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: ensembl_gene_stable_id, 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
Endocytosis	8	163 5338 440073 51534 10193 56288 2321 3312	C=201;O=8;E=1.33;R=6.00;rawP=6.46e-05;adjP=0.0035
RNA transport	6	10419 1983 1982 4928 11171 1964	C=151;O=6;E=1.00;R=5.99;rawP=0.0005;adjP=0.0135
Adherens junction	4	1457 3643 1495 56288	C=73;O=4;E=0.48;R=8.26;rawP=0.0014;adjP=0.0252
Protein processing in endoplasmic reticulum	5	3320 80331 3998 55666 3312	C=165;O=5;E=1.09;R=4.57;rawP=0.0050;adjP=0.0675
Ubiquitin mediated proteolysis	4	1643 9690 7318 8452	C=135;O=4;E=0.90;R=4.47;rawP=0.0127;adjP=0.0762
Tight junction	4	5520 1457 1495 56288	C=132;O=4;E=0.88;R=4.57;rawP=0.0118;adjP=0.0762
p53 signaling pathway	3	1643 55367 64393	C=68;O=3;E=0.45;R=6.65;rawP=0.0105;adjP=0.0762
Axon guidance	4	5365 5533 64221 5062	C=129;O=4;E=0.86;R=4.68;rawP=0.0109;adjP=0.0762
Glycosaminoglycan biosynthesis - chondroitin sulfate	2	135152 56548	C=22;O=2;E=0.15;R=13.71;rawP=0.0093;adjP=0.0762
Pathways in cancer	6	3320 7480 3716 2475 1495 1869	C=326;O=6;E=2.16;R=2.78;rawP=0.0222;adjP=0.0799

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).