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| Status |
Public on Dec 10, 2022 |
| Title |
The effect of background noise and its removal on the analysis of single-cell expression data |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
BACKGROUND: 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 swappingevents. Here, we characterize this background noise exemplified by three single-cell RNA-seq(scRNA-seq) and two single-nucleus RNA-seq (snRNA-seq) replicates of mouse kidney cells. For each experiment, kidney cells from two mouse subspecies were pooled, allowing to identify cross-genotype contaminating molecules and estimate the levels of background noise. RESULTS: We find that background noise is highly variable across replicates and individual cells, making up on average 3-35% of the total counts (UMIs) per cell and show that this has a considerable impact on the specificity and detectability of marker genes. In search of the source of the background noise, we find that expression profiles of cell-free droplets are very similar to expression profiles of cross-genotype contamination profiles and hence 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. CONCLUSION: 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.
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| Overall design |
Three scRNA-seq (rep1-3) and two snRNA-seq (nuc2, nuc3) replicates were generated from mouse kidney cells. In each experiment, cells from dissociated kidneys of three mice from different strains (C57BL/6, CAST/EiJ and 129S1/SvImJ) were pooled. The replicates rep2 & nuc2 and rep3 & nuc3 were generated from the same set of mice each.
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| Contributor(s) |
Janssen P, Kliesmete Z, Vieth B, Adiconis X, Simmons S, Marshall J, McCabe C, Heyn H, Levin JZ, Enard W, Hellmann I |
| Citation(s) |
37337297 |
| Submission date |
Nov 28, 2022 |
| Last update date |
Sep 08, 2023 |
| Contact name |
Philipp Janssen |
| E-mail(s) |
janssen@bio.lmu.de
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| Phone |
+4915780795502
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| Organization name |
Ludwig-Maximilians University Munich
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| Department |
Anthropology and Human Genomics, Faculty of Biology
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| Lab |
Enard/Hellmann lab
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| Street address |
Großhaderner Straße 2
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| City |
Planegg-Martinsried |
| ZIP/Postal code |
82152 |
| Country |
Germany |
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| Platforms (1) |
| GPL24247 |
Illumina NovaSeq 6000 (Mus musculus) |
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| Samples (5)
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| Relations |
| BioProject |
PRJNA906016 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE218853_RAW.tar |
1.1 Gb |
(http)(custom) |
TAR (of H5) |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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