Cargando…
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000Research
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112579/ https://www.ncbi.nlm.nih.gov/pubmed/27909575 http://dx.doi.org/10.12688/f1000research.9501.2 |
_version_ | 1782468029082238976 |
---|---|
author | Lun, Aaron T.L. McCarthy, Davis J. Marioni, John C. |
author_facet | Lun, Aaron T.L. McCarthy, Davis J. Marioni, John C. |
author_sort | Lun, Aaron T.L. |
collection | PubMed |
description | Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines. |
format | Online Article Text |
id | pubmed-5112579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-51125792016-11-30 A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor Lun, Aaron T.L. McCarthy, Davis J. Marioni, John C. F1000Res Software Tool Article Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines. F1000Research 2016-10-31 /pmc/articles/PMC5112579/ /pubmed/27909575 http://dx.doi.org/10.12688/f1000research.9501.2 Text en Copyright: © 2016 Lun ATL et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Lun, Aaron T.L. McCarthy, Davis J. Marioni, John C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor |
title | A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor |
title_full | A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor |
title_fullStr | A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor |
title_full_unstemmed | A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor |
title_short | A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor |
title_sort | step-by-step workflow for low-level analysis of single-cell rna-seq data with bioconductor |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112579/ https://www.ncbi.nlm.nih.gov/pubmed/27909575 http://dx.doi.org/10.12688/f1000research.9501.2 |
work_keys_str_mv | AT lunaarontl astepbystepworkflowforlowlevelanalysisofsinglecellrnaseqdatawithbioconductor AT mccarthydavisj astepbystepworkflowforlowlevelanalysisofsinglecellrnaseqdatawithbioconductor AT marionijohnc astepbystepworkflowforlowlevelanalysisofsinglecellrnaseqdatawithbioconductor AT lunaarontl stepbystepworkflowforlowlevelanalysisofsinglecellrnaseqdatawithbioconductor AT mccarthydavisj stepbystepworkflowforlowlevelanalysisofsinglecellrnaseqdatawithbioconductor AT marionijohnc stepbystepworkflowforlowlevelanalysisofsinglecellrnaseqdatawithbioconductor |