Seminars, events & talks

Thursday, 18th February, 2021, 12:00

Multivariate analysis of single-cell and spatial transcriptomic data


Password: 788137

Recent technological advances in molecular biology allow the sequencing of RNA from individual cells (single-cell RNA-seq) and the joint analysis of gene expression and spatial cellular organization (spatial transcriptomics). Typically, the genes whose expressions are differential between cell states or across the different areas of a tissue slice are identified with univariate (gene-wise) models. However, it may be beneficial to explicitly account for gene dependencies in multivariate statistical models. In this talk, I will show two examples of such models: (i) a graphical model for single-cell RNA-seq and (ii) a co-clustering model for spatial data. I will show how to use the first model to explore the dynamics of transcription factors in development and the second model to jointly identify spatially dependent genes and cell states/types. Finally, I will discuss the advantages of community-based open software development in Bioconductor.

Speaker: Davide Risso; Dept. of Statistical Sciences, Università di Padova, Italy

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