אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Over the past two decades, advancements in gene expression laboratory methods have brought about a level of maturity that allows for the routine examination of gene expression at both the single-cell level and spatially across tissues. However, existing data analysis methods in single-cell sequencing predominantly concentrate on identifying cell clusters or delineating the principal progression line within the data. Spatial transcriptomics analysis primarily focuses on clustering and identifying spatially variable genes. While these methods effectively capture the primary features of the data, they may overlook more subtle processes, potentially involving specific subsets of samples. Also, widely employed techniques do not detect the regulatory activity of microRNAs, which play a crucial role in governing the expression of mRNAs and lncRNAs.

In this talk, I will introduce SPIRAL, an algorithm grounded in Gaussian statistics that is adept at identifying all statistically significant biological processes in single-cell, bulk, and spatial transcriptomics data. I will also present miTEA-HiRes, a method designed to facilitate the evaluation of microRNA activity at a high resolution. Lastly, I will speak about our work in detecting somatic point mutations in bulk RNA-seq samples.