- Data tables
- Batch effect analysis
- Differential gene expression
- Gene set analysis
- Functional analysis: pathway, GO, drug signatures
- Signature comparison (Venn diagram, correlation)
- DE methods: DESeq2, edgeR, LIMMA/Voom, LIMMA-trend, t-test.
- Enrichment methods: GSEA, GSVA, ssGSEA, Fisher exact, rank correlation, camera, fry.
- Drug enrichment analysis using drug activity profiles from the L1000 database.
- Feature selection algorithms: LASSO, elastic nets, random forests, and extreme gradient boosting.
- Classifier algorithms: random forest, deep nets.
- Deconvolution methods: contrained-NNLS, NNLM, EPIC, rank correlation and DCQ.
- Immune cell reference databases: LM22, DICE, ImmProt, ImmunoStates.
- Normalization methods: quantile normalization, median-centering, TMM, RLE.
- Batch methods: quantile normalization, NMM, MNN, ComBat, LIMMA.
- Data integration using multi-matrix factorization and multi-partite graph algorithms.
- Multi-omics biomarker selection using PLS-DA, group LASSO, path scoring.
- Integrative multi-omics clustering using SNF, parallel heatmaps, multi-layer t-SNE.
Requirements (local installation)
- Workstation at least 8Gb RAM
- At least 10Gb disk space.