Inference after latent variable estimation for single-cell RNA sequencing data Article Swipe
Anna Neufeld
,
Lucy L. Gao
,
Joshua M Popp
,
Alexis Battle
,
Daniela Witten
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/biostatistics/kxac047
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/biostatistics/kxac047
Summary In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell’s state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this article, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study and apply count splitting to a data set of pluripotent stem cells differentiating to cardiomyocytes.
Related Topics
Concepts
Inference
Latent variable
Count data
Computer science
Type I and type II errors
Latent variable model
Context (archaeology)
Poisson distribution
Statistical inference
Data set
Data mining
Algorithm
Latent class model
Statistics
Mathematics
Machine learning
Artificial intelligence
Biology
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/biostatistics/kxac047
- OA Status
- green
- Cited By
- 53
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311239350
All OpenAlex metadata
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https://openalex.org/W4311239350Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/biostatistics/kxac047Digital Object Identifier
- Title
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Inference after latent variable estimation for single-cell RNA sequencing dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-13Full publication date if available
- Authors
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Anna Neufeld, Lucy L. Gao, Joshua M Popp, Alexis Battle, Daniela WittenList of authors in order
- Landing page
-
https://doi.org/10.1093/biostatistics/kxac047Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/12235260Direct OA link when available
- Concepts
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Inference, Latent variable, Count data, Computer science, Type I and type II errors, Latent variable model, Context (archaeology), Poisson distribution, Statistical inference, Data set, Data mining, Algorithm, Latent class model, Statistics, Mathematics, Machine learning, Artificial intelligence, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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53Total citation count in OpenAlex
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2025: 21, 2024: 21, 2023: 9, 2022: 2Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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