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xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology.

Spatial-eXpression-R (spacexr) is a package for analyzing cell types in spatial transcriptomics data. This implementation is a fork of the spacexr GitHub repo (https://github.com/dmcable/spacexr), adapted to work with Bioconductor objects. The original package implements two statistical methods: RCTD for learning cell types and CSIDE for inferring cell type-specific differential expression. Currently, this fork only implements RCTD, which learns cell type profiles from annotated RNA sequencing (RNA-seq) reference data and uses these profiles to identify cell types in spatial transcriptomic pixels while accounting for platform-specific effects. Future releases will include an implementation of CSIDE.

Provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA).

Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line.