Joint Dimension Reduction and Spatial Clustering
Joint dimension reduction and spatial clustering
Joint dimension reduction and spatial clustering
tNSE or UMAP plot visualization
Find spatially variable genes
Calculate adjacency matrix by automatically choosing radius
Calculate adjacency matrix by user-specified radius
Calculate the adjacency matrix given the spatial coordinates
getneighborhood_fast
A human dorsolateral prefrontal cortex data
MBIC plot visualization
Read the spatial transcriptomics data measured on 10X Visium platform
Read the scRNAseq data measured on scRNA sequencing platform
Run Weighted Principal Component Analysis
Select the number of clusters
A simulated spatial transcriptomics data
Calculate column-wise or row-wise mean
Calculate column-wise or row-wise sum
Spatial coordinates plot visualization
Return the top n SVGs
Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.