Evidence map›Paper›PMID 41256547›Full record

ArticlebioRxiv : the preprint server for biology2025

A knowledge-guided approach to recovering important rare signals from high-dimensional single-cell data.

Zhenghao Zhang, Jiamin Chen, Haoran Wu, Kelly Yichen Li, Peter D Adams, Pamela Itkin-Ansari, Kevin Y Yip

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

7 authors.

Zhenghao ZhangDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.ORCID 0000-0001-7866-1106
Jiamin ChenDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Haoran WuSchool of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Kelly Yichen LiCenter for Data Science and Artificial Intelligence, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.ORCID 0000-0001-7377-5729
Peter D AdamsCancer Genome and Epigenetics Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.ORCID 0000-0002-0684-1770
Pamela Itkin-AnsariDevelopment, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.
Kevin Y YipDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.ORCID 0000-0001-5516-9944

Funding

Tumor Microenvironment and Cancer ImmunologyP30CA030199 · NCI · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI Michael Jackson · 1985 to 2026
$107.2M
Spatial Mapping Senescent Cells Across the Mouse Lifespan by Multiplex Transcriptomics and EpigenomicsU54AG079758 · NIA · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI PETER D. ADAMS · 2022 to 2026
$12.1M
Rewiring T cell exhaustion with immune checkpoint blockade therapyR01CA287114 · NCI · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI Linda Mac Pherson Bradley · 2024 to 2026
$3.4M
Novel Neuroprotective Roles for the Alzheimer's Disease Risk Gene SORLA in Tau Pathology and PathogenesisR01AG085498 · NIA · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI Timothy Yikai Huang · 2024 to 2026
$2.6M
A knowledge-guided analysis approach to recovering rare signals from single-cell transcriptomic dataR21GM159319 · NIGMS · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI YIP, YUK-LAP KEVIN · 2025 to 2025
$536k
NCI NIH HHS P30 CA030199NCI NIH HHS R01 CA287114NIA NIH HHS R01 AG085498NIA NIH HHS U54 AG079758NIGMS NIH HHS R21 GM159319
6 · The paper itself

Abstract

Single-cell transcriptomic data are high-dimensional, with many genes profiled in each cell. Dimensionality reduction is routinely applied to improve interpretability, remove noise and redundancy, and enable visualization. Most existing methods aim at preserving the most prominent data properties, which can lead to omission of rare but important signals. Here we propose a novel framework that uses knowledge-derived genes of interest to guide dimensionality reduction, which can help cluster rare cells and separate highly similar cell sub-populations. We demonstrate the utility of our framework in identifying endocrine cell subtypes in the pancreatic islet, highly similar hematopoietic sub-populations, and rare senescent cells.

Identifiers

PMID41256547
PMCPMC12621906

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Registered trials

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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.