BASiCS is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori. BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells.

This package provides tools to identify cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection.

ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction(ANCOM-BC) and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package were designed to correct these biases and construct statistically consistent estimators.

This package provides an interface to implementations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.

This package provides methods for working with Illumina arrays using the gdsfmt package.

This package provides an annotation database of Homo sapiens genome data. It is derived from the UCSC hg19 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

This package provides tools for the identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).

This package awst (Asymmetric Within-Sample Transformation) that regularizes RNA-seq read counts and reduces the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.

This package implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.

rtracklayer is an extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.

This package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. It also contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data like gene expression/RNA sequencing/methylation/brain imaging data that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise.

This package implements a new RNA-Seq analysis method and integrates two modules: a basic model for pairwise comparison and a linear model for complex design. RNA-Seq quantifies gene expression with reads count, which usually consists of conditions (or treatments) and several replicates for each condition. This software infers differential expression directly by the counts difference between conditions. It assumes that the sum counts difference between conditions follow a negative binomial distribution. In addition, ABSSeq moderates the fold-changes by two steps: the expression level and gene-specific dispersion, that might facilitate the gene ranking by fold-change and visualization.

BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance.

This package exposes an annotation database generated from Ensembl.

The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.

This package provides a collection of software tools for calculating distance measures.

This is an annotation package for Illumina Infinium DNA methylation probes. It contains the compiled HumanMethylation27 and HumanMethylation450 annotations.

The package ANF(Affinity Network Fusion) provides methods for affinity matrix construction and fusion as well as spectral clustering. This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion.

This package is used for the identification and validation of sequence motifs. It makes use of STAMP for comparing a set of motifs to a given database (e.g. JASPAR). It can also be used to visualize motifs, motif distributions, modules and filter motifs.

This package stores all schemas required by various alabaster.* packages. No computation should be performed by this package, as that is handled by alabaster.base.

This package performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using EBP estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weight. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs.

This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments. The common goal in analyzing this ChIP-chip data is to detect DNA-protein interactions from ChIP-chip experiments. The BAC package has mainly been tested with Affymetrix tiling array data. However, we expect it to work with other platforms (e.g. Agilent, Nimblegen, cDNA, etc.). Note that BAC does not deal with normalization, so you will have to normalize your data beforehand.

This package provides a method for combining single-cell cytometry datasets, which increases the analytical flexibility and the statistical power of the analyses while minimizing technical noise.

This package is a collection of gene expression data from a breast cancer study published in Wang et al. 2005 and Minn et al 2007.