GSEA Report for Dataset w6a_vs_wt

Enrichment in phenotype: ago1w6a (3 samples)

• 117 / 177 gene sets are upregulated in phenotype ago1w6a
• 28 gene sets are significant at FDR < 25%
• 22 gene sets are significantly enriched at nominal pvalue < 1%
• 29 gene sets are significantly enriched at nominal pvalue < 5%
• Snapshot of enrichment results
• Detailed enrichment results in html format
• Detailed enrichment results in excel format (tab delimited text)
• Guide to interpret results

Enrichment in phenotype: ago1gfp (3 samples)

• 60 / 177 gene sets are upregulated in phenotype ago1gfp
• 12 gene sets are significantly enriched at FDR < 25%
• 7 gene sets are significantly enriched at nominal pvalue < 1%
• 12 gene sets are significantly enriched at nominal pvalue < 5%
• Snapshot of enrichment results
• Detailed enrichment results in html format
• Detailed enrichment results in excel format (tab delimited text)
• Guide to interpret results

Dataset details

• The dataset has 17975 features (genes)
• No probe set => gene symbol collapsing was requested, so all 17975 features were used

Gene set details

• Gene set size filters (min=15, max=500) resulted in filtering out 9 / 186 gene sets
• The remaining 177 gene sets were used in the analysis
• List of gene sets used and their sizes (restricted to features in the specified dataset)

Gene markers for the ago1w6a versus ago1gfp comparison

• The dataset has 17975 features (genes)
• # of markers for phenotype ago1w6a: 9184 (51.1% ) with correlation area 55.4%
• # of markers for phenotype ago1gfp: 8791 (48.9% ) with correlation area 44.6%
• Detailed rank ordered gene list for all features in the dataset
• Heat map and gene list correlation profile for all features in the dataset

Global statistics and plots

• Plot of p-values vs. NES
• Global ES histogram

Other

• Parameters used for this analysis

Comments

• There were duplicate row identifiers in the specified dataset. One id was arbitarilly choosen. Details are below Generally, this is OK, but if you want to avoid this automagic, edit your dataset so that all row ids are unique # of row ids in original dataset: 17996 # of row UNIQUE ids in original dataset: 17975 # The duplicates were ARL17B CC2D2B RPP21 GTF2H2 SEM1 RABGAP1 HERC3 GALNT8 HLA-DQA2 LCOR NLRP1 CTAGE5 KLHL23 ERCC6 APLF UVSSA DCDC1 NBPF11 CERS1 FCGR2A NHSL2