A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification
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Abstract
BACKGROUND: The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification. RESULTS: We have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment. CONCLUSIONS: Based on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments.
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The number of cells is crucial for power to find DE/eQTL genes by increasing sensitivity of gene expression detection.
“We show that the number of cells is not only crucial for the power to detect rare cell types37,38 but also for the power to find DE/eQTL genes by increasing the sensitivity of gene expression detection.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [38]
scPower can be combined with specific power analysis tools for cell type annotation to ensure sufficient power.
“To ensure sufficient power for cell type annotation, our framework scPower can be combined with specific power analysis tools for cell type annotation38,82.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [38]
The power to annotate new rare cell types by clustering analysis of scRNA-seq data might have different optimal parameters.
“Furthermore, the power to annotate new rare cell types by clustering analysis of scRNA-seq data might have different optimal parameters38.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [38]
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scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
- Authors
- Abrams, D., Kumar, P., Karuturi, R. K. M., & George, J.
- Affiliations
- Colby College (US)
- The Jackson Laboratory for Genomic Medicine (US)
- Corresponding author
- Karuturi, R. Krishna Murthy
- George, Joshy
- Venue
- BMC Bioinformatics
- Published
- 2019-06
- Volume
- 20
- Issue
- S11
- Pages
- 275-275
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC6551246
- OpenAlex
- W2789792126
- Cited by
- 13
- References
- 14
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 1 · 2018: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Cancer Institute (R25CA174584, P30CA034196)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, experimental design, cell type annotation, sample size determination, marker genes, Seurat
- MeSH terms
- Algorithms, Computer Simulation, Humans, Research Design, Sequence Analysis, RNA, Sample Size, Computational Biology, Gene Expression Profiling, Databases, Genetic, Single-Cell Analysis
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