09-25, 13:50–14:20 (Europe/Paris), Louis Armand 1 - Est
Spatial Transcriptomics, named the method of the year by Nature in 2020, offers remarkable visuals of gene expression across tissues and organs, providing valuable insights into biological processes. This talk presents the Squidpy library for analyzing and visualizing spatial molecular data, including demonstrations of gene expression visualization in mouse brain tissue.
Considered the method of the year by Nature in 2020, Spatial Transcriptomics has unveiled remarkable visuals of how genes are expressed across tissues and organs of living organisms. This breakthrough offers valuable insights to scientists, shedding light on the inner workings of biological processes such as brain function and cancer development. In this talk, we will explore how to build such visuals using Squidpy, a python library for the analysis and visualization of spatial molecular data. First, we will take a look at how Spatial Transcriptomics emerged inside bioinformatics and why it became the method of the year. Next, we will have a brief overview of the Squidpy library, as well as practical demonstrations showcasing how to visualize gene expression in mouse brain tissue. Lastly, we will discuss currently challenges on Spatial Transcriptomics. With this talk, you will gain a better understanding of spatial transcriptomics and will learn how to utilize the Squidpy library for visualizing gene expression in tissue samples.
BSc in Information Technology and PhD in Bioinformatics from the Federal University of Rio Grande do Norte (UFRN). Currently, I am Postdoctoral Researcher at Institut Curie. During my academic journey, I have been extensively involved in research projects focusing on bioinformatics and data analysis. Pythonist and headbanger, with 6 years of experience in employing the Python data science and machine learning stack on bioinformatics and personal projects.