Registration Required -  *Attendance in person*

Welcome to the Peer-to-Peer Teaching Spring Season 2023. These sessions are taught by our own community members to fill knowledge gaps and keep up with the accelerated pace at which bioinformatics grows as well as other data analysis methodologies. 

This session dedicated to single cell RNAseq data analysis, will be led by our colleague Ana Lledo Delgado, MD. who is a postdoctoral associate in the Dr. Herold lab at the Immunobiology Department.

Description:

Omics data is transforming the way we understand science and opening new insights to many different scientific fields. Single cell RNA sequencing (scRNA-seq) allows us to analyze gene expression patterns in individual cells, providing a more detailed understanding of the cellular heterogeneity and diversity within a tissue or organism.

Seurat is a widely used R package developed to accomplish the full analysis of annotated scRNA-seq data. Experiments with complex designs (e.g., multiple conditions, time points, intersection between conditions) can sometimes be difficult to analyze with this established package. In this workshop you will learn how to overcome some of these difficulties and fine-tune the original Seurat code, so it may address the specificities of your own experimental design.

You will learn how to:

• process human PBMCs with different hashtag using Seurat package.

• add different metadata to the main data frame to analyze multiple conditions.

• follow the main steps in the data process before comparing different conditions (normalization, scale, find clusters, run UMAP, perform batch correction…)

• join different files to have a unique file to analyze.

• make different clusters or subclusters to validate or to discover new information.

• find the differentially expressed genes based on the conditions that you want to define.

Requirements: this workshop will be a demonstration. No need to bring your laptop. Previous knowledge of R/Rstudio is helpful but not necessary. Registration is required to attend.