SCIntRuler-package

SCIntRuler

The SCIntRuler package addresses the challenges of integrating multiple single-cell RNA-seq (scRNA-seq) datasets. It provides tools to enhance analytical robustness by augmenting sample sizes and reducing batch discrepancies. Developed using the Seurat framework, SCIntRuler includes both existing and novel workflows for single-cell analysis.

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.

Yue Lyu

Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets

SCIntRuler-package function

Why SCIntRuler? Integrating scRNA-seq datasets can be complex due to various factors such as batch effects and sample diversity. SCIntRuler provides a statistical metric to aid in crucial decisions regarding dataset integration, ensuring more robust and a

<ul>
<li>Informed Decision Making: Helps researchers decide on the necessity
of data integration and the most suitable method.</li>
<li>Flexibility: Suitable for various scenarios, accommodating different
levels of data heterogeneity.</li>
<li>Robustness: Enhances analytical robustness in joint analyses of merged
or integrated scRNA-seq datasets.</li>
<li>User-Friendly: Streamlines decision-making processes, simplifying the
complexities involved in scRNA-seq data integration.</li>
</ul>

Features

Refer to the "Getting Started with SCIntRuler" article in the package vignettes for detailed user instructions.

Getting Started

Author

SCIntRuler: Integration of Single-Cell RNA-seq Datasets — SCIntRuler-package

Why SCIntRuler? Integrating scRNA-seq datasets can be complex due to various factors such as batch effects and sample diversity. SCIntRuler provides a statistical metric to aid in crucial decisions regarding dataset integration, ensuring more robust and accurate analyses.

<ul>
<li>Informed Decision Making: Helps researchers decide on the necessity
of data integration and the most suitable method.</li>
<li>Flexibility: Suitable for various scenarios, accommodating different
levels of data heterogeneity.</li>
<li>Robustness: Enhances analytical robustness in joint analyses of merged
or integrated scRNA-seq datasets.</li>
<li>User-Friendly: Streamlines decision-making processes, simplifying the
complexities involved in scRNA-seq data integration.</li>
</ul>

SCIntRuler-package: SCIntRuler: Integration of Single-Cell RNA-seq Datasets

Description

Arguments

Value

Why SCIntRuler? Integrating scRNA-seq datasets can be complex due to various factors such as batch effects and sample diversity. SCIntRuler provides a statistical metric to aid in crucial decisions regarding dataset integration, ensuring more robust and a

Features

Getting Started

Author