The enrichplot package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analyses. All the visualization methods are developed based on ggplot2 graphics.

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model.

This is a package for normalization, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalization procedure is subset-quantile within-array normalization (SWAN), which allows Infinium I and II type probes on a single array to be normalized together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.

This package provides a package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server.

This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.

This package provides a consistent C++ class interface for a variety of commonly used matrix types, including sparse and HDF5-backed matrices.

This package provides functions for pathway analysis based on the REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization.

This package provides a simple interface to and data from the Human Protein Atlas project.

The package uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor.

This package provides rna-seq datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. The Rna-seq data format is explained here https://wiki.nci.nih.gov/display/TCGA/RNASeq+Version+2. The data source is Illumina hiseq Level 3 RSEM normalized expression data from 2015-11-01 snapshot.

Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).

This package provides a framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. It imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. It preprocesses data for high-throughput, untargeted analyte profiling.

This package implements some simple graph handling capabilities for R.

This package provides robust model-based clustering using a t-mixture model with Box-Cox transformation.

This is the core package for the automated analysis of Affymetrix arrays.

The sparse nature of single cell epigenomics data can be overruled using probabilistic modelling methods such as Latent Dirichlet Allocation (LDA). This package allows the probabilistic modelling of cis-regulatory topics (cisTopics) from single cell epigenomics data, and includes functionalities to identify cell states based on the contribution of cisTopics and explore the nature and regulatory proteins driving them.

The package ASGSCA (Association Study using Generalized Structured Component Analysis) provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables.

The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods.

This is a package to perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113).

This package provides high performance functions for row and column operations on sparse matrices. Currently, the optimizations are limited to data in the column sparse format.

This package implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate.

This package takes sample information in the form of the fraction of mutations in each of 96 trinucleotide contexts and identifies the weighted combination of published signatures that, when summed, most closely reconstructs the mutational profile.

LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets.

This package provides a database of PolyPhen predictions for Homo sapiens dbSNP build 131.