_3_statistics.csv")