Cargando…
FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets
Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy control...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687889/ https://www.ncbi.nlm.nih.gov/pubmed/38030970 http://dx.doi.org/10.1186/s12864-023-09822-3 |
_version_ | 1785152065548320768 |
---|---|
author | Berg, Marijn Petoukhov, Ilya van den Ende, Inge Meyer, Kerstin B. Guryev, Victor Vonk, Judith M. Carpaij, Orestes Banchero, Martin Hendriks, Rudi W. van den Berge, Maarten Nawijn, Martijn C. |
author_facet | Berg, Marijn Petoukhov, Ilya van den Ende, Inge Meyer, Kerstin B. Guryev, Victor Vonk, Judith M. Carpaij, Orestes Banchero, Martin Hendriks, Rudi W. van den Berge, Maarten Nawijn, Martijn C. |
author_sort | Berg, Marijn |
collection | PubMed |
description | Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09822-3. |
format | Online Article Text |
id | pubmed-10687889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106878892023-11-30 FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets Berg, Marijn Petoukhov, Ilya van den Ende, Inge Meyer, Kerstin B. Guryev, Victor Vonk, Judith M. Carpaij, Orestes Banchero, Martin Hendriks, Rudi W. van den Berge, Maarten Nawijn, Martijn C. BMC Genomics Software Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09822-3. BioMed Central 2023-11-29 /pmc/articles/PMC10687889/ /pubmed/38030970 http://dx.doi.org/10.1186/s12864-023-09822-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Berg, Marijn Petoukhov, Ilya van den Ende, Inge Meyer, Kerstin B. Guryev, Victor Vonk, Judith M. Carpaij, Orestes Banchero, Martin Hendriks, Rudi W. van den Berge, Maarten Nawijn, Martijn C. FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets |
title | FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets |
title_full | FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets |
title_fullStr | FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets |
title_full_unstemmed | FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets |
title_short | FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets |
title_sort | fastcar: fast correction for ambient rna to facilitate differential gene expression analysis in single-cell rna-sequencing datasets |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687889/ https://www.ncbi.nlm.nih.gov/pubmed/38030970 http://dx.doi.org/10.1186/s12864-023-09822-3 |
work_keys_str_mv | AT bergmarijn fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT petoukhovilya fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT vandenendeinge fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT meyerkerstinb fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT guryevvictor fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT vonkjudithm fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT carpaijorestes fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT bancheromartin fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT hendriksrudiw fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT vandenbergemaarten fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets AT nawijnmartijnc fastcarfastcorrectionforambientrnatofacilitatedifferentialgeneexpressionanalysisinsinglecellrnasequencingdatasets |