Identifying cell states in single-cell RNA-seq data at statistically maximal resolution Article Swipe
Pascal Grobecker
,
Thomas Sakoparnig
,
Erik van Nimwegen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1012224
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1012224
Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with ‘similar’ expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.
Related Topics
Concepts
RNA-Seq
Cell
Single-cell analysis
Computational biology
RNA
Transcriptome
Biology
Genetics
Gene expression
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pcbi.1012224
- https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012224&type=printable
- OA Status
- gold
- Cited By
- 7
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400579193
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400579193Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pcbi.1012224Digital Object Identifier
- Title
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Identifying cell states in single-cell RNA-seq data at statistically maximal resolutionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-12Full publication date if available
- Authors
-
Pascal Grobecker, Thomas Sakoparnig, Erik van NimwegenList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pcbi.1012224Publisher landing page
- PDF URL
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https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012224&type=printableDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012224&type=printableDirect OA link when available
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RNA-Seq, Cell, Single-cell analysis, Computational biology, RNA, Transcriptome, Biology, Genetics, Gene expression, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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