Identifying cell states in single-cell RNA-seq data at statistically maximal resolution Article Swipe
Pascal Grobecker
,
Erik van Nimwegen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.31.564980
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.31.564980
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 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, dimensionalityreduction, 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 C ellstates 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, C ellstates almost perfectly recovers optimal partitions. On real data, C ellstates 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 C ellstates 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 C ellstates 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 marker genes at each branch of this hierarchy, and to visualize these results.
Related Topics
Concepts
Cluster analysis
Normalization (sociology)
Population
Partition (number theory)
Computer science
Expression (computer science)
Data mining
Noise (video)
Computational biology
Algorithm
Artificial intelligence
Pattern recognition (psychology)
Mathematics
Biology
Combinatorics
Demography
Sociology
Programming language
Anthropology
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.10.31.564980
- https://www.biorxiv.org/content/biorxiv/early/2023/11/03/2023.10.31.564980.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388283166
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388283166Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.10.31.564980Digital Object Identifier
- Title
-
Identifying cell states in single-cell RNA-seq data at statistically maximal resolutionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-03Full publication date if available
- Authors
-
Pascal Grobecker, Erik van NimwegenList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.10.31.564980Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/11/03/2023.10.31.564980.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/11/03/2023.10.31.564980.full.pdfDirect OA link when available
- Concepts
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Cluster analysis, Normalization (sociology), Population, Partition (number theory), Computer science, Expression (computer science), Data mining, Noise (video), Computational biology, Algorithm, Artificial intelligence, Pattern recognition (psychology), Mathematics, Biology, Combinatorics, Demography, Sociology, Programming language, Anthropology, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works_count | 35 |
| abstract_inverted_index.C | 247, 275, 285, 315, 350 |
| abstract_inverted_index.a | 7, 18, 39, 59, 118, 184, 244, 300, 356, 367 |
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| abstract_inverted_index.it | 125 |
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| abstract_inverted_index.data | 51, 255 |
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| abstract_inverted_index.tree | 369 |
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| abstract_inverted_index.zero | 266 |
| abstract_inverted_index.(UMI) | 153 |
| abstract_inverted_index.Given | 148 |
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| abstract_inverted_index.count | 343 |
| abstract_inverted_index.data, | 224, 284 |
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| abstract_inverted_index.cells, | 105 |
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| abstract_inverted_index.marker | 378 |
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| abstract_inverted_index.cluster | 263 |
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| abstract_inverted_index.dataset | 41, 205 |
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| abstract_inverted_index.methods | 64, 80 |
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| abstract_inverted_index.optimal | 260, 280 |
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| abstract_inverted_index.problem | 226 |
| abstract_inverted_index.provide | 355 |
| abstract_inverted_index.subsets | 74, 163 |
| abstract_inverted_index.toolbox | 358 |
| abstract_inverted_index.tunable | 122, 267 |
| abstract_inverted_index.without | 206 |
| abstract_inverted_index.Abstract | 0 |
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| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.accurate | 25 |
| abstract_inverted_index.addition | 347 |
| abstract_inverted_index.concrete | 131 |
| abstract_inverted_index.dataset, | 158 |
| abstract_inverted_index.directly | 251 |
| abstract_inverted_index.distance | 101 |
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| abstract_inverted_index.identify | 374 |
| abstract_inverted_index.measures | 99 |
| abstract_inverted_index.methods. | 141 |
| abstract_inverted_index.molecule | 151 |
| abstract_inverted_index.multiple | 87 |
| abstract_inverted_index.operates | 250 |
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| abstract_inverted_index.results. | 390 |
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| abstract_inverted_index.software | 360 |
| abstract_inverted_index.solution | 234, 242 |
| abstract_inverted_index.sparsity | 47 |
| abstract_inverted_index.Moreover, | 304 |
| abstract_inverted_index.annotated | 298 |
| abstract_inverted_index.clusters, | 372 |
| abstract_inverted_index.datasets, | 274 |
| abstract_inverted_index.different | 63 |
| abstract_inverted_index.diversity | 29, 309 |
| abstract_inverted_index.ellstates | 248, 276, 286, 316, 351 |
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| abstract_inverted_index.generally | 81 |
| abstract_inverted_index.important | 377 |
| abstract_inverted_index.maximally | 199 |
| abstract_inverted_index.obtaining | 23 |
| abstract_inverted_index.partition | 159, 194, 261 |
| abstract_inverted_index.perfectly | 278 |
| abstract_inverted_index.profiles, | 78 |
| abstract_inverted_index.scRNA-seq | 50, 114, 157, 223 |
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| abstract_inverted_index.technical | 328 |
| abstract_inverted_index.variation | 13 |
| abstract_inverted_index.virtually | 127 |
| abstract_inverted_index.visualize | 388 |
| abstract_inverted_index.cellstates | 365 |
| abstract_inverted_index.clustering | 71 |
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| abstract_inverted_index.filtering, | 92 |
| abstract_inverted_index.hierarchy, | 385 |
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| abstract_inverted_index.population | 19 |
| abstract_inverted_index.properties | 112 |
| abstract_inverted_index.rigorously | 83 |
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| abstract_inverted_index.sequencing | 3 |
| abstract_inverted_index.similarity | 103 |
| abstract_inverted_index.structure. | 212 |
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| abstract_inverted_index.challenging | 44 |
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| abstract_inverted_index.corresponds | 182 |
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| abstract_inverted_index.partitions. | 281 |
| abstract_inverted_index.principles. | 237 |
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| abstract_inverted_index.higher-order | 371 |
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| abstract_inverted_index.systematically | 318 |
| abstract_inverted_index.indistinguishable, | 178 |
| abstract_inverted_index.dimensionalityreduction, | 95 |
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| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.70528924 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |