This package contains data for the ChIPexoQual package, consisting of 3 chromosome 1 aligned reads from a ChIP-exo experiment for FoxA1 in mouse liver cell lines aligned to the mm9 genome.

This package implements two functions. One of them reads an Affymetrix CDF and creates a hash table environment containing the location/probe set membership mapping. The other one creates a package that automatically loads that environment.

This package allows importing most common specific structure (motif) types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.

This package provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new or custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.

BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with rstan. It provides as output the stanfit object as well as posterior median and credible interval estimates for each correlation element.

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (dropout imputation). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. The ADImpute package proposes two methods to address this issue:

1. a gene regulatory network-based approach using gene-gene relationships learnt from external data;

2. a baseline approach corresponding to a sample-wide average.

ADImpute implements these novel methods and also combines them with existing imputation methods like DrImpute and SAVER. ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include

1. detect cell-type specific or cross-cell type differential signals

2. tree-based differential analysis

3. improve variable selection in reference-free deconvolution

4. partial reference-free deconvolution with prior knowledge.

The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.

The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency.

This package provides functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files).

This package provides full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer11, May 2017) and stored in Biostrings objects.

This package provides a fairly extensive and comprehensive interface to the graph algorithms contained in the Boost library.

This package implements tools for delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. It also supports partial computation of residuals where selected factors are to be preserved in the output matrix. It implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means.

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This package computes differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.

tRNAdbImport imports the entries of the tRNAdb and mtRNAdb as GRanges object.

This package provides tools for calculating the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data.

This package provides data from 3 technical replicates of the cell line GM12878 from the EPIC methylation array.

R-msigdb provides the Molecular Signatures Database in a R accessible objects. Signatures are stored in GeneSet class objects form the GSEABase package and the entire database is stored in a GeneSetCollection object. These data are then hosted on the ExperimentHub. Data used in this package was obtained from the MSigDB of the Broad Institute. Metadata for each gene set is stored along with the gene set in the GeneSet class object.

This package provides utilities for flow cytometry data.

This package ofers functions for importation, normalization, visualization, and quality control to correct identified sources of variability in array of CGH experiments.

This is a package for saving SingleCellExperiment into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

The HiTC package was developed to explore high-throughput "C" data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided.

The package r-alevinqc generates quality control reports summarizing the output from an alevin run. The reports can be generated as HTML or PDF files, or as Shiny applications.