This package offers functionality for taking methtuple or Bismark outputs to calculate ASM scores and compute DAMEs regions. It also offers nice visualization of methyl-circle plots.

R-dsb improves protein expression analysis in droplet-based single-cell studies. The package specifically addresses noise in raw protein UMI counts from methods like CITE-seq. It identifies and removes two main sources of noise—protein-specific noise from unbound antibodies and droplet/cell-specific noise. The package is applicable to various methods, including CITE-seq, REAP-seq, ASAP-seq, TEA-seq, and Mission Bioplatform data. Check the vignette for tutorials on integrating dsb with Seurat and Bioconductor, and using dsb in Python.

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 provides per-exon and per-gene read counts computed for selected genes from RNA-seq data that were presented in the article 'Conservation of an RNA regulatory map between Drosophila and mammals' by Brooks et al., Genome Research 2011.

This package provides a suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects. The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.

This package provides an interface to simulate metabolic reconstruction from the BiGG database and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs.

This package defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform.

This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.

This R package is providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest.

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>.

This package implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows.

This package provides summarized MinION sequencing data for Salmonella Typhi published by Ashton et al. in 2015. Three replicate runs are each provided as Fast5Summary objects.

This package provides a set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.

The aim of TCGAbiolinks is:

1. facilitate GDC open-access data retrieval;

2. prepare the data using the appropriate pre-processing strategies;

3. provide the means to carry out different standard analyses, and;

4. to easily reproduce earlier research results.

In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

satuRn provides a framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

Store minor allele frequency data from the Phase 1 of the 1000 Genomes Project for the human genome version hs37d5.

This package provides a differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test, a Kruskal-Wallis test, a generalized linear model, or a correlation test. All tests report p-values and Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs.

This package is a collection of Strand-seq data. The main purpose is to demonstrate functionalities of the breakpointR package.

This package exposes an annotation database generated from Ensembl.

The MBECS provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects.

This package provides tools for Bayesian integrated analysis of Affymetrix GeneChips.

This package provides functions for visualizing hypergraphs.

This package provides a simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.

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.