ArticleResearch square2024
Unsupervised multi-scale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.
Won-Min Song, Chen Ming, Christian V Forst, Bin Zhang
Abstract readPreprint
In one paragraphArticle in Research square, 2024. 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
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1 · What the graph read from itWhat 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 registryThe trial behind it
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3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
4 authors.
Won-Min SongDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
Chen MingFaculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, China.
Christian V ForstDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
Bin ZhangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
Funding
Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's DiseaseU01AG046170 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WANG, MINGHUI, ZHANG, BIN · 2013 to 2022
$26.0MDissect the interplay between sex and APOE at the single cell level to uncover novel pathways, targets and therapeutics for Alzheimer's diseaseRF1AG074010 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CAI, DONGMING, ZHANG, BIN · 2021 to 2021
$3.6MDeciphering the Heterogeneous Response to Influenza by a Multi-Scale Systems ApproachR01AI170112 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CHRISTIAN FORST · 2022 to 2026
$3.1MIntegrative network modeling of bulk and single-cell sequencing data to characterize multi-scale cell architectureR35GM142918 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SONG, WON-MIN · 2021 to 2025
$2.1MMulti-scale analysis of single cell sequencing data to dissect the complexity of influenza infectionsR21AI149013 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FORST, CHRISTIAN, SONG, WON-MIN · 2020 to 2021
$466kNIAID NIH HHS R01 AI170112NIAID NIH HHS R21 AI149013NIA NIH HHS RF1 AG074010NIA NIH HHS U01 AG046170NIGMS NIH HHS R35 GM142918
6 · The paper itselfAbstract
Cell clustering is an essential step in uncovering cellular architectures in single cell RNA-sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. Here, we develop a multi-scale clustering (MSC) approach to construct sparse cell-cell correlation network for identifying
Indexed as
bioinformaticsmulti-scale clusteringscRNA-seqsimilarity network
Identifiers
PMID39764102
PMCPMC11703337
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