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SplineDV

Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions)

Bioconductor version: 3.24 · Package version: 1.5.0

A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SplineDV")

Details

MaintainerShreyan Gupta <xenon8778@tamu.edu>
AuthorShreyan Gupta [aut, cre] (ORCID: <https://orcid.org/0000-0002-1904-9862>), James Cai [aut] (ORCID: <https://orcid.org/0000-0002-8081-6725>)
LicenseGPL-2
URLhttps://github.com/Xenon8778/SplineDV
Bug Reportshttps://github.com/Xenon8778/SplineDV/issues
Downloads rank314
Source branchdevel
biocViewsDifferentialExpression, FeatureExtraction, GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageSplineDV_1.5.0.tar.gz
Windows binary (x86_64)SplineDV_1.5.0.zip
macOS binary (arm64)SplineDV_1.5.0.tgz
macOS binary (x86_64)SplineDV_1.5.0.tgz
Dependencies

Depends: R (>= 3.5.0)

Imports: plotly, dplyr, scuttle, methods, Biobase, BiocGenerics, S4Vectors, sparseMatrixStats, SingleCellExperiment, SummarizedExperiment, Matrix (>= 1.6.4), utils

Suggests: knitr, DelayedMatrixStats, rmarkdown, BiocStyle, ggplot2, ggpubr, MASS, scales, scRNAseq, testthat (>= 3.0.0)