Evidence map›Paper›PMID 36993743›Full record

ArticlebioRxiv : the preprint server for biology2023

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Rujia Dai, Tianyao Chu, Ming Zhang, Xuan Wang, Alexandre Jourdon, Feinan Wu, Jessica Mariani, Flora M Vaccarino, Donghoon Lee, John F Fullard and 10 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Rujia DaiDepartment of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
Tianyao ChuCenter for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
Ming ZhangCenter for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
Xuan WangCenter for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
Alexandre JourdonChild Study Center, Yale University, New Haven, CT, USA.
Feinan WuChild Study Center, Yale University, New Haven, CT, USA.
Jessica MarianiChild Study Center, Yale University, New Haven, CT, USA.
Flora M VaccarinoChild Study Center, Yale University, New Haven, CT, USA.
Donghoon LeeCenter for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
John F FullardCenter for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Gabriel E HoffmanCenter for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Panos RoussosCenter for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Yue WangDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA.
Xusheng WangDepartment of Biology, University of North Dakota, Grand Forks, ND, USA.
Dalila PintoDepartment of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Sidney H WangCenter for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Chunling ZhangDepartment of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA.
PsychENCODE consortium
Chao ChenCenter for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
Chunyu LiuDepartment of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.

Funding

Functional Genomics of Human Brain DevelopmentP50MH106934 · NIMH · YALE UNIVERSITY · PI SESTAN, NENAD · 2014 to 2019
$7.9M
Cis-Regulatory Epigenome Mappings in SchizophreniaU01MH103392 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AKBARIAN, SCHAHRAM, SKLAR, PAMELA · 2014 to 2017
$7.6M
Molecular Profiling of SchizophreniaR01MH110921 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CHESS, ANDREW J, ROUSSOS, PANAGIOTIS · 2016 to 2020
$6.9M
Transcriptional and Epigenetic Signatures of Human Brain Development and AutismU01MH103339 · NIMH · YALE UNIVERSITY · PI SESTAN, NENAD, STATE, MATTHEW W. · 2014 to 2017
$6.5M
1/2 Cell Type and Region-Specific Regulatory Networks in Human Brain Development and DisordersU01MH116488 · NIMH · YALE UNIVERSITY · PI SESTAN, NENAD · 2018 to 2022
$6.5M
Discovery and validation of neuronal enhancers as development of psychiatric disorders supplementU01MH116492 · NIMH · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GERSTEIN, MARK BENDER, WENG, ZHIPING · 2018 to 2023
$6.2M
The 3D genome in transcriptional regulation across the postnatal life span, with implications for schizophrenia and bipolar disorderU01MH116442 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AKBARIAN, SCHAHRAM, DRACHEVA, STELLA · 2018 to 2022
$5.9M
2/3 Understanding PTSD through Postmortem Targeted Brain Multi-omicsR01MH117291 · NIMH · LIEBER INSTITUTE, INC. · PI Joel Edward Kleinman · 2018 to 2026
$5.2M
Site 3/3, Understanding PTSD through Postmortem Targeted Brain MultiomicsR01MH117292 · NIMH · MCLEAN HOSPITAL · PI Nikolaos Daskalakis, KERRY J. RESSLER · 2018 to 2026
$5.0M
Dynamic RNA Modifications in human brain development and autismU01MH116441 · NIMH · EMORY UNIVERSITY · PI JIN, PENG · 2018 to 2022
$4.9M
Risk genetic variants and cis regulation of gene expression in Bipolar DisorderR01MH109677 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ROUSSOS, PANAGIOTIS · 2016 to 2020
$4.6M
Sex-specific trajectories in epigenomic regulation of brain patterningR01MH129301 · NIMH · YALE UNIVERSITY · PI FLORA M VACCARINO · 2022 to 2026
$4.5M
NIDA NIH HHS U01 DA048279NIMH NIH HHS P50 MH106934NIMH NIH HHS R01 MH094714NIMH NIH HHS R01 MH105472NIMH NIH HHS R01 MH105898NIMH NIH HHS R01 MH109648NIMH NIH HHS R01 MH109677NIMH NIH HHS R01 MH109715NIMH NIH HHS R01 MH110555NIMH NIH HHS R01 MH110905NIMH NIH HHS R01 MH110920NIMH NIH HHS R01 MH110921NIMH NIH HHS R01 MH110926NIMH NIH HHS R01 MH110927NIMH NIH HHS R01 MH110928NIMH NIH HHS R01 MH111721NIMH NIH HHS R01 MH116529NIMH NIH HHS R01 MH117291NIMH NIH HHS R01 MH117292NIMH NIH HHS R01 MH117293NIMH NIH HHS R01 MH117406NIMH NIH HHS R01 MH121521NIMH NIH HHS R01 MH125516NIMH NIH HHS R01 MH126393NIMH NIH HHS R01 MH126459NIMH NIH HHS R01 MH129301NIMH NIH HHS R21 MH102791NIMH NIH HHS R21 MH103877NIMH NIH HHS R21 MH105853NIMH NIH HHS R21 MH105881NIMH NIH HHS R21 MH109956NIMH NIH HHS R21 MH129817NIMH NIH HHS R56 MH114899NIMH NIH HHS R56 MH114901NIMH NIH HHS R56 MH114911NIMH NIH HHS U01 MH103339NIMH NIH HHS U01 MH103340NIMH NIH HHS U01 MH103346NIMH NIH HHS U01 MH103365NIMH NIH HHS U01 MH103392NIMH NIH HHS U01 MH116438NIMH NIH HHS U01 MH116441NIMH NIH HHS U01 MH116442NIMH NIH HHS U01 MH116487NIMH NIH HHS U01 MH116488NIMH NIH HHS U01 MH116489NIMH NIH HHS U01 MH116492NIMH NIH HHS U01 MH122509NIMH NIH HHS U01 MH122590NIMH NIH HHS U01 MH122591NIMH NIH HHS U01 MH122592NIMH NIH HHS U01 MH122678NIMH NIH HHS U01 MH122681NIMH NIH HHS U01 MH122849
6 · The paper itself

Abstract

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

Identifiers

PMID36993743
PMCPMC10054947

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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 OpenQuestion graph.