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
Cell cycle	13	8318 5347 4085 991 9232 1869 699 1111 990 9088 891 9133 983	C=124;O=13;E=0.26;R=50.24;rawP=6.55e-19;adjP=6.55e-18
Oocyte meiosis	9	5347 699 9088 891 9133 983 4085 991 9232	C=112;O=9;E=0.23;R=38.51;rawP=2.29e-12;adjP=1.14e-11
Progesterone-mediated oocyte maturation	7	5347 699 9088 891 9133 983 4085	C=86;O=7;E=0.18;R=39.00;rawP=6.39e-10;adjP=2.13e-09
p53 signaling pathway	5	6241 1111 891 9133 983	C=68;O=5;E=0.14;R=35.23;rawP=3.32e-07;adjP=8.30e-07
Pyrimidine metabolism	4	7298 6241 7083 5427	C=99;O=4;E=0.21;R=19.36;rawP=5.74e-05;adjP=0.0001
Pancreatic cancer	2	5888 1869	C=70;O=2;E=0.15;R=13.69;rawP=0.0095;adjP=0.0158
Pathways in cancer	3	332 5888 1869	C=326;O=3;E=0.68;R=4.41;rawP=0.0310;adjP=0.0408
Ubiquitin mediated proteolysis	2	11065 991	C=135;O=2;E=0.28;R=7.10;rawP=0.0326;adjP=0.0408
Purine metabolism	2	6241 5427	C=162;O=2;E=0.34;R=5.92;rawP=0.0453;adjP=0.0503
Metabolic pathways	5	7298 6241 7083 5427 5050	C=1130;O=5;E=2.36;R=2.12;rawP=0.0878;adjP=0.0878

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).