Bioconductor Packages

A Functional Approach To Impute Genetically Regulated Expression

Batch Effects Quality Control Software

Singular Value Decomposition for Bioconductor Packages

Coordinate Covariation Analysis

Analysis of single-cell epigenomics datasets with a Shiny App

ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data

Differential gene expression analysis based on the negative binomial distribution

Interactive Workflow for Discovering Rhythmicity in Biological Data

GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness

Interpretation of RNA-seq experiments through robust, efficient comparison to public databases

The removal of batch effects from datasets using a PCA and constrained optimisation based technique

Inference of gene regulatory networks from gene expression data

Multiomic Batch effect Correction

An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples

PCAtools: Everything Principal Components Analysis

PLSDA-batch

Tools for Omics Data Analysis

PathoStat Statistical Microbiome Analysis Package

Infers biological signatures from gene expression data

Robust Ancestry Inference using Data Synthesis

The Cancer Genome Atlas Data Integration

Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation

Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

Classes and methods for multi-omics data integration

Surface Protein Prediction and Identification

Artificial Components Detection of Differentially Expressed Genes

Unified Statistical Modeling of Omics Data

ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)

Correspondence Analysis for Single Cell Data

Visualizations of expressionSet Bioconductor object

Genotype Conditional Association TEST

Integrative Modules for Multi-Omics Data

Integrates DNA methylation data with gene expression in a single gene network

Logistic Factor Analysis for Categorical Data

An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods

Handling Missing Individuals in Multi-Omics Data Integration

Multiple omics data integrative clustering and gene set analysis

Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis

Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

Interactive Visualization of RNA-seq Data Using a Principal Components Approach

Visual and interactive gene expression analysis

Genomic trajectories with heterogeneous genetic and environmental backgrounds

Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data

rqt: utilities for gene-level meta-analysis

Single-Cell Exploratory Compositional Data Analysis

Sparse Contrastive Principal Component Analysis