ArticleNucleic acids research2019
LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 54 citations in OpenAlex.
- NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.Bioinformatics (Oxford, England) · 2026Article
- Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.bioRxiv : the preprint server for biology · 2026Article
- scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling.Biology direct · 2025Article
- Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.Briefings in bioinformatics · 2024Article
- Flexible modeling of regulatory networks improves transcription factor activity estimation.NPJ systems biology and applications · 2024Article
- Osteomacs promote maintenance of murine hematopoiesis through megakaryocyte-induced upregulation of Embigin and CD166.Stem cell reports · 2024Article
- New Ref-1/APE1 targeted inhibitors demonstrating improved potency for clinical applications in multiple cancer types.Pharmacological research · 2024Article
- Acid-base Homeostasis and Implications to the Phenotypic Behaviors of Cancer.Genomics, proteomics & bioinformatics · 2023Article
- scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.PLoS biology · 2023Article
- Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2023Article
- Single-cell biological network inference using a heterogeneous graph transformer.Nature communications · 2023Article
- Identification of Novel Pathways Regulated by APE1/Ref-1 in Human Retinal Endothelial Cells.International journal of molecular sciences · 2023Article
- Transcriptomic analyses of ovarian clear-cell carcinoma with concurrent endometriosis.Frontiers in endocrinology · 2023Article
- Activation of the integrated stress response (ISR) pathways in response to Ref-1 inhibition in human pancreatic cancer and its tumor microenvironment.Frontiers in medicine · 2023Article
- Distinct functions and transcriptional signatures in orally induced regulatory T cell populations.Frontiers in immunology · 2023Article
- Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.Frontiers in oncology · 2023Article
- scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data.Bioinformatics (Oxford, England) · 2022Article
- Combined heterozygosity of FLT3 ITD, TET2, and DNMT3A results in aggressive leukemia.JCI insight · 2022Article
- Bias Aware Probabilistic Boolean Matrix Factorization.Proceedings of machine learning research · 2022Article
- CCPE: cell cycle pseudotime estimation for single cell RNA-seq data.Nucleic acids research · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.
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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.