LMP1111: Introduction to R and the Analysis of Single Cell Data

Who can attend

No specific courses are required, however, we prefer background knowledge in molecular genetics and biochemistry (such as LMP301H1 / BCH242Y / MGY340H1).

No prior training in the R programming language or the analysis of single-cell RNA-sequencing (scRNA-seq) data is required. Some prior knowledge or completed courses in programming would be beneficial.

Enrollment is open to University of Toronto graduate students and capped at a maximum of 20 students.

Course description

We are in the middle of a revolution. A single cell analysis revolution.

Driven by the desire to unlock the heterogeneity of tissues, methods to profile the transcriptomes of single cells have exploded onto the scene in recent years. Such approaches leverage RNA-sequencing of thousands of single cells (scRNA-seq) to provide a picture of cell types and states that is unprecedented in history. In parallel to developments in scRNA-seq, -omic data types and volume has expanded at record pace.

In order to manage the wave of scRNA-seq data (and other -omic data) that abounds, it is critical that investigators of the future have the skills required to analyze these types of data.

There are two major goals of this module:

1. to introduce you to the R programming language/environment.
2. for you apply your new skills in R to analyze scRNA-seq data. In so doing, using scRNA-seq data as a platform, you will learn in-demand skills that are broadly applicable across industries and -omic data types.

Importantly, as described above, you do not need any prior experience in the R programming language. The intent of this course is to enable your entry into the analysis of scRNA-seq data without any prior knowledge. You will be expected to follow along and write/execute R code/analyses in lecture in real-time.

By the end of this module, you will be able to:

• Describe and use the basic aspects of the R language/environment (such as Data Types and Structures, Plotting and Basic Statistics, Inputting and Outputting Data and Working with R Packages);
• Acquire publicly available scRNA-seq data from data repositories and input these into the R environment.
• Work with commonly available R Packages to analyze scRNA-seq data.
• Apply skills learned in this course to analyze a scRNA-seq dataset of their choosing in R to produce an academic quality data analysis report.

Course coordinator

Dr. Scott Yuzwa

scott.yuzwa@utoronto.ca

lmp.grad@utoronto.ca for administrative queries.

Timings and location

This course will be offered in alternative years starting Fall 2023 (i.e. 2023, 2025, etc).

Timings: Thursdays, 10 am

Location: TBA

Evaluation methods

Class participation - 10%

Quiz 1 – 25%

Quiz 2 – 25%

Final assignment – 40%

Your final assignment will be discussed and assigned during Lecture 2 and will be due two weeks following Lecture 6..

You will be asked to choose a scRNA-seq data set that is publicly available and analyze this dataset in R using the techniques learned in this course. You will then be asked to prepare a 5-page (maximum length) report where you will detail the steps taken to analyze your chosen dataset and what you learned about this dataset (from both biological and technical perspectives).

You will be required to submit your report along with an R code file and an R workspace that the instructor will use to verify your analyses. Grading will include the quality and content of the report, the R code and the reproducibility of the analysis. 

Schedule