Amelia Schroeder

TL;DR: Analyzing five spatially resolved transcriptomics datasets using SpaGCN, it is shown it can detect genes with much more enriched spatial expression patterns than existing methods and are transferrable and can be utilized to study spatial variation of gene expression in other datasets.

TL;DR: In this paper, the authors focus on the statistical and machine learning aspects for spatially resolved transcriptomics (SRT) data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity.

TL;DR: SpaGCN as mentioned in this paper is a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis, which can detect genes with much more enriched spatial expression patterns than existing methods.

TL;DR: In this article , a multi-use deep learning approach is proposed to integrate multiple CITE-seq and single-cell RNA-seq (scRNA-seq) datasets, which allows the utilization of as many data as possible to uncover cell population heterogeneity.

TL;DR: CeLEry as discussed by the authors is a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq.