This package is an R program for the subset-based analysis of heterogeneous traits and disease subtypes. ASSET allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant.

This package implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. It includes a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results.

Oscope is a oscillatory genes identifier in unsynchronized single cell RNA-seq. This statistical pipeline has been developed to identify and recover the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group.

This package provides statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motifs and amino acid sequence motifs. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors.

This package provides full genome sequences for Drosophila melanogaster (Fly) as provided by UCSC (dm6) and stored in Biostrings objects.

RCAS aims to be a standalone RNA-centric annotation system that provides intuitive reports and publication-ready graphics. This package provides the R library implementing most of the pipeline's features.

biomaRt provides an interface to a growing collection of databases implementing the http://www.biomart.org. The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. Examples of BioMart databases are Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase and dbSNP mapped to Ensembl. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from gene annotation to database mining.

This package uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. For this it uses segmented data from the QDNAseq package, which in turn uses a number of dependencies to turn mapped reads into segmented data. ACE will run QDNAseq or use its output rds-file of segmented data. It will subsequently run through all samples in the object(s), for which it will create individual subdirectories. For each sample, it will calculate how well the segments fit (the relative error) to integer copy numbers for each percentage of tumor cells (cells with divergent segments).

This package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. It computes allele counts at individual variants (SNPs/SNVs), implements extensive QC (quality control) steps to remove problematic variants, and utilizes a Bayesian framework to identify statistically significant allele-specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.

This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions.

Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of transcription factors and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states.

The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0.

This package provides user interface and database connection code for annotation data packages using SQLite data storage.

This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported.

This package provides a collection of compression filters for use with HDF5 datasets.

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.

This package adductomicsR processes data generated by the second stage of mass spectrometry (MS2) to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies level mass spectral peaks from first stage of mass spectrometry (MS1) data.

BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via DTU (differential transcript usage), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via MCMC (Markov chain Monte Carlo) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.

This package provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.

This package defines classes for "class discovery" in the OOMPA project. Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance.

This package provides an integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. Currently only Affymetrix oligonucleotide analysis is supported.

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

This is a package for the assessment and comparison of the performance of risk prediction (survival) models.