DEComplexDisease is designed to find the DEGs for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DEComplexDisease finds the DEGs shared by as many patients. Applying the DEComplexDisease analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures.

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 data from 6 samples across 2 groups from 450k methylation arrays.

This package contains an implementation of AIMS -- Absolute Intrinsic Molecular Subtyping. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data.

The msa package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade.

The polyester package simulates RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression.

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 Mus musculus (Mouse) as provided by UCSC (mm10, December 2011) and stored in Biostrings objects.

This package provides functions that are needed by many other packages on Bioconductor or which replace R functions.

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 provides an SQL-based mass spectrometry (MS) data backend supporting also storage and handling of very large data sets. Objects from this package are supposed to be used with the Spectra Bioconductor package. Through the MsBackendSql with its minimal memory footprint, this package thus provides an alternative MS data representation for very large or remote MS data sets.

This package provides a collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters.

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.

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 can be used to test two sets of gene lists and visualize the results.

This is a package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface).

CelliD is a clustering-free method for extracting per-cell gene signatures from scRNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function.

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

The purpose of biocViews is to create HTML pages that categorize packages in a Bioconductor package repository according to keywords, also known as views, in a controlled vocabulary.

This package provides a library of core pre-processing and normalization routines.

This package lets you carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. It implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.

The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

This package provides tools to visualize oligonucleotide patterns and sequence motif occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature.