Evidence map›Paper›PMID 40294099›Full record

ArticlePLoS computational biology2025

scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.

Xiaopeng Wei, Jingli Wu, Gaoshi Li, Jiafei Liu, Xi Wu, Chang He

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PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

6 authors.

Xiaopeng WeiGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.
Jingli WuGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.ORCID 0000-0003-4951-6908
Gaoshi LiGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.
Jiafei LiuGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.
Xi WuGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.
Chang HeGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, Guangxi, China.

Funding

Guangxi Natural Science FoundationNational Natural Science Foundation of China
6 · The paper itself

Abstract

It is a significant step for single cell analysis to identify cell types through clustering single-cell RNA sequencing (scRNA-seq) data. However, great challenges still remain due to the inherent high-dimensionality, noise, and sparsity of scRNA-seq data. In this study, scPEDSSC, a deep sparse subspace clustering method based on proximity enhancement, is put forward. The self-expression matrix (SEM), learned from the deep auto-encoder with two part generalized gamma (TPGG) distribution, are adopted to generate the similarity matrix along with its second power. Compared with eight state-of-the-art single-cell clustering methods on twelve real biological datasets, the proposed method scPEDSSC can achieve superior performance in most datasets, which has been verified through a number of experiments.

Indexed as

RNA-SeqSequence Analysis, RNASingle-Cell AnalysisAlgorithmsCluster AnalysisComputational BiologyHumansSingle-Cell Gene Expression Analysis

Identifiers

PMID40294099
PMCPMC12036905

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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.