Single cell RNAseq data for beginners

In this course I will briefly mention the folllwing:

1. How traditional RNAseq analysis is done with limma, edgeR, deseq2. Next I will tell
2. How Single Cell protocols are organized in R, like Scater, Seurat, Monocle, what’s the difference
3. Major goals of a single cell protocols: Finding cell clusters, finding markers of clustered cells.
4. Introduction to a specific elements: pseudotiming, inferring differentiation patterns (a concept of velocity)
5. Predicting cell-signalling (CellChat, SingleCellSignal packages).

PhD students in biology, medical biology, medicine or farmacy- those who professionally connected to biology. Students should have an elementary knowledge of using R packages for the RNAseq analysis, preferably passed the course on “gene expression data for beginners” (or equivalent level). The course is practically oriented. It means students will exercise in writing R-scripts and analyzing the real expression data.

General knowledge of cell biology, regulation of a gene expression, and basics of R scripting is required. Knowledge of the regular RNAseq analysis is an advantage.

Students should have personal laptop with administrative rights (able to install software and packages) with 8 GM RAM at least. It is implied that students have already installed R and R-studio, have access to the internet.
Students will be referred to online documentation of each package.

Expect homework on discussed subjects. This will also form the basis to pass the course.

January 20, 21,27, 28 and February 3 (09:00-11:00 hours)

90% participation required, 90% delivered homework required.

Note: Minimally 10 students required. Maximum 25 students accepted.