https://github.com/bioconductor-source/splinedv
Science Score: 26.0%
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Repository
Basic Info
- Host: GitHub
- Owner: bioconductor-source
- License: gpl-2.0
- Language: R
- Default Branch: devel
- Size: 16.5 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Spline-DV
A spline-based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions.
Why to use Spline-DV?
One of the most intuitive ways to evaluate a gene expression change is using Differential Expression (DE) analysis. Traditionally, DE analysis focuses on identifying genes that are up- or down-regulated (increased or decreased expression) between conditions, typically employing a basic mean-difference approach. We propose a paradigm shift that acknowledges the central role of gene expression variability in cellular function and challenges the current dominance of mean-based DE analysis in single-cell studies. We suggest that scRNA-seq data analysis should embrace the role of inherent gene expression variability in defining cellular function and move beyond mean-based approaches.
Installation
R
if (!require("devtools")) install.packages("devtools")
devtools::install_github("Xenon8778/SplineDV")
Tutorial - Spline-DV
Loading scRNAseq count example data
The example data is borrowed from an experimental Nkx2-1 Gene knockout scRNA-seq study by Liebler et al. [1] ```R
Load Data
library(SplineDV) WTcount <- get(data("WTcount", package = 'SplineDV')) # WT Sample KOcount <- get(data("KOcount", package = 'SplineDV')) # KO Sample ```
Running Spline-DV
For the analysis, the test data (X) is always use in contrast with the control data (Y).
R
DV_res <- DV_splinefit(X = KO_count, Y = WT_count, ncells = 3, ncounts = 200)
head(DV_res)
Tutorial - Spline-HVG
```R
Loading Data
WTcount <- get(data("WTcount", package = 'SplineDV')) # WT Sample
Running Spline-HVG
HVGres <- HVGsplinefit(WTcount, nHVGs = 100, ncells = 3, ncounts = 200) head(HVGres) ```
References
- Liebler JM, Marconett CN, Juul N, et al. Combinations of differentiation markers distinguish subpopulations of alveolar epithelial cells in adult lung. Am J Physiol Lung Cell Mol Physiol. 2016;310(2):L114-L120. doi:10.1152/ajplung.00337.2015
Owner
- Name: (WIP DEV) Bioconductor Packages
- Login: bioconductor-source
- Kind: organization
- Email: maintainer@bioconductor.org
- Website: https://bioconductor.org
- Repositories: 1
- Profile: https://github.com/bioconductor-source
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Dependencies
- R >= 3.5.0 depends
- Biobase * imports
- Matrix >= 1.6.4 imports
- SingleCellExperiment * imports
- SummarizedExperiment * imports
- dplyr * imports
- methods * imports
- plotly * imports
- scuttle * imports
- sparseMatrixStats * imports
- utils * imports
- BiocStyle * suggests
- MASS * suggests
- ggplot2 * suggests
- ggpubr * suggests
- knitr * suggests
- rmarkdown * suggests
- scales * suggests
- testthat >= 3.0.0 suggests