Evidence map›Paper›PMID 37808714›Full record

ArticlebioRxiv : the preprint server for biology2023

Guanqun Meng, Yue Pan, Wen Tang, Lijun Zhang, Ying Cui, Fredrick R Schumacher, Ming Wang, Rui Wang, Sijia He, Jeffrey Krischer and 2 more

Open access · greenAbstract 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, 1 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 6 institutions in 1 country.

Guanqun MengDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
Yue PanDepartment of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
Wen TangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
Lijun ZhangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
Ying CuiDepartment of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA.
Fredrick R SchumacherDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
Ming WangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
Rui WangDepartment of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA.
Sijia HeDepartment of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA.
Jeffrey KrischerHealth Informatics Institute, University of South Florida, Tampa, 38105, FL, USA.
Qian LiDepartment of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
Hao FengDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.ORCID 0000-0003-2243-9949
Case Western Reserve University · USSt. Jude Children's Research Hospital · USStanford University · USUniversity Hospitals of Cleveland · USUniversity of Michigan · USUniversity of South Florida · US

Funding

Viral Vector Technology (VVTSR)P30CA021765 · NCI · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Shondra Michelle Miller · 1985 to 2026
$166.9M
NIDDK Network Coordinating UnitU24DK097771 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Shuibing Chen, Jeffrey S. Grethe · 2013 to 2026
$20.9M
NCI NIH HHS P30 CA021765NIDDK NIH HHS U24 DK097771
6 · The paper itself

Abstract

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present

Identifiers

PMID37808714
PMCPMC10557724
OpenAlexW4387167965

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.