A list by @biology · 1 papers · 0 followers
Min Dai, Xiaobing Pei, Xiu‐Jie Wang
COSG identifies marker genes for cell type distinction in single-cell and spatial transcriptome data with high accuracy and efficiency.
Briefings in Bioinformatics · Jan 5, 2022Peer-reviewedBioinformatics
Papers closest to the centre of this list, not already in it.
Tianyu Liu, Wenxin Long, Zhiyuan Cao, Yuge Wang, Chuan Hua He, Le Zhang, Stephen M. Strittmatter, Hongyu Zhao
CosGeneGate selects non-redundant, cell-type-specific marker genes for single-cell analysis, outperforming existing methods in downstream applications.
Briefings in Bioinformatics · Nov 13, 2024Peer-reviewedBioinformatics
Fang Wang, Shaoheng Liang, Tapsi Kumar, Nicholas E. Navin, Ken Chen
SCMarker identifies cell-type-discriminative markers in scRNA-seq data through unsupervised analysis of gene expression patterns without prior knowledge.
PLoS Computational Biology · Oct 28, 2019Peer-reviewedBioinformatics
Jeffrey M. Pullin, Davis James McCarthy
This study benchmarks 56 marker gene selection methods for scRNA-seq data using real and simulated datasets, finding simple methods like Wilcoxon rank-sum test and t-test to be effective.
Genome biology · Feb 26, 2024Peer-reviewedBioinformatics
Jinjin Chen, Mengbo Li, Dharmesh Dinesh Bhuva, Melissa J. Davis, Anthony T. Papenfuss, Chin Wee Tan
The paper introduces smartid, a method for accurately detecting markers in rare cell populations using a modified TF-IDF approach with Gaussian Mixture Model, improving cell type annotation in single-cell RNA-seq data.
bioRxiv · Nov 10, 2025 · PreprintBioinformatics
Bartol Borozan, Luka Borozan, Domagoj Ševerdija, Domagoj Matijević, Stefan Canzar
The paper proposes a method to identify a small set of marker genes that optimally separate cell types in single-cell RNA-seq data by considering expression variation within cell types.
bioRxiv · Feb 12, 2025 · PreprintBioinformatics
scMAGS selects marker genes from scRNA-seq data for spatial transcriptomics by using cluster validity indices, offering scalability and efficiency.
Computers in Biology and Medicine · Feb 9, 2023Peer-reviewedBioinformatics
Vasilis Ntranos, Lynn Yi, Páll Melsted, Lior S. Pachter
The paper introduces a method to identify cell types and their transcriptional signatures from single-cell RNA-Seq data by analyzing transcript compatibility counts.
Nature Methods · Jan 8, 2019Peer-reviewedBioinformatics
Aleksandr Ianevski, Anil Kumar Giri, Tero A. Aittokallio
ScType automates cell-type identification from scRNA-seq data using marker genes, achieving accurate and unbiased annotations across human and mouse tissues.
Nature Communications · Mar 10, 2022Peer-reviewedSystems Biology