PopsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data. Issue 11 (15th June 2022)

• Record Type: Journal Article
• Authors: Grandi, Francesco; Caroli, Jimmy; Romano, Oriana; Marchionni, Matteo; Forcato, Mattia; Bicciato, Silvio
• Is Part Of: Journal of molecular biology. Volume 434:Issue 11(2022)
• Publication Date: 2022-06-15
• Subjects: single cell RNA-sequencing -- data analysis -- bioinformatics -- R language -- software tools; QC Quality Controls -- scRNA-seq single cell RNA-sequencing -- HVGs highly variable genes -- t-SNE t-distributed stochastic neighbor embedding -- UMAP Uniform Manifold Approximation and Projection -- HPCA Human Primary Cell Atlas data -- BpEn Blueprint Encode data -- scMCA single-cell Mouse Cell Atlas; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2022.167560
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: popsicleR workflow. The package is composed of seven main functions to perform exploration of quality-control metrics, filtering of low-quality cells, identification of cell doublets, data normalization, removal of technical and biological biases, cell clustering, and cell annotation. Highlights: Pre-processing of single cell RNA-seq data requires both computational skills and biological sensibility. An effective pre-processing requires the manual combination of different computational strategies to quantify QC-metrics. Currently, no set of methods has been unanimously defined to pre-process single cell RNA-seq data. popsicleR is a R package for the interactive pre-processing and quality control of single cell RNA-seq data. popsicleR main functions integrates pre-processing methods derived from widely used computational workflows. Abstract: The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters. Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR .