Evidence map›Paper›PMID 42779705›Full record

ArticlebioRxiv : the preprint server for biology2026

SMORE: joint dimension reduction and cell population discovery on single-cell methylome data.

Jingwen Deng, Zixi Wang, Wen Tang, Guanyu Hu, Hao Feng

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jingwen DengDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Zixi WangDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Wen TangDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID 0000-0003-3710-9029
Guanyu HuDepartment of Statistics and Probability, Michigan State University, East Lansing, MI, USA.
Hao FengDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID 0000-0003-2243-9949

Funding

Personalized genomics signal deconvolution to improve cell-type level inferenceR35GM154862 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Hao Feng · 2024 to 2026
$1.2M
NIGMS NIH HHS R35 GM154862
6 · The paper itself

Abstract

Single-cell DNA methylation profiling technology captures novel epigenetic data modality but are challenging to analyze because of their heterogeneity, high dimensionality, and ultra-sparsity. Here we present SMORE (

Indexed as

Bayesian factor modelclusteringdimension reductionordinal datasingle-cell DNA methylation

Identifiers

PMID42779705
PMCPMC13596350

What Socratic holds

Textmetadata
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.