MAIN KEGG FILE RESULTS

	WEB-based GEne SeT AnaLysis Toolkit

	*Translating gene lists into biological insights...*

User data and parameters: User data: textAreaUpload.txt, Organism: hsapiens, Id Type: gene_symbol, Ref Set: entrezgene, Significance Level: Top10, Statistics Test: Hypergeometric, MTC: BH, Minimum: 2

This table lists the enriched KEGG pathways, number of Entrez IDs in your user data set for the pathway, the corresponding Entrez IDs, and the statistics for the enriched pathway.
The statistic column lists:

C: the number of reference genes in the category
O: the number of genes in the gene set and also in the category
E: the expected number in the category
R: ratio of enrichment
rawP: p value from hypergeometric test
adjP: p value adjusted by the multiple test adjustment

Finally, the pathway name is linked to KEGG where the user ids are highlighted, the number of user gene ids is linked to a table with information about the user ids, and the Entrez IDs are linked to Entrez Gene.

PathwayName	#Gene	EntrezGene	Statistics
Cytokine-cytokine receptor interaction	13	6374 6354 3976 1440 6363 6364 3576 6372 2919 3586 7042 6362 5473	C=265;O=13;E=0.39;R=33.58;rawP=1.05e-16;adjP=2.21e-15
Chemokine signaling pathway	9	6374 6354 6363 6364 3576 6372 2919 6362 5473	C=189;O=9;E=0.28;R=32.60;rawP=9.50e-12;adjP=9.98e-11
Malaria	6	1440 3586 7042 1378 6401 3576	C=51;O=6;E=0.07;R=80.54;rawP=1.30e-10;adjP=9.10e-10
Rheumatoid arthritis	6	2919 6374 7042 6364 3576 6372	C=91;O=6;E=0.13;R=45.14;rawP=4.60e-09;adjP=2.42e-08
Amoebiasis	4	2919 3586 7042 3576	C=106;O=4;E=0.15;R=25.83;rawP=1.84e-05;adjP=7.73e-05
Toxoplasmosis	4	3586 7042 3310 5320	C=132;O=4;E=0.19;R=20.74;rawP=4.34e-05;adjP=0.0002
Leishmaniasis	3	3586 7042 1378	C=72;O=3;E=0.11;R=28.52;rawP=0.0002;adjP=0.0006
Chagas disease (American trypanosomiasis)	3	3586 7042 3576	C=104;O=3;E=0.15;R=19.75;rawP=0.0005;adjP=0.0012
Toll-like receptor signaling pathway	3	6696 3929 3576	C=102;O=3;E=0.15;R=20.13;rawP=0.0005;adjP=0.0012
MAPK signaling pathway	4	7042 10912 3310 5320	C=268;O=4;E=0.39;R=10.22;rawP=0.0007;adjP=0.0015

WebGestalt is currently developed and maintained by Jing Wang and Bing Zhang at the Zhang Lab. Other people who have made significant contribution to the project include Dexter Duncan, Stefan Kirov, Zhiao Shi, and Jay Snoddy.

Funding credits: NIH/NIAAA (U01 AA016662, U01 AA013512); NIH/NIDA (P01 DA015027); NIH/NIMH (P50 MH078028, P50 MH096972); NIH/NCI (U24 CA159988); NIH/NIGMS (R01 GM088822).