Annotation of spatially resolved single-cell data with STELLAR

STELLAR (Spatially-resolved Transcriptomics with Ellipsoid Decomposition and Latent Actualization for Reconstruction) is a computational tool developed by researchers at the Broad Institute of MIT and Harvard for annotating spatially resolved single-cell data. It uses a combination of machine learning algorithms and image analysis techniques to identify cell types and characterize gene expression patterns within individual cells.

To use STELLAR, researchers first generate spatially resolved single-cell data using techniques such as spatial transcriptomics or in situ sequencing. This data typically consists of spatial coordinates for each cell, as well as information on gene expression levels for a large number of genes.

STELLAR then uses a number of different algorithms to analyze this data and identify cell types. First, it uses an ellipsoid decomposition algorithm to model the spatial distribution of cells within the tissue sample. This allows it to identify clusters of cells that are likely to be of the same type.

Next, STELLAR uses a latent actualization algorithm to model the gene expression patterns within each cell. This allows it to identify genes that are expressed at high levels within specific cell types, and to assign cell type labels to individual cells based on their gene expression profiles.

Overall, STELLAR provides a powerful tool for analyzing spatially resolved single-cell data, and has the potential to significantly advance our understanding of cellular organization and function within complex tissues.