Simultaneous visualization of cells and marker genes from scRNA-seq studies
Sengupta, D.; Gupta, K.; Ahuja, G.; Chakraborty, T.; Chakraborti, S.; Mittal, A.; Sinha, D.
Show abstract
The complexity of scRNA-sequencing datasets highlights the urgent need for enhanced clustering and visualization methods. Here, we propose Stardust, an iterative, force-directed graph layouting algorithm that enables the simultaneous embedding of cells and marker genes. Stardust, for the first time, allows a single-stop visualization of cells and marker genes on a single 2D map. While Stardust provides its own visualization pipeline, it can be plugged in with state-of-the-art methods such as Uniform Manifold Approximation and Projection (UMAP) and t-Distributed Stochastic Neighbor Embedding (tSNE). We benchmarked Stardust against popular visualization and clustering tools on both scRNA-seq and spatial transcriptomics datasets. In all cases, Stardust performs competitively in identifying and visualizing cell types in an accurate and spatially coherent manner.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench 96%
- SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics 96%
- CellPie: a scalable spatial transcriptomics factor discovery method via joint non-negative matrix factorization 95%
Similar papers in this journal
- ICARUS v3, a massively scalable web server for single cell RNA-seq analysis of millions of cells. 96%
- Sub-Cluster Identification through Semi-SupervisedOptimization of Rare-cell Silhouettes (SCISSORS) in Single-Cell Sequencing 96%
- PRIME: a probabilistic imputation method to reduce dropouteffects in single cell RNA sequencing 96%
Similar papers in this journal
- Simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization 96%
- SHARE-Topic: Bayesian Inerpretable Modelling of Single-Cell Multi-Omic Data 96%
- Giotto, a toolbox for integrative analysis and visualization of spatial expression data 96%
Similar papers in this journal
- CellScope: High-Performance Cell Atlas Workflow with Tree-Structured Representation 96%
- CellMentor: Cell-Type Aware Dimensionality Reduction for Single-cell RNA-Sequencing Data 96%
- FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.