Represents the OpenAPI v2 Azul API as an R object for performing requests. The infrastructure uses the AnVIL and rapiclient packages. Users can connect to either the AnVIL or Human Cell Atlas Data Explorers.

Full reference nuclear genome sequences for Vitis vinifera subsp. vinifera PN40024 (derived from Pinot Noir and close to homozygosity after 6-9 rounds of selfing) as assembled by the IGGP (version 12Xv0) and available at the URGI (INRA).

Full genome sequences for Pan troglodytes (Chimp) as provided by UCSC (panTro3, Oct. 2010) and stored in Biostrings objects. The sequences are the same as in BSgenome.Ptroglodytes.UCSC.panTro3, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

iFull genome sequences for Apis mellifera (Honey Bee) as provided by BeeBase (assembly4, Feb. 2008) and stored in Biostrings objects.

This package provides a tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data.

Full genome sequences for Pan paniscus (Bonobo) as provided by UCSC (panPan1, May 2012) and stored in Biostrings objects.

This package is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data.

Example whole genome bisulfite data for the bsseq package.

This R package provide functions that are used in the BREW3R workflow. This mainly contains a function that extend a gtf as GRanges using information from another gtf (also as GRanges). The process allows to extend gene annotation without increasing the overlap between gene ids.

Full genome sequences for Arabidopsis thaliana as provided by TAIR (snapshot from April 23, 2008) and stored in Biostrings objects.

Full genome sequences for Drosophila virilis (assembly dvir_caf1, GenBank assembly accession GCA_000005245.1) as provided by Ensembl and stored in Biostrings objects.

It contains pre-compiled Gene Ontology gene sets for all organisms available on the Ensembl database. It also includes GO gene sets for organisms on Ensembl Plants, Ensembl Metazoa, Ensembl Fungi and Ensembl Protists. The data was collected with the biomaRt package.

This package provides a package containing an environment representing the Bsubtilis.CDF file.

Full genome sequences for Mus musculus (Mouse) as provided by UCSC (genome mm39, based on assembly GRCm39) and stored in Biostrings objects.

BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript.

Experiment data package. The set were prepared using microarray images of human mesenchymal cells treated with various supplements. This package is intended to provide example data to test functionality provided by blima.

The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples.

Full genome sequences for Macaca mulatta (Rhesus) as provided by UCSC (rheMac10, Feb. 2019) and stored in Biostrings objects.

Example data files and use cases for processing Illumina BeadArray expression data using Bioconductor.

Full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm8, Feb. 2006) and stored in Biostrings objects.

Full genome sequences for Gasterosteus aculeatus (Stickleback) as provided by UCSC (gasAcu1, Feb. 2006) and stored in Biostrings objects. The sequences are the same as in BSgenome.Gaculeatus.UCSC.gasAcu1, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations.

Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.

The package contains data of the Primary Blood Cancer Encyclopedia (PACE) project together with a complete executable transcript of the statistical analysis and reproduces figures presented in the paper "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oleś M, Lu J et al., J. Clin. Invest. (2018) 128(1):427-445. doi:10.1172/JCI93801.