Calculating pairwise correlation among all columns

Assuming the data you have is in a pandas DataFrame.

df.corr('pearson')  # 'kendall', and 'spearman' are the other 2 options

will provide you a correlation matrix between each column.

from pandas import *
import numpy as np
from libraries.settings import *
from scipy.stats.stats import pearsonr
import itertools

Creating random sample data:

df = DataFrame(np.random.random((5, 5)), columns=['gene_' + chr(i + ord('a')) for i in range(5)]) 
print(df)

     gene_a    gene_b    gene_c    gene_d    gene_e
0  0.471257  0.854139  0.781204  0.678567  0.697993
1  0.292909  0.046159  0.250902  0.064004  0.307537
2  0.422265  0.646988  0.084983  0.822375  0.713397
3  0.113963  0.016122  0.227566  0.206324  0.792048
4  0.357331  0.980479  0.157124  0.560889  0.973161

correlations = {}
columns = df.columns.tolist()

for col_a, col_b in itertools.combinations(columns, 2):
    correlations[col_a + '__' + col_b] = pearsonr(df.loc[:, col_a], df.loc[:, col_b])

result = DataFrame.from_dict(correlations, orient='index')
result.columns = ['PCC', 'p-value']

print(result.sort_index())

                     PCC   p-value
gene_a__gene_b  0.461357  0.434142
gene_a__gene_c  0.177936  0.774646
gene_a__gene_d -0.854884  0.064896
gene_a__gene_e -0.155440  0.802887
gene_b__gene_c -0.575056  0.310455
gene_b__gene_d -0.097054  0.876621
gene_b__gene_e  0.061175  0.922159
gene_c__gene_d -0.633302  0.251381
gene_c__gene_e -0.771120  0.126836
gene_d__gene_e  0.531805  0.356315

• Get unique combinations of DataFrame columns using itertools.combination(iterable, r)
• Iterate through these combinations and calculate pairwise correlations using scipy.stats.stats.personr
• Add results (PCC and p-value tuple) to dictionary
• Build DataFrame from dictionary

You could then also save result.to_csv(). You might find it convenient to use a MultiIndex (two columns containing the names of each columns) instead of the created names for the pairwise correlations.

To get pairs, it is a combinations problem. You can concat all the rows into one the result dataframe.

from pandas import *
from itertools import combinations
df = pandas.read_csv('gene.csv')
# get the column names as list, which are gene names
column_list = df.columns.values.tolist()
result = []
for c in combinations(column_list, 2):
    firstGene, secondGene = c
    firstGeneData = df[firstGene].tolist()
    secondGeneData = df[secondGene].tolist()
    # now to get the PCC, P-value using scipy
    pcc = ...
    p-value = ...
    result.append(pandas.DataFrame([{'PCC': pcc, 'P-value': p-value}], index=str(firstGene)+ '_' + str(secondGene), columns=['PCC', 'P-value'])

result_df = pandas.concat(result)
#result_df.to_csv(...)

A simple solution is to use the pairwise_corr function of the Pingouin package (which I created):

import pingouin as pg
pg.pairwise_corr(data, method='pearson')

This will give you a DataFrame with all combinations of columns, and, for each of those, the r-value, p-value, sample size, and more.

There are also a number of options to specify one or more columns (e.g. one-vs-all behavior), as well as covariates for partial correlation and different methods to calculate the correlation coefficient. Please see this example Jupyter Notebook for a more in-depth demo.