Best practices for single-cell histone modification analysis Article Swipe
Félix Raimundo
,
Pacôme Prompsy
,
Jean‐Philippe Vert
,
Céline Vallot
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.09.21.508811
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.09.21.508811
Background Single-cell histone post translation modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues, and are likely to unlock our understanding of various epigenetic mechanisms involved in development or diseases. Running an scHTPM experiment and analyzing the data produced remains, however, a challenging task since few consensus guidelines exist currently regarding good practices for experimental design and data analysis pipelines. Methods We perform a computational benchmark to assess the impact of experimental parameters and of the data analysis pipeline on the ability of the cell representation produced to recapitulate known biological similarities. We run more than ten thousands experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. Results The analysis of the benchmark results allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation, and that using fixed-size bin counts outperforms annotation-based binning; that dimension reduction methods based on latent semantic indexing outperform others; and that feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed.
Related Topics
Concepts
Computer science
Dimensionality reduction
Benchmark (surveying)
Representation (politics)
Feature selection
Normalization (sociology)
Pipeline (software)
Selection (genetic algorithm)
Probabilistic logic
Data mining
Artificial intelligence
Machine learning
Geodesy
Geography
Political science
Sociology
Politics
Anthropology
Programming language
Law
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.09.21.508811
- https://www.biorxiv.org/content/biorxiv/early/2022/09/22/2022.09.21.508811.full.pdf
- OA Status
- green
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297894174
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297894174Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2022.09.21.508811Digital Object Identifier
- Title
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Best practices for single-cell histone modification analysisWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-09-22Full publication date if available
- Authors
-
Félix Raimundo, Pacôme Prompsy, Jean‐Philippe Vert, Céline VallotList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.09.21.508811Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/09/22/2022.09.21.508811.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2022/09/22/2022.09.21.508811.full.pdfDirect OA link when available
- Concepts
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Computer science, Dimensionality reduction, Benchmark (surveying), Representation (politics), Feature selection, Normalization (sociology), Pipeline (software), Selection (genetic algorithm), Probabilistic logic, Data mining, Artificial intelligence, Machine learning, Geodesy, Geography, Political science, Sociology, Politics, Anthropology, Programming language, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.1101/2022.09.21.508811 |
| publication_date | 2022-09-22 |
| publication_year | 2022 |
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| abstract_inverted_index.The | 139 |
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| cited_by_percentile_year | |
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| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I1294671590, https://openalex.org/I2746051580, https://openalex.org/I80043 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
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| citation_normalized_percentile.is_in_top_10_percent | False |