Use DEG to get cohort-specific and disease relatated ceRNA axis

Use differential expressed (DE) miRNA/mRNA to get cohort-specific and disease relatated ceRNA axis

%load_ext autoreload
import sys
sys.path.append('../../')

from cernatax.cernatax import CERNATAX

# initialize CERNATAX object
cernatax = CERNATAX()

# Use DE miRNA/mRNA to get the ceRNA_axis

import pandas as pd

# use DE miRNA list as the strict criteria
deg_strict_df = pd.read_csv('../../demo/scz_deg_miRNA.csv', index_col=0)
print('deg_strict_df\n', deg_strict_df.head())
# use DE mRNA list as the loose criteria
deg_loose_df = pd.read_csv('../../demo/scz_deg_mRNA.csv', index_col=0)
print('deg_loose_df\n', deg_loose_df.head())

deg_strict_df
                   type             gene    log2FC        pvalue          padj
hsa-miR-16-2-3p  miRNA  hsa-miR-16-2-3p -5.139793  2.640000e-13  4.020000e-10
hsa-miR-1306-3p  miRNA  hsa-miR-1306-3p -2.209798  3.320000e-11  1.690000e-08
hsa-miR-1307-3p  miRNA  hsa-miR-1307-3p -2.093602  2.960000e-11  1.690000e-08
hsa-miR-132-5p   miRNA   hsa-miR-132-5p -2.121375  5.510000e-09  1.680000e-06
hsa-miR-624-5p   miRNA   hsa-miR-624-5p  3.021188  3.580000e-08  9.100000e-06
deg_loose_df
            baseMean  log2FoldChange     lfcSE      stat    pvalue      padj  \
MPO      306.948587        2.216337  0.564877  3.923577  0.000087  0.002920   
DEPDC1    13.781165        2.521829  0.955832  2.638359  0.008331  0.072875   
DEPDC1B   35.058035        1.846581  0.528130  3.496453  0.000471  0.010010   
UTS2     166.152947        2.148164  0.747088  2.875383  0.004035  0.044262   
COL17A1   17.798800        2.779151  1.039143  2.674463  0.007485  0.067760   

            gene  type    log2FC  
MPO          MPO  mRNA  2.216337  
DEPDC1    DEPDC1  mRNA  2.521829  
DEPDC1B  DEPDC1B  mRNA  1.846581  
UTS2        UTS2  mRNA  2.148164  
COL17A1  COL17A1  mRNA  2.779151  

# Use strict DE miRNA to get the ceRNA_axis
ceRNA_df, axis_df = cernatax.find_ceRNA_axis_by_DEG(deg_strict_df)
# store the filtered ceRNA network and ceRNA axis
ceRNA_df.to_csv('../../demo_out/SCZ_ceRNA_network.csv')
axis_df.to_csv('../../demo_out/SCZ_ceRNA_axis.csv')
ceRNA_df

	miRNA	ceRNA	species	database	type	miRNA_log2FC	ceRNA_log2FC	inference
682758	hsa-miR-1278	CEBPB	Homo sapiens	RNAInter	miRNA-mRNA	2.011778	-7.399231	strict
805405	hsa-miR-1306-3p	CEACAM6	Homo sapiens	miRWalk	miRNA-mRNA	-2.209798	2.820942	strict
806911	hsa-miR-1306-3p	NEBL	Homo sapiens	miRWalk	miRNA-mRNA	-2.209798	2.529632	strict
807351	hsa-miR-1306-3p	PPARGC1A	Homo sapiens	miRWalk	miRNA-mRNA	-2.209798	3.927339	strict
812245	hsa-miR-1307-3p	ADAM32	Homo sapiens	miRWalk	miRNA-mRNA	-2.093602	3.126004	strict
...	...	...	...	...	...	...	...	...
6996766	hsa-miR-6855-5p	FSIP2	Homo sapiens	miRWalk	miRNA-mRNA	-2.456434	4.072671	strict
6998598	hsa-miR-6855-5p	RNASE3	Homo sapiens	miRWalk	miRNA-mRNA	-2.456434	2.428955	strict
6998710	hsa-miR-6855-5p	RWDD3	Homo sapiens	miRWalk	miRNA-mRNA	-2.456434	2.265765	strict
6999339	hsa-miR-6855-5p	TLCD4-RWDD3	Homo sapiens	miRWalk	miRNA-mRNA	-2.456434	3.067442	strict
6999841	hsa-miR-6855-5p	ZNF208	Homo sapiens	miRWalk	miRNA-mRNA	-2.456434	2.872955	strict

93 rows × 8 columns

# Use strict DE miRNA and loose DE mRNA to get the final ceRNA_axis
loose_axis_df = cernatax.expand_ceRNA_axis_by_loose_DEG(deg_strict_df, deg_loose_df)
# store the final ceRNA axis
loose_axis_df.to_csv('../../demo_out/SCZ_ceRNA_loose_axis.csv')
# output the final ceRNA axis
loose_axis_df

	miRNA	ceRNA	species	database	type	miRNA_log2FC	ceRNA_log2FC	inference
1775644	hsa-miR-3064-5p	ARHGAP8	Homo sapiens	miRWalk	miRNA-mRNA	-2.123515	5.218091	loose
1775806	hsa-miR-3064-5p	BCL2A1	Homo sapiens	miRWalk	miRNA-mRNA	-2.123515	1.586707	loose
1775833	hsa-miR-3064-5p	BIRC5	Homo sapiens	miRWalk	miRNA-mRNA	-2.123515	1.762193	loose
1775877	hsa-miR-3064-5p	BTNL3	Homo sapiens	miRWalk	miRNA-mRNA	-2.123515	4.612546	loose
1776086	hsa-miR-3064-5p	CCDC80	Homo sapiens	TargetSCAN_8.0	miRNA-mRNA	-2.123515	5.326842	loose
...	...	...	...	...	...	...	...	...
4495205	hsa-miR-485-5p	TLCD4-RWDD3	Homo sapiens	miRWalk	miRNA-mRNA	-3.818348	3.067442	loose
4495575	hsa-miR-485-5p	UNC5B	Homo sapiens	TargetSCAN_8.0	miRNA-mRNA	-3.818348	1.507402	loose
4495619	hsa-miR-485-5p	UTS2	Homo sapiens	miRWalk	miRNA-mRNA	-3.818348	2.148164	loose
4495817	hsa-miR-485-5p	ZFP30	Homo sapiens	miRWalk	miRNA-mRNA	-3.818348	1.746945	loose
4495936	hsa-miR-485-5p	ZNF441	Homo sapiens	miRWalk	miRNA-mRNA	-3.818348	1.650988	loose

118 rows × 8 columns