ArticleComputational and structural biotechnology journal2021
scDA: Single cell discriminant analysis for single-cell RNA sequencing data.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AugGCL: Multimodal graph learning for spatial transcriptomics analysis with enhanced gene and morphological data.PLoS computational biology · 2026Article
- Immune Cell Dynamics in Cardiac Diseases: Insights From Single-Cell Sequencing.Journal of cellular and molecular medicine · 2025Review
- Spatially informed graph transformers for spatially resolved transcriptomics.Communications biology · 2025Article
- Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics.Computational and structural biotechnology journal · 2024Review
- Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.Briefings in bioinformatics · 2024Article
- Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.Briefings in bioinformatics · 2024Article
- Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics.Briefings in bioinformatics · 2024Article
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5 authors.
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Abstract
Single-cell RNA-sequencing (scRNA-seq) techniques provide unprecedented opportunities to investigate phenotypic and molecular heterogeneity in complex biological systems. However, profiling massive amounts of cells brings great computational challenges to accurately and efficiently characterize diverse cell populations. Single cell discriminant analysis (scDA) solves this problem by simultaneously identifying cell groups and discriminant metagenes based on the construction of cell-by-cell representation graph, and then using them to annotate unlabeled cells in data. We demonstrate scDA is effective to determine cell types, revealing the overall variabilities between cells from eleven data sets. scDA also outperforms several state-of-the-art methods when inferring the labels of new samples. In particular, we found scDA less sensitive to drop-out events and capable to label a mass of cells within or across datasets after learning even from a small set of data. The scDA approach offers a new way to efficiently analyze scRNA-seq profiles of large size or from different batches. scDA was implemented and freely available at https://github.com/ZCCQQWork/scDA.
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Registered trials
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