Welcome to STGMVA’s documentation!
- Installation
- Tutorial 1: DLPCF 151671 Spatial clustering task
- Tutorial 2: Slide-seqV2 mouse olfactory bulb
- Tutorial 3: Stereo-seq mouse olfactory bulb
- Tutorial 5: Slide-seqV2 mouse hippocampus with 14 clusters
- Tutorial 6: Stereo-seq mouse embryo E10.5_E1S3
- Tutorial 9: 10x Visium mouse brain data from Squidpy package
- Tutorial 10: Multi-sample integration task for mouse brain data
- Tutorial 11: Joint analysis of mouse brain anterior-posterior sections
Overview
In this study, we present STGMVA, a comprehensive analysis toolkit employs a spatiotemporal gaussian mixture variational autoencoder to tackle these tasks effectively. STGMVA consists of two stages: pretraining the gene expression and spatial location using a gaussian mixture model, and learning the embedding vectors through a variational graph autoencoder. Results demonstrate STGMVA surpasses state-of-the-art approaches on various spatial transcriptomics datasets, exhibiting superior performance across different scales and resolutions. Notably, STGMVA achieves the highest clustering accuracy in human brain, mouse hippocampus, and mouse olfactory bulb tissues. Furthermore, STGMVA enhances and denoises gene expression patterns for gene imputation task. Additionally, STGMVA has the capability to correct batch effects and achieve joint analysis when integrating multiple tissue slices.