Workshop Description

This workshop aims to provide an entry-level introduction to the basic concepts and data analysis tools for single-cell RNA-seq techniques. It will help the attenders obtain a better idea of the important applications of scRNA-seq, the important considerations in designing a scRNA-seq experiment, the major differences between popular technical platforms, and the main steps in preliminary data analysis. It can serve as a starting point to conceive a scRNA-seq study or to analyze a scRNA-seq dataset.

Session 1 (Emily Wu): Intro to scRNA-seq basics

  • Concept of scRNA-seq and main applications
  • Important issues in experimental design
  • Comparison between popular technical platforms
  • Raw data processing

Session 2 (Yerbol Kurmangaliyev): Guided case study using Seurat;

  • Overview of scRNA-Seq analysis and logistics of Seurat;
  • Data preprocessing: loading, QC/filtering, normalization/scaling;
  • Dimensionality reduction and clustering;
  • Identification of marker genes;
  • Visualizations.

Workshop Materials

The first 10 attendees can use the classroom computer, others can attend if using their own laptop. The room is equipped with ethernet jacks and wireless internet.

Instructors

Dr. Igor Mandric is currently a postdoctoral fellow in the labs of Bogdan Pasaniuc and Eran Halperin at UCLA. He completed his M.S in Applied Mathematics at Moldova State University. Before joining UCLA, he earned Ph.D in Bioinformatics under the supervision of Alex Zelikovsky at Georgia State University. His main research interest lays in the area of single-cell RNA sequencing. In particular, he is interested in inferring cell-type specific expression profiles in the settings of low sequencing coverage. Additionally, Igor is also interested in statistical fine-mapping for large-scale genotype-phenotype data. Email: imandric@ucla.edu

Dr. Ying Tang is a postdoc in signaling systems laboratory working with Professor Alexander Hoffmann. His research interest is to develop quantitative understanding on immune systems through mathematical modeling, from the level of intracellular information transmission to cell population dynamics. Prior to joining UCLA, he did PhD study in Shanghai Jiao Tong University and University of California, San Deigo. During PhD, he worked on stochastic process, nonequilibrium statistical physics, and mathematical modeling of E. coli chemotaxis. Email: jamestang23@ucla.edu

Workshop Details

Prerequisites: Basic experience with R would be helpful but not required
Length: 3 days, 3 hrs per day
Level: Introductory
Location: Collaboratory Classroom  (Boyer Hall, 529)
Seats Available: 28

Fall 2019 Dates

November 5, 6, and 7, 2019
1:30 PM to 4:30 PM