Enrichment in phenotype: OIS (2 samples)
- 428 / 583 gene sets are upregulated in phenotype OIS
- 79 gene sets are significant at FDR < 25%
- 233 gene sets are significantly enriched at nominal pvalue < 1%
- 233 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: Pro (2 samples)
- 155 / 583 gene sets are upregulated in phenotype Pro
- 0 gene sets are significantly enriched at FDR < 25%
- 47 gene sets are significantly enriched at nominal pvalue < 1%
- 47 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 617 native features
- After collapsing features into gene symbols, there are: 615 genes
Gene set details
- Gene set size filters (min=15, max=500) resulted in filtering out 3148 / 3731 gene sets
- The remaining 583 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 OIS versus Pro comparison
- The dataset has 615 features (genes)
- # of markers for phenotype OIS: 351 (57.1% ) with correlation area NaN%
- # of markers for phenotype Pro: 264 (42.9% ) with correlation area 0.0%
- 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: 1626301093423
- Warning: Phenotype permutation was performed but the number of samples in class A is < 7, phenotype: Cliques.cls#OIS_versus_Pro
- Warning: Phenotype permutation was performed but the number of samples in class B is < 7, phenotype: Cliques.cls#OIS_versus_Pro
- 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.
