Link to CSHL Course Website
Link to Slack channel help
Precourse Recommendations:
The course workshops will use Python, and some practice beforehand is strongly recommended. For those without Python experience at this point, I would recommend purchasing and going through the Codecademy Python 3 Tutorial (focus on Sections 1-6) and the Data Analysis with Pandas Tutorial. These two courses will provide a sufficient background for the course.
I also suggest you get some exposure to some of the mathematical concepts we will teach during the workshop. A great place to start is the following review article from our lab: Manifold learning-based methods for analyzing single-cell RNA-sequencing data.
A full list of pre-course activites can be found at our lab website.
Course data and Colabs:
In this course, we will be using several publicly available and simulated single-cell datasets. All our code will be available on Google Colab. The full drive with data and Colab coding notebooks is accessible here, and the hyperlinks in the schedule below correspond to the slides and code for each lecture. If you are interested in using Google Colab more generally for your work, see guides in the introduction notebook and FAQ.
Single-cell preprocessing:
The data we will be working with is already pre-processed. To learn how to preprocess scRNA-seq data from publicly available data, see this Google Colab. To learn how to preprocess your scRNA data, see this Google Colab.
Saturday-- 3 December 2022
| 9:00 | [Slides] | Single-cell Analysis I-- PHATE/Multiscale PHATE/MELD |
| 11:00 | [Colab] [Answers] | Workshop Single-cell analysis I |
Sunday-- 4 December 2022
| 10:30 | [Slides] | Single-cell Analysis II-- MAGIC/ Pseudotime and Trajectory Net |
| 11:00 | [Colab] [Answers] | Workshop Single-cell analysis II |
Monday-- 5 December 2022
| 14:00 | #slides_link | Special Seminar: Manifolds, flows and dynamics of single cell data |