Janssen, PhilippKliesmete, ZaneVieth, Beate I.H.Adiconis, XianSimmons, SeanMarshall, Jamie L.McCabe, CristinHeyn, HolgerLevin, Joshua Z.Enard, WolfgangHellmann, Ines2023-10-302023-10-302023Janssen P, Kliesmete Z, Vieth B, Adiconis X, Simmons S, Marshall J, McCabe C, Heyn H, Levin JZ, Enard W, Hellmann I. The effect of background noise and its removal on the analysis of single-cell expression data. Genome Biol. 2023 Jun 19;24(1):140. DOI: 10.1186/s13059-023-02978-x1474-7596http://hdl.handle.net/10230/58180Background: In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Results: Here, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3-35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. Conclusions: Our findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.application/pdfeng© The Author(s) 2023. 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/. The Creative Commons Public Domain Dedication waiver (http://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.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13059-023-02978-xSingle-cell RNA-sequencingBackground noiseAmbient RNABarcode swappingCorrection method comparison(Gold) standard scRNA-seq data setinfo:eu-repo/semantics/openAccess