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:
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
Pathways in cancer 14 572 5925 83439 4087 7185 6654 27006 331 5900 1147 867 4233 1856 3066 C=326;O=14;E=3.00;R=4.67;rawP=2.48e-06;adjP=7.19e-05
Lysosome 9 2799 3920 9583 1213 3073 23431 3425 26985 4074 C=121;O=9;E=1.11;R=8.08;rawP=1.98e-06;adjP=7.19e-05
Metabolic pathways 29 85465 5436 55312 7360 8540 84245 5433 29071 526 11128 5281 1571 84920 2799 2058 64768 2618 5427 1583 50484 64132 5033 201595 10768 6309 3073 3425 94005 79646 C=1130;O=29;E=10.40;R=2.79;rawP=9.01e-07;adjP=7.19e-05
Ubiquitin mediated proteolysis 9 55958 9320 83737 331 10055 26091 22888 867 55236 C=135;O=9;E=1.24;R=7.24;rawP=4.88e-06;adjP=0.0001
Protein processing in endoplasmic reticulum 9 5887 6400 80267 55757 22926 201595 56886 821 23471 C=165;O=9;E=1.52;R=5.93;rawP=2.46e-05;adjP=0.0004
Chronic myeloid leukemia 6 572 1147 5925 867 6654 3066 C=73;O=6;E=0.67;R=8.93;rawP=5.93e-05;adjP=0.0009
Purine metabolism 8 2618 9583 5427 5436 50484 10846 5433 11128 C=162;O=8;E=1.49;R=5.36;rawP=0.0001;adjP=0.0011
Endocytosis 9 4087 10015 10254 83737 1213 867 23096 4233 57403 C=201;O=9;E=1.85;R=4.86;rawP=0.0001;adjP=0.0011
Acute myeloid leukemia 5 572 6198 1147 83439 6654 C=57;O=5;E=0.52;R=9.53;rawP=0.0002;adjP=0.0017
Apoptosis 6 572 1147 1677 5576 11261 331 C=87;O=6;E=0.80;R=7.49;rawP=0.0002;adjP=0.0017


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).