i-stLearn: An interactive platform for spatial transcriptomics analysis

Abstract

SummaryEmerging spatial transcriptomics technologies (e.g. Visium, Slideseq, or MERFISH) have made it possible to keep the spatial information while profiling gene expression of every cell/spatial-spot. Integrating expression values, spatial coordinates, and imaging data type promises to bring more biological insights but is still technically challenging. A user-friendly software tool to enable interactive analysis of spatial transcriptomic data by the broader community is lacking. We present i-stLearn, an all-in-on web application with an analysis pipeline and interactive visualization for studying spatial heterogeneity using spatial transcriptomics data. i-stLearn can be used to gain biological insights from tissue through key analysis types cell-cell interaction analysis, clustering, and trajectory inference. Using functions, users can interactively segment the tissue and identify cellular state transition or cellular communications in a heterogeneous biological sample. Availabilityi-stLearn is freely available at https://github.com/BiomedicalMachineLearning/stlearn_interactive as local web application and we also provide a demo online web service available at https://i-stlearn-demo.web.app. Contactquan.nguyen@imb.uq.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.