This package provides data needed to use the ITALICS package.

This is a package to perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay.

This package provides Affymetrix Human Genome U95 Set annotation data (hgu95av2) assembled using data from public data repositories.

This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions.

This package provides platform design info for Affymetrix Mapping50K_Xba240 (pd.mapping50k.xba240).

The affyPLM provides a package that extends and improves the functionality of the base affy package. For speeding up the runs, it includes routines that make heavy use of compiled code. The central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools are also provided.

This package provides raw data objects to be used for blood cell proportion estimation in minfi and similar packages. The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; for details see Salas et al. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110554.

The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays.

This package exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

BgeeCall allows generating present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee.

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 is a package for saving SummarizedExperiments into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables to quickly get a first annotation of the cell types present in the dataset without requiring prior knowledge. The package also lets you train using own models to predict new cell types based on specific research needs.

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 provides dataset samples (Affymetrix: Expression, Gene, Exon, SNP; NimbleGen: Expression, Tiling) to be used with the oligo package.

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 contains functions and classes that are needed by arrayCGH packages.

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.

This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access.

This package provides rna-seq datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. The Rna-seq data format is explained here https://wiki.nci.nih.gov/display/TCGA/RNASeq+Version+2. The data source is Illumina hiseq Level 3 RSEM normalized expression data from 2015-11-01 snapshot.

LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets.

r-kegggraph is an interface between Kegg Pathway database and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated kgml (Kegg XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc.

This package aims to provide a pipeline for the low-level analysis of gene expression microarray data, primarily focused on the Agilent platform, but which also provides utilities which may be useful for other platforms.

bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data.