ArticleNucleic acids research2018
A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes.Bioinformatics (Oxford, England) · 2023Article
- DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.PLoS computational biology · 2022Article
- scDA: Single cell discriminant analysis for single-cell RNA sequencing data.Computational and structural biotechnology journal · 2021Article
- Single-cell RNA-seq clustering: datasets, models, and algorithms.RNA biology · 2020Article
- 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
3 authors.
Funding
Abstract
Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes. By solving a unified optimization problem, SparseDC completes all three tasks simultaneously. SparseDC is highly computationally efficient and demonstrates its accuracy on both simulated and real data.
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Registered trials
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