Efficient End-to-End Analysis for Single-Cell RNA-seq data (scend)

Installation

You can install the development version of scend from GitHub with:

if (!requireNamespace("pak")) {
    install.packages("pak",
        repos = sprintf(
            "https://r-lib.github.io/p/pak/devel/%s/%s/%s",
            .Platform$pkgType, R.Version()$os, R.Version()$arch
        )
    )
}
pak::pkg_install("Yunuuuu/scend")

Introduction

scrapper is a package provides methods for an end-to-end analysis of a single-cell RNA-sequencing (scRNA-seq) analysis, starting from the count matrix and finishes with clusters, markers, and various embeddings (i.e., t-SNE and UMAP). It’s pretty fast and memory-efficient. The package serves as a bridging tool connecting the scrapper package with either Seurat or SingleCellExperiment objects.

Available functions

functions	Description
librarySizeFactors	Compute library size factors
logNormCounts	Log-transformed normalized expression
modelGeneVar	Model per-gene variances in expression
runPCA	Principal component analysis
runMNN	Fast mutual nearest neighbors correction
subSample	Subsample cells based on their neighbors
runTSNE	t-stochastic neighbor embedding
runUMAP	uniform manifold approximation and projection
runDiffusionMap	diffusion map
clusterSNNGraph	Graph-based clustering
subCluster	Find subclusters
scoreMarkers	Score marker genes
scoreFeatureSet	Score feature set activity for each cell
infercnv	Run infercnv workflow
scenic	Single-Cell Regulatory Network Inference and Clustering