An Integrative Analysis Pipeline for Sequencing and Imaging-Based Gene Expression Data

To develop an integrative computational pipeline to combine the strengths of sequencing and image based single-cell technologies.

The Yuan lab developed a number of computational tools to facilitate the comprehensive exploration of spatial datasets in order to address the rapid development of novel technologies to profile thousands of single-cell transcriptomes in situ. The Hidden Markov Random Field model (HMRF) detects spatial domains with coherent gene expression patterns, as described in their preprint. Giotto, a comprehensive pipeline for spatial transcriptomic data analysis and visualization, is also openly available.