Enrichment in phenotype: Fosl1 (7 samples)
- 9 / 32 gene sets are upregulated in phenotype Fosl1
- 0 gene sets are significant at FDR < 25%
- 0 gene sets are significantly enriched at nominal pvalue < 1%
- 0 gene sets are significantly enriched at nominal pvalue < 5%
- Snapshot of enrichment results
- Detailed enrichment results in html format
- Detailed enrichment results in TSV format (tab delimited text)
- Guide to interpret results
Enrichment in phenotype: Ctrl (4 samples)
- 23 / 32 gene sets are upregulated in phenotype Ctrl
- 13 gene sets are significantly enriched at FDR < 25%
- 2 gene sets are significantly enriched at nominal pvalue < 1%
- 9 gene sets are significantly enriched at nominal pvalue < 5%
- Snapshot of enrichment results
- Detailed enrichment results in html format
- Detailed enrichment results in TSV format (tab delimited text)
- Guide to interpret results
Dataset details
- The dataset has 24389 features (genes)
- No probe set => gene symbol collapsing was requested, so all 24389 features were used
Gene set details
- Gene set size filters (min=15, max=5000) resulted in filtering out 0 / 32 gene sets
- The remaining 32 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 Fosl1 versus Ctrl comparison
- The dataset has 24389 features (genes)
- # of markers for phenotype Fosl1: 10636 (43.6% ) with correlation area 46.0%
- # of markers for phenotype Ctrl: 13753 (56.4% ) with correlation area 54.0%
- Detailed rank ordered gene list for all features in the dataset
- Heat map and gene list correlation profile for all features in the dataset
- Butterfly plot of significant genes
Global statistics and plots
Comments
- Timestamp used as random seed: 1619602030436
- Warning: Phenotype permutation was performed but the number of samples in class B is < 7, phenotype: NSCs_shNf1_sgFosl1.cls#Fosl1_versus_Ctrl_repos
- With small datasets, there might not be enough random permutations of sample labels to generate a sufficient null distribution. In such cases, gene_set randomization might be a better choice.