Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.03.27.534291v1?rss=1
Authors: Pham, D., Balderson, B., Nguyen, Q.
Abstract:
Emerging spatial transcriptomics technologies (e.g. Visium, Slide-seq, 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.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
