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

Research output: Contribution to journalJournal articlepeer-review

Standard

popsicleR : A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data. / Grandi, Francesco; Caroli, Jimmy; Romano, Oriana; Marchionni, Matteo; Forcato, Mattia; Bicciato, Silvio.

In: Journal of Molecular Biology, Vol. 434, No. 11, 167560, 2022.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Grandi, F, Caroli, J, Romano, O, Marchionni, M, Forcato, M & Bicciato, S 2022, 'popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data', Journal of Molecular Biology, vol. 434, no. 11, 167560. https://doi.org/10.1016/j.jmb.2022.167560

APA

Grandi, F., Caroli, J., Romano, O., Marchionni, M., Forcato, M., & Bicciato, S. (2022). popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data. Journal of Molecular Biology, 434(11), [167560]. https://doi.org/10.1016/j.jmb.2022.167560

Vancouver

Grandi F, Caroli J, Romano O, Marchionni M, Forcato M, Bicciato S. popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data. Journal of Molecular Biology. 2022;434(11). 167560. https://doi.org/10.1016/j.jmb.2022.167560

Author

Grandi, Francesco ; Caroli, Jimmy ; Romano, Oriana ; Marchionni, Matteo ; Forcato, Mattia ; Bicciato, Silvio. / popsicleR : A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data. In: Journal of Molecular Biology. 2022 ; Vol. 434, No. 11.

Bibtex

@article{34c52bf750a94d2285d73826d094ec9d,
title = "popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data",
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.",
keywords = "bioinformatics, data analysis, R language, single cell RNA-sequencing, software tools",
author = "Francesco Grandi and Jimmy Caroli and Oriana Romano and Matteo Marchionni and Mattia Forcato and Silvio Bicciato",
note = "Funding Information: This work was supported by funds from Fondazione AIRC under 5 per Mille 2019 program (ID. 22759) to S.B. and from the PRIN 2017 Project 2017HWTP2K of the Italian Ministry of Education, University and Research and the FAR 2019 ( E54I19002000001 ) and GR-2016-02362451 of the Italian Ministry of Health to M.F.. F.G. is a recipient of a Doctoral Fellowship Progetti di formazione alla ricerca (Bando 2018) from Regione Emilia Romagna. O.R. has been supported by Fondazione Umberto Veronesi (Post-Doctoral Fellowship 2020). We thank Martina Dori and Andrea Grilli for their support in coding the graphical routines of popsicleR. ",
year = "2022",
doi = "10.1016/j.jmb.2022.167560",
language = "English",
volume = "434",
journal = "Journal of Molecular Biology",
issn = "0022-2836",
publisher = "Academic Press",
number = "11",

}

RIS

TY - JOUR

T1 - popsicleR

T2 - A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

AU - Grandi, Francesco

AU - Caroli, Jimmy

AU - Romano, Oriana

AU - Marchionni, Matteo

AU - Forcato, Mattia

AU - Bicciato, Silvio

N1 - Funding Information: This work was supported by funds from Fondazione AIRC under 5 per Mille 2019 program (ID. 22759) to S.B. and from the PRIN 2017 Project 2017HWTP2K of the Italian Ministry of Education, University and Research and the FAR 2019 ( E54I19002000001 ) and GR-2016-02362451 of the Italian Ministry of Health to M.F.. F.G. is a recipient of a Doctoral Fellowship Progetti di formazione alla ricerca (Bando 2018) from Regione Emilia Romagna. O.R. has been supported by Fondazione Umberto Veronesi (Post-Doctoral Fellowship 2020). We thank Martina Dori and Andrea Grilli for their support in coding the graphical routines of popsicleR.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - bioinformatics

KW - data analysis

KW - R language

KW - single cell RNA-sequencing

KW - software tools

U2 - 10.1016/j.jmb.2022.167560

DO - 10.1016/j.jmb.2022.167560

M3 - Journal article

C2 - 35662457

AN - SCOPUS:85127528877

VL - 434

JO - Journal of Molecular Biology

JF - Journal of Molecular Biology

SN - 0022-2836

IS - 11

M1 - 167560

ER -

ID: 306590962