ArticlePLoS computational biology2018
A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- scIRT: Imputation and Dimensionality Reduction for Single-Cell RNA-Seq Data by Combining NMF with SMOTE.International journal of molecular sciences · 2026Article
- Distinctive CD39Nature communications · 2024Article
- Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease.Scientific reports · 2024Article
- Accelerated Aging and Microsatellite Instability in Recessive Dystrophic Epidermolysis Bullosa-Associated Cutaneous Squamous Cell Carcinoma.The Journal of investigative dermatology · 2024Article
- scCURE identifies cell types responding to immunotherapy and enables outcome prediction.Cell reports methods · 2023Article
- Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes.Journal of personalized medicine · 2023Article
- Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.Military Medical Research · 2022Review
- A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Article
- coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.PLoS computational biology · 2021Article
- Automatic cell type identification methods for single-cell RNA sequencing.Computational and structural biotechnology journal · 2021Review
- Gene signatures from scRNA-seq accurately quantify mast cells in biopsies in asthma.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2020Article
- Single-cell RNA-seq clustering: datasets, models, and algorithms.RNA biology · 2020Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.Scientific reports · 2019Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.BMC genomics · 2019Article
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC.
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Registered trials
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.