The accumulation of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. SCIntRuler, a novel R package, addresses these challenges by guiding the integration of multiple scRNA-seq datasets.
Integrating scRNA-seq datasets can be complex due to various factors, including batch effects and sample diversity. Key decisions – whether to integrate datasets, which method to choose for integration, and how to best handle inherent data discrepancies – are crucial. SCIntRuler offers a statistical metric to aid in these decisions, ensuring more robust and accurate analyses.
Until the next CRAN release is published, install SCIntRuler 0.99.7 directly from GitHub. First install the Bioconductor dependencies, then install SCIntRuler:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("MatrixGenerics", "SingleCellExperiment",
"SummarizedExperiment"))
# Install SCIntRuler 0.99.7 from GitHub
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("yuelyu21/SCIntRuler")
library(SCIntRuler)After SCIntRuler returns to CRAN, the released version will again be available with install.packages("SCIntRuler").
To try our new method, please refer to our getting started with SCIntRuler article for user instructions.
Lyu Y, Sengupta S, Wang X (2024). SCIntRuler: guiding the integration of multiple single-cell RNA-seq datasets. Bioinformatics, 40(9), btae537. doi:10.1093/bioinformatics/btae537