popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell sequencing offers deep insights into tissue complexity and cellular functions.
• Analyzing single-cell RNA sequencing (scRNA-seq) data is computationally intensive and requires specialized expertise.
• Technical artifacts and biological biases in scRNA-seq data necessitate robust quality control (QC) for accurate downstream analysis.

Purpose of the Study:

• To present popsicleR, an R package designed to simplify and standardize the pre-processing and QC of scRNA-seq data.
• To provide an interactive tool for both computational experts and biologists to perform essential scRNA-seq data handling.
• To integrate established methods for QC metric estimation, cell filtering, normalization, and bias removal into a user-friendly package.

Main Methods:

• Developed popsicleR, an R package with wrapper functions for scRNA-seq data analysis.
• Integrated methods for QC metric calculation, low-quality cell filtering, normalization, and batch effect correction.
• Designed the package to accept input from Cell Ranger or raw count matrices from various scRNA-seq technologies.

Main Results:

• popsicleR offers an integrated pipeline for interactive pre-processing and QC of scRNA-seq data.
• The package facilitates the identification and removal of unreliable signals and unwanted variation.
• It supports cell clustering and annotation as part of the analysis workflow.

Conclusions:

• popsicleR significantly lowers the barrier to entry for scRNA-seq data analysis, making advanced QC accessible.
• The package enhances the reliability of scRNA-seq data, leading to more robust biological interpretations.
• Open-source availability and a tutorial promote widespread adoption and reproducible research in single-cell genomics.