geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.
scRNA-seq datasets are increasingly used to identify gene panels that can be probed using alternative technologies, such as spatial transcriptomics, where choosing the best subset of genes is vital. Existing methods are limited by a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cells. We introduce an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. Our approach outperforms existing strategies and can resolve cell types and subtle cell state differences.
1. Comprehensive Integration of Single-Cell Data.
2. Integrated analysis of multimodal single-cell data.
3. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
4. jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.
5. Single-cell and spatial transcriptomics approaches of cardiovascular development and disease.
1. Single cell RNA-seq analysis of melanoma
2. Comprehensive integration of single-cell data
3. Single-cell RNA-seq reveals distinct maturation stages of the Paneth cell lineage
4. Integrated analysis of multimodal single-cell data
5. Molecular, Spatial and Functional Single-Cell Profiling of the Hypothalamic Preoptic Region