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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

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MIT
LICENSE.md

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