Rarr
The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It's design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Rarr aims to implement this specification in R with minimal reliance on an external tools or libraries.
README
Zarr arrays with Rarr ================ Introduction to Rarr Limitations with Rarr Quick start guide Installation and setup Reading a from a local Zarr array Reading from S3 storage Writing to a Zarr array Required system libraries | GitHub Actions | Bioconductor Build Sysytem | Test Coverage | |:--------------:|:-------------:|:-----:| | | | | Introduction to Rarr The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It’s design allows efficient access to…
- Repository
- github.com/huber-group-embl/rarr
Source attribution
- Bioconductor — Rarr
- GitHub — github.com/huber-group-embl/rarr
Related resources
Programmatically access the NIH / NCI Genomic Data Commons RESTful service.
The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package.
Tools for parsing Illumina's microarray output files, including IDAT.
The package imports the result of tRNAscan-SE as a GRanges object.
lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation.
The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.