Enrichment in phenotype: 4hr (3 samples)
- 546 / 901 gene sets are upregulated in phenotype 4hr
- 62 gene sets are significant at FDR < 25%
- 114 gene sets are significantly enriched at nominal pvalue < 1%
- 114 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: 0hr (3 samples)
- 355 / 901 gene sets are upregulated in phenotype 0hr
- 0 gene sets are significantly enriched at FDR < 25%
- 17 gene sets are significantly enriched at nominal pvalue < 1%
- 17 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 29503 native features
- After collapsing features into gene symbols, there are: 13884 genes
Gene set details
- Gene set size filters (min=15, max=500) resulted in filtering out 428 / 1329 gene sets
- The remaining 901 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 4hr versus 0hr comparison
- The dataset has 13884 features (genes)
- # of markers for phenotype 4hr: 7277 (52.4% ) with correlation area 58.2%
- # of markers for phenotype 0hr: 6607 (47.6% ) with correlation area 41.8%
- Detailed rank ordered gene list for all features in the dataset
- Heat map and gene list correlation profile for all features in the dataset
- Buttefly plot of significant genes
Global statistics and plots
Comments
- Timestamp used as random seed: 1525363062992
- Warning: Phenotype permutation was performed but the number of samples in class A is < 7, phenotype: class.cls#4hr_versus_0hr_repos
- Warning: Phenotype permutation was performed but the number of samples in class B is < 7, phenotype: class.cls#4hr_versus_0hr_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.
