B1866
Title: Analysis of multi-modal spatial omics with MISO
Authors: Kyle Coleman - University of Pennsylvania (United States) [presenting]
Jian Hu - Emory University (United States)
Daiwei Zhang - University of Pennsylvania (United States)
Mingyao Li - University of Pennsylvania (United States)
Abstract: Multi-modal spatial omics provide the opportunity to analyze multiple omics and imaging data modalities within a spatial context. A prominent goal in multi-modal spatial omics is the consolidation of features from different modalities into unified embeddings that can be used for downstream analyses. While some methods have been developed to integrate modalities from a limited subset of multi-modal spatial omics experiments, they are not suitable for most technologies, can only take two modalities as input, and require a great deal of hyperparameter tuning. MISO is presented, a feature extraction and spatial clustering algorithm that can be applied to all modalities from any multi-modal spatial omics experiment. MISO first extracts low-dimensional embeddings for each modality using modality-specific multilayer perceptrons trained to minimize spectral clustering and reconstruction loss functions. MISO then constructs features representing the interactions between each pair of modalities by taking the outer product between the modality-specific embeddings. The modality-specific and interaction feature vectors are concatenated to form embeddings coherent with respect to all modalities. When evaluated on a diverse set of multi-modal spatial omics datasets, including spatially resolved transcriptomics, spatial ATAC-RNA-seq, and spatial CITE-seq, MISO is able to accurately integrate different modalities and separate spots into biologically meaningful spatial domains.
