Evidence map›Paper›PMID 38405805›Full record

ArticlebioRxiv : the preprint server for biology2024

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

Louise A Huuki-Myers, Kelsey D Montgomery, Sang Ho Kwon, Sophia Cinquemani, Nicholas J Eagles, Daianna Gonzalez-Padilla, Sean K Maden, Joel E Kleinman, Thomas M Hyde, Stephanie C Hicks and 2 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 9 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Louise A Huuki-MyersLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0001-5148-3602
Kelsey D MontgomeryLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0001-8420-0138
Sang Ho KwonLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0001-5328-0956
Sophia CinquemaniLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.
Nicholas J EaglesLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0002-9808-5254
Daianna Gonzalez-PadillaLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0009-0005-8348-3195
Sean K MadenDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.ORCID 0000-0002-2212-4894
Joel E KleinmanLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0002-4210-6052
Thomas M HydeLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0002-8746-3037
Stephanie C HicksDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.ORCID 0000-0002-7858-0231
Kristen R MaynardLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0003-0031-8468
Leonardo Collado-TorresLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.ORCID 0000-0003-2140-308X
Johns Hopkins University · USLieber Institute for Brain Development · US

Funding

Integrative Genomics of the Corticolimbic Circuit in Major Depressive DisorderR01MH111721 · NIMH · JOHNS HOPKINS UNIVERSITY · PI GOES, FERNANDO SAMPAIO, HYDE, THOMAS MICHAEL · 2017 to 2021
$4.2M
Integrative cellular deconvolution of human brain RNA sequencing dataR01MH123183 · NIMH · LIEBER INSTITUTE, INC. · PI HICKS, STEPHANIE CARINNE, MAYNARD, KRISTEN ROSE · 2020 to 2023
$2.3M
NIMH NIH HHS R01 MH111721NIMH NIH HHS R01 MH123183
6 · The paper itself

Abstract

Background: Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results: A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Conclusions:

Indexed as

benchmarkDeconvolutionhuman brainimmunofluorescencemulti-assayRNAScopeRNA-seqsmFISHsnRNA-seqtranscriptomics

Identifiers

PMID38405805
PMCPMC10888823
OpenAlexW4391756636

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.