In one paragraphArticle 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
4 authors.
Penghui HuangDepartment of Biostatistics, University of Pittsburgh, De Soto St, Pittsburgh, 15261, PA, USA.ORCID 0009-0009-4623-1115 Manqi CaiDepartment of Biostatistics, University of Pittsburgh, De Soto St, Pittsburgh, 15261, PA, USA.ORCID 0000-0003-3296-1730 Chris McKennanDepartment of Statistics, University of Pittsburgh, S Bouquet St, Pittsburgh, 15213, PA, USA.ORCID 0000-0003-2096-8257 Jiebiao WangDepartment of Biostatistics, University of Pittsburgh, De Soto St, Pittsburgh, 15261, PA, USA.ORCID 0000-0003-3828-4400 Funding
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4MSUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1MEPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3MRISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4MMulti-omic network-directed proteoform discovery, dissection and functional validation to prioritize novel AD therapeutic targetsU01AG061356 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2018 to 2022
$13.7MPathway discovery, validation and compound identification for Alzheimer's disease - SupplementU01AG046152 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2013 to 2017
$13.6MDiscovery of Novel Proteomic Targets in Alzheimer's DiseaseU01AG046161 · NIA · EMORY UNIVERSITY · PI BENNETT, DAVID ALAN, GESCHWIND, DANIEL H · 2014 to 2018
$8.4MGenetic Epidemiology of Cognitive Decline in an Aging Population SampleR01AG030146 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI EVANS, DENIS A · 2007 to 2018
$6.2MExploring the Role of the Brain Epigenome: Cognitive Decline and Life ExperiencesR01AG036042 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2009 to 2013
$4.1MDeconstructing and modeling the single cell architecture of the Alzheimer brainRF1AG057473 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2017 to 2018
$4.0MExploring the Role of the Brain Transcriptome in Cognitive DeclineR01AG036836 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI DE JAGER, PHILIP L · 2011 to 2014
$2.9MIdentifying, validating and targeting AD susceptibility networks in monocytesR01AG048015 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI DE JAGER, PHILIP L · 2014 to 2018
$1.9MNIA NIH HHS P30 AG010161NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG030146NIA NIH HHS R01 AG036042NIA NIH HHS R01 AG036836NIA NIH HHS R01 AG048015NIA NIH HHS R01 AG080590NIA NIH HHS RC2 AG036547NIA NIH HHS RF1 AG057473NIA NIH HHS U01 AG032984NIA NIH HHS U01 AG046152NIA NIH HHS U01 AG046161NIA NIH HHS U01 AG061356NIH HHS R03 OD034501
6 · The paper itselfAbstract
Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets. BLEND learns the most suitable references for each bulk sample by exploring the convex hulls of references and employs a "bag-of-words" representation for bulk count data for deconvolution. To speed up the computation, we provide an efficient EM algorithm for parameter estimation. Notably, BLEND requires no data transformation, normalization, cell type marker gene selection, or reference quality evaluation. Benchmarking studies on both simulated and real human brain data highlight BLEND's superior performance in various scenarios. The analysis of Alzheimer's disease data illustrates BLEND's application in real data and reference resource integration.
Indexed as
Bayesian estimationCellular deconvolutionEM algorithmGibbs samplingMaximum a posteriori estimationSingle-cell RNA sequencing
Identifiers
PMID39149243
PMCPMC11326155
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC
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