Correlation Case vs. Gene Expression Control

Question

I have data on gene expression in 77 cancer patients. I have one set of patients blood, one set of patients' tumors and one set of healing tissue:

data1 <- ExpressionBlood
data2 <- ExpressionCancerTissue
data3 <- ExpressionHealtyTissue

I would like to analyze if the expression in the tumor tissue correlated with the expression in the blood for all my genes. What is the best way to do this?

Answer 1

If you are familiar with python, I would use pandas . It uses "DataFrames" similar to R, so you can take the concept and apply it to R.

, data1 - , :

GeneName | ExpValue |
gene1       300.0
gene2       250.0

, DataFrame:

dfblood = pd.read_csv('path/to/data1',delimiter='\t')
dftissue = pd.read_csv('path/to/data2',delimiter='\t')
dftumor = pd.read_csv('path/to/data3',delimiter='\t')

merge DataFrame df.

dftmp = pd.merge(dfblood,dftissue,on='GeneName',how='inner')
df = pd.merge(dftmp,dftumor,on='GeneName',how='inner')

, , .

df.columns = ['GeneName','blood','tissue','tumor']

( ) .

df = df.set_index('GeneName') # allows you to perform computations on the entire dataset
df_norm = (df - df.mean()) / (df.max() - df.min())

df_norm.corr() . numpy , .

          blood      tissue       tumor
blood   1.000000    0.395160    0.581629
tissue  0.395160    1.000000    0.840973
tumor   0.581629    0.840973    1.000000

HTH .

Student T, , numpy.log

import numpy as np

df[['blood','tissue','tumor']] = df[['blood','tissue','tumor']]+1
# +1 to avoid taking the log of 0
df_log = np.log(df[['blood','tissue','tumor']])

, df_log DataFrame.

df_log['logFCBloodTumor'] = df_log['blood'] - df_log['tumor']
df_log['logFCBloodTissue'] = df_log['blood'] - df_log['tissue']