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.

This package provides a set of functions to create and interact with dynamic documents and vignettes.

This is a package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure.

ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is a free cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly.

This package provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction.

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.

The package alpine helps to model bias parameters and then using those parameters to estimate RNA-seq transcript abundance. Alpine is a package for estimating and visualizing many forms of sample-specific biases that can arise in RNA-seq, including fragment length distribution, positional bias on the transcript, read start bias (random hexamer priming), and fragment GC-content (amplification). It also offers bias-corrected estimates of transcript abundance in FPKM(Fragments Per Kilobase of transcript per Million mapped reads). It is currently designed for un-stranded paired-end RNA-seq data.

This package provides a computational method that infers copy number variations (CNV) in cancer scRNA-seq data and reconstructs the tumor phylogeny. It integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. It does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). It can be used to:

1. detect allele-specific copy number variations from single-cells

2. differentiate tumor versus normal cells in the tumor microenvironment

3. infer the clonal architecture and evolutionary history of profiled tumors

For details on the method see Gao et al in Nature Biotechnology 2022.

Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys.

This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms.

This package provides methods and models for handling zero-inflated single cell assay data.

This package provides genome wide annotation for Yeast, primarily based on mapping using ORF identifiers from SGD.

Graphite provides networks derived from eight public pathway databases, and automates the conversion of node identifiers (e.g. from Entrez IDs to gene symbols).

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.

This package provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments.

The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium.

AgiMicroRna provides useful functionality for the processing, quality assessment and differential expression analysis of Agilent microRNA array data. The package uses a limma-like structure to generate the processed data in order to make statistical inferences about differential expression using the linear model features implemented in limma. Standard Bioconductor objects are used so that other packages could be used as well.

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames.

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 contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order.

This package provides a Poisson mixture model is implemented to cluster genes from high-throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).

This package provides fully Bayesian mixture models for differential gene expression.

This is a package for identification of metabolites using high precision mass spectrometry. MS peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists.

The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.