Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. This package provides an extensible R based data client for Firehose pre-processed data.

The parody package provides routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics.

DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It models the expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles.

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 includes positive ionization mode data in NetCDF file format. Centroided subset from 200-600 m/z and 2500-4500 seconds. Data originally reported in "Assignment of Endogenous Substrates to Enzymes by Global Metabolite Profiling" Biochemistry; 2004; 43(45). It also includes detected peaks in an xcmsSet.

This package provides a collection of tools for analyzing and visualizing bisulfite sequencing data.

The package is usable with Affymetrix GeneChip short oligonucleotide arrays, and it can be adapted or extended to other platforms. It is able to modify or replace the grouping of probes in the probe sets. Also, the package contains simple functions to read R connections in the FASTA format and it can create an alternative mapping from sequences.

This package provides classes and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters are used by ensembldb, Organism.dplyr, and other packages.

This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined.

This package exposes an annotation database generated from Ensembl.

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 to plot data associated with arbitrary genomic intervals along chromosomal ideogram.

This package provides S4 data structures and basic functions to deal with flow cytometry data.

This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.

This package provides support for numerical and graphical summaries of RNA-Seq genomic read data. Provided within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization. Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization.

This package provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

This package provides a new clustering algorithm, binary cut, for clustering similarity matrices of functional terms is implemented in this package. It also provides functionalities for visualizing, summarizing and comparing the clusterings.

This package provides a pure data-driven gene network, WGCN(weighted gene co-expression network) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach.

RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge).

This package provides various wrapper functions that have been written to streamline the more common analyses that a Biostatistician might see.

Independent Surrogate Variable Analysis is an algorithm for feature selection in the presence of potential confounding factors (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).

This package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site.

Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc.

This package provides functions for reading array comparative genomic hybridization (aCGH) data from image analysis output files and clone information files, creation of aCGH objects for storing these data. Basic methods are accessing/replacing, subsetting, printing and plotting aCGH objects.