ShardSeq

ShardSeq is an R package that wraps Seurat and SingleR for automated scRNA-Seq analysis: QC/preprocessing, variance-based clustering, diagnostic visualization, and reference-based cell type annotation.

Installation

Dependences

Before the ShardSeq installation be sure to have installed the following dependences with this commands:

install.packages(c("Seurat", "ggplot2", "dplyr"))

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

BiocManager::install(c("SingleR", "SingleCellExperiment", "celldex"))

If the celldex installation raises errors try:

options(pkgType = "source")
BiocManager::install(c("AnnotationHub", "ExperimentHub"), type = "source", update = FALSE)
BiocManager::install("celldex", type = "source", update = FALSE)

Installing ShardSeq

Download the latest release on GitHub (https://github.com/SalvoE276/ShardSeq/releases).

You can install ShardSeq using the following (change the version if needed):

install.packages("ShardSeq_1.0.0.tar.gz", repos = NULL, type = "source")

Quick Start

library(ShardSeq)

# 1. Load and Preprocess Raw Counts
sce <- LoadAndPreprocess(counts = raw_counts, project = "MySample")

# 2. Run Dimensionality Reduction and Clustering
sce <- runExperiment(sce, resolution = 0.5)

# 3. View Diagnostic Plots
plots <- viewDiagnostics(sce)
print(plots[[5]]) # Cell cycle proportions per cluster

# 4. Annotate Cell Types
sce <- runClusterAnnotation(sce)