This package provides many functions for computing the nonparametric maximum likelihood estimator (NPMLE) for censored and truncated data.

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.

This package provides processed 22 samples from BrainSpan keeping only the information for chromosome 21. Data is stored in the BigWig format and is used for examples in other packages.

This package provides a set of tools to for machine and deep learning in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.

This package provides a client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various administrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details.

This package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

This package provides full genome sequences for Homo sapiens from 1000genomes phase2 reference genome sequence (hs37d5), based on NCBI GRCh37.

This package provides tools for finding bumps in genomic data in order to identify differentially methylated regions in epigenetic epidemiology studies.

This package provides functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets.

This package provides a method for combining single-cell cytometry datasets, which increases the analytical flexibility and the statistical power of the analyses while minimizing technical noise.

This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome.

This package implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches.

The project is intended to support the use of sequins(synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard library for quantitative analysis, modelling, and visualization of spike-in controls.

This package implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.

Explore, diagnose, and compare variant calls using filters. The VariantTools package supports a workflow for loading data, calling single sample variants and tumor-specific somatic mutations or other sample-specific variant types (e.g., RNA editing). Most of the functions operate on alignments (BAM files) or datasets of called variants. The user is expected to have already aligned the reads with a separate tool, e.g., GSNAP via gmapR.

AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. AbseqR empowers the users of abseqPy with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.

This package provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk.

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 works analogous to BiocManager but for Docker images. Use the BiocDockerManager package to install and manage Docker images provided by the Bioconductor project.

This package provides genome wide annotation for E coli strain K12, primarily based on mapping using Entrez Gene identifiers. Entrez Gene is National Center for Biotechnology Information (NCBI)’s database for gene-specific information. Entrez Gene maintains records from genomes which have been completely sequenced, which have an active research community to submit gene-specific information, or which are scheduled for intense sequence analysis.

This package provides high performance functions for row and column operations on sparse matrices. Currently, the optimizations are limited to data in the column sparse format.

This package provides the data for the gene expression enrichment analysis conducted in the package ABAEnrichment. The package includes three datasets which are derived from the Allen Brain Atlas:

1. Gene expression data from Human Brain (adults) averaged across donors,

2. Gene expression data from the Developing Human Brain pooled into five age categories and averaged across donors, and

3. a developmental effect score based on the Developing Human Brain expression data.

All datasets are restricted to protein coding genes.

This package implements a method that aims to identify enhancers on large scale. The STARR-seq data consists of two sequencing datasets of the same targets in a specific genome. The input sequences show which regions where tested for enhancers. Significant enriched peaks i.e. a lot more sequences in one region than in the input where enhancers in the genomic DNA are, can be identified. So the approach pursued is to call peak every region in which there is a lot more (significant in a binomial model) STARR-seq signal than input signal and propose an enhancer at that very same position. Enhancers then are called weak or strong dependent of there degree of enrichment in comparison to input.

EBarrays provides tools for the analysis of replicated/unreplicated microarray data.