Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.

Genome biology - 18 Aug 2023

Swapna Lakshmipuram Seshadri, Huang Michael, Li Yue

Abstract excerpt

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Phenotype
Share to Transcriptome

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Open in PubMed Open full text

Identifiers

PMID: 37596691
DOI: 10.1186/s13059-023-03034-4
PMCID: PMC10436670

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.PMID 37596691Read ↗

Guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomesSemantic proximity Read ↗

GLDADec: marker-gene guided LDA modelling for bulk gene expression deconvolutionSemantic proximity Read ↗

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic dataSemantic proximity Read ↗

scMoE: single-cell mixture of experts for learning hierarchical, cell-type-specific, and interpretable representations from heterogeneous scRNA-seq dataSemantic proximity Read ↗

Tree-structured topic modelling of single-cell gene expression data uncovers hierarchical relationships between immune cell typesSemantic proximity Read ↗

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic dataSemantic proximity Read ↗

TopicVI: A Knowledge-guided deep interpretable model for resolving context-specific gene programsSemantic proximity Read ↗

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysisSemantic proximity Read ↗

scE <sup>2</sup> TM improves single-cell embedding interpretability and reveals cellular perturbation signaturesSemantic proximity Read ↗

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approachSemantic proximity Read ↗

Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signaturesSemantic proximity Read ↗

CellDiffusion: a generative model to annotate single-cell and spatial RNA-seq using bulk referencesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.