Cell-type-specific co-expression inference from single cell RNA-sequencing data
Su C, Xu Z, Shan X, Cai B, Zhao H, Zhang J.. Nature communications, 2023-08-10
Topic: Brain proteins, microglia and lab models
The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer's disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Su, C., Xu, Z., Shan, X., Cai, B., Zhao, H., & Zhang, J. (2023). Cell-type-specific co-expression inference from single cell RNA-sequencing data. Nature communications. https://doi.org/10.1038/s41467-023-40503-7
Vancouver
Su C, Xu Z, Shan X, Cai B, Zhao H, Zhang J. Cell-type-specific co-expression inference from single cell RNA-sequencing data. Nature communications. 2023. doi:10.1038/s41467-023-40503-7. PMID: 37563115.
BibTeX
@article{su2023-37563115,
author = {Su, C. and Xu, Z. and Shan, X. and Cai, B. and Zhao, H. and Zhang, J.},
title = {{Cell-type-specific co-expression inference from single cell RNA-sequencing data}},
journal = {Nature communications},
year = {2023},
doi = {10.1038/s41467-023-40503-7},
pmid = {37563115},
url = {https://doi.org/10.1038/s41467-023-40503-7}
}
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Key findings
- For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods.
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Study details
- Participants19
- Evidence strengthLow
How this study scored 53/100
Published in a leading specialty journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 31 of the 58 points we could judge it on, which is 53 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Nature communications) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 1 / 4 | 19 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37563115) |
| Free to read | 2 / 2 | Free to read |
| Points | 31 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 31 of the 58 points we could judge it on is 53 out of 100. |
| Total | 53 / 100 | — |
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