Product Sheet

Analyis Modules

  • Data tables
  • Batch effect analysis
  • Differential gene expression
  • Gene set analysis
  • Functional analysis: pathway, GO, drug signatures
  • Signature comparison (Venn diagram, correlation)

Computational methods

  • 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.