Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
0numbers the graph read from it
0cells of the map it votes in
9citing 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.
Rujia DaiDepartment of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.ORCID 0000-0002-9979-3295
Tianyao ChuMOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.ORCID 0000-0002-2915-5298
Ming ZhangMOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.
Xuan WangMOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.ORCID 0009-0005-0519-8727
Donghoon LeeCenter for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0003-0453-6059
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.ORCID 0000-0001-9874-2907
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.ORCID 0000-0002-0957-0224
Panos RoussosCenter for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-4640-6239
Yue WangDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Xusheng WangDepartment of Biology, University of North Dakota, Grand Forks, ND, USA.ORCID 0000-0002-1759-9588
Dalila PintoDepartments of Psychiatry and 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.ORCID 0000-0002-8769-0846
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.ORCID 0000-0003-2752-1028
PsychENCODE consortium
Chao ChenMOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.ORCID 0000-0002-5114-2282
Chunyu LiuDepartment of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.ORCID 0000-0002-5986-4415
Funding
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
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
Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per 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 expressions. 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 did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.
Indexed as
BrainSingle-Cell AnalysisTranscriptomeAdultAlzheimer DiseaseGene Expression ProfilingGenome-Wide Association StudyHumansSchizophreniaSequence Analysis, RNA
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.
Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. · full record | OpenQuestion