Deconvolving the contributions of cell-type heterogeneity on cortical gene expression
Patrick E, Taga M, Ergun A, Ng B, Casazza W, Cimpean M, Yung C, Schneider JA et al.. PLoS computational biology, 2020-08-17
Topic: Brain proteins, microglia and lab models
Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Patrick, E., Taga, M., Ergun, A., Ng, B., Casazza, W., Cimpean, M., et al. (2020). Deconvolving the contributions of cell-type heterogeneity on cortical gene expression. PLoS computational biology. https://doi.org/10.1371/journal.pcbi.1008120
Vancouver
Patrick E, Taga M, Ergun A, Ng B, Casazza W, Cimpean M, et al. Deconvolving the contributions of cell-type heterogeneity on cortical gene expression. PLoS computational biology. 2020. doi:10.1371/journal.pcbi.1008120. PMID: 32804935.
BibTeX
@article{patrick2020-32804935,
author = {Patrick, E. and Taga, M. and Ergun, A. and Ng, B. and Casazza, W. and Cimpean, M. and Yung, C. and Schneider, J. A. and others},
title = {{Deconvolving the contributions of cell-type heterogeneity on cortical gene expression}},
journal = {PLoS computational biology},
year = {2020},
doi = {10.1371/journal.pcbi.1008120},
pmid = {32804935},
url = {https://doi.org/10.1371/journal.pcbi.1008120}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear.
New to this subject? Start with our plain-English guide: Genetics →
Study details
- Participants70
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed 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 28 of the 58 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PLoS computational biology) |
| 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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 70 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 32804935) |
| Free to read | 2 / 2 | Free to read |
| Points | 28 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 28 of the 58 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Related
Studies in the library that share something with this one. The line above each title says what.