CNVMetrics
The CNVMetrics package calculates similarity metrics to facilitate copy number variant comparison among samples and/or methods. Similarity metrics can be employed to compare CNV profiles of genetically unrelated samples as well as those with a common genetic background. Some metrics are based on the shared amplified/deleted regions while other metrics rely on the level of amplification/deletion. The data type used as input is a plain text file containing the genomic position of the copy number variations, as well as the status and/or the log2 ratio values. Finally, a visualization tool is provided to explore resulting metrics.
README
The CNVMetrics package offers multiple quantitative metrics of similarity between copy number profiles. Among these are metrics based on CNV status calls only (amplification/deletion status) or on the level of amplification/deletion. In addition, a visualization tool is provided to explore resulting metrics. Citing ## If you use this package for a publication, we would ask you to cite the following: Belleau P, Deschênes A, Beyaz S et al. CNVMetrics package: Quantifying similarity between copy…
- Repository
- github.com/krasnitzlab/cnvmetrics
Source attribution
- GitHub — github.com/krasnitzlab/cnvmetrics
- Bioconductor — CNVMetrics
Related resources
Tools for parsing Illumina's microarray output files, including IDAT.
SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.
The package imports the result of tRNAscan-SE as a GRanges object.
This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. In genomic analysis where feature identification generates a position value surrounded by a genomic range, such as ChIP-Seq peaks and nucleosome positions, the replication of an experiment may result in slight differences between predicted values. This package enables the conciliation of the results into consensus regions.
Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme().
The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation.