Evidence map›Paper›PMID 42508407›Full record

ArticleCell reports methods2026

Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.

Rebecca Danning, Zheng Tracy Ke, Xihong Lin, Rong Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 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

5 · Who and what money

Authors and funding

4 authors.

Rebecca DanningCenter for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Zheng Tracy KeDepartment of Statistics, Harvard University, Cambridge, MA 02138, USA.
Xihong LinDepartment of Statistics, Harvard University, Cambridge, MA 02138, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. Electronic address: xihong_lin@harvard.edu.
Rong MaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Electronic address: rongma@hsph.harvard.edu.

Funding

Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Predictive Modeling of the Functional and Phenotypic Impacts of Genetic VariantsU01HG012064 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Manuel Garber, XIHONG LIN · 2021 to 2026
$4.0M
Interdisciplinary training: Statistical Genetics/Genomics and Computational BiologyT32GM135117 · NIGMS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Curtis Huttenhower, XIHONG LIN · 2020 to 2026
$3.1M
Statistical Methods for Integrative Analysis of Large-Scale Whole Genome Sequencing Studies and Biobanks of Common DiseasesR01HL163560 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2022 to 2026
$2.6M
NCI NIH HHS R35 CA197449NHGRI NIH HHS U01 HG012064NHLBI NIH HHS R01 HL163560NIGMS NIH HHS T32 GM135117
6 · The paper itself

Abstract

The prioritization of highly variable genes is an important step in single-cell trajectory inference. However, when variability arises from a continuous latent cell development trajectory, standard methods may fail to differentiate trajectory-relevant genes from uninformative genes. SEEK-VFI (Spectral Ensembling of topic models with Eigenscore for K-agnostic Variable Feature Identification) is an ensemble topic modeling machine learning algorithm for trajectory inference preprocessing that prioritizes trajectory-relevant genes. It outperforms existing methods and identifies key genes that improve trajectory topology reconstruction, enhance visualization, and augment downstream trajectory analyses.

Indexed as

Computational BiologySingle-Cell AnalysisAlgorithmsAnimalsHumansMachine LearningSingle-Cell Gene Expression AnalysisCP: computational biologyensemble learningfeature selectionsingle-cell analysistopic modelingtrajectory inference

Identifiers

PMID42508407
PMCPMC13615488

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.