In this PhD theses I present two new computational models, Cell2fate and CountCorrect, for the analysis of single-cell and spatial transcriptomics data and I show how they can be applied to more effectively map the rules of brain cell development in health and disease.

Cell2fate is an RNA velocity model for inference of transcriptional dynamics from spliced and unspliced RNA counts. Unlike existing models, cell2fate is capable of capturing complex biological processes while still being analytically tractable. This is achieved with an implicit factorization of RNA velocity solutions into modules, which also enhances statistical power and interpretability. By evaluating cell2fate in various real-world scenarios, I demonstrate its enhanced ability to capture complex dynamics and weak dynamical signals in rare and mature cell types. Finally, I apply cell2fate to developing mouse and human brain single cell datasets, where I also demonstrate that RNA velocity modules can be mapped to parallel spatial transcriptomics data.

The CountCorrect model provides new normalization and cell type mapping methods for the Nanostring WTA spatial transcriptomics technology that take into account, background binding of RNA probes. I use CountCorrect to analyze a spatial transcriptomics dataset of the human developing cortex, which revealed spatial autism enrichment patterns, a cortical cell type abundance map and differential gene expression patterns in Cajal-Retzius cells across developmental time and cortical regions.