Get Started

Stephanie Hicks

Create a copy of this repository

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Creating a repository with the template can be done in the following way:

From there, you can follow these instructions. However do not optionally select “Include all branches” as you do not need this for your own projects.

Software

I will make heavy use of R, so you should have R installed. You can obtain R from the Comprehensive R Archive Network. There are versions available for Mac, Windows, and Unix/Linux. This software is required for this course.

It is important that you have the latest version of R installed. I will be using R version 4.2.1. You can determine what version of R you have by starting up R and typing into the console R.version.string and hitting the return/enter key. If you do not have the latest version of R installed, go to CRAN and download and install the latest version.

I will also make use of the RStudio interactive development environment (IDE). RStudio requires that R be installed, and so is an “add-on” to R. You can obtain the RStudio Desktop for free from the RStudio web site.

Install packages

These materials use Bioconductor version 3.15. This is the current ‘release’ version of Bioconductor, which can be installed following these instructions.

For example, you can then install a subset of the packages necessary for these tutorials using the following:

library(BiocManager)
install(c("SingleCellExperiment", "TENxPBMCData", "scater", "scran",
  "scry", "mbkmeans"))

Please see the DESCRIPTION for a full list of dependencies.

Schedule

Day (time) Schedule Topics (👩‍💻 Lecture, 🌴 Lab )
Module 1 Single-cell Data Science
Jul 5 (1-4pm) 1-1:25pm 👋 👩‍💻 Introduction to single-cell [slides]
1:25-1:30pm 5min break; prepare for working in R/RStudio
1:30-2pm 👩‍💻 Getting single-cell data and SingleCellExperiment class [html] [Rmd]
2-2:05pm 5min break; pick 1 of 3 labs below to read through until 2:30pm
🌴 Lab (Getting single-cell data) [html]
🌴 Lab (SingleCellExperiment class) [html]
🌴 Lab (Importing counts from salmon alevin into R/Bioc) [html]
2:30-3pm 👩‍💻 Best practices for single-cell data science [slides]
3-3:15pm 👩‍💻 Overview of single-cell analysis in R/Bioconductor [html] [Rmd]
3:15-3:20m 5min break; pick 1 of 3 labs below to read through until 3:50pm
🌴 Lab (Quality control) [html]
🌴 Lab (Normalization) [html]
🌴 Lab (Feature selection) [html]
3:50-4pm Wrap-up; next steps
Jul 6 (10:30am-12pm) 10:30-11am 👩‍💻 More single-cell data science [slides]
11-11:15am 👩‍💻 Dealing with large single-cell data [html] [Rmd]
11:15-11:20am 5min break; pick 1 of labs below to read through until 11:50pm
🌴 Lab (Dim reduction) [html]
🌴 Lab (Clustering) [html]
🌴 Lab (Marker gene detection) [html]
🌴 Lab (Cell type annotation) [html]
🌴 Lab (Python for R users) [html] [Rmd]
11:50am-12pm Wrap-up; next steps
Module 2 Spatial Transcriptomics Data Science
Jul 6 (1-2:30pm) 1-1:25pm 👩‍💻 Introduction to spatially-resolved transcriptomics (SRT) [slides]
1:25-1:30pm 5min break
1:30-2pm 👩‍💻 SpatialExperiment class and spatially variable genes [slides] [html] [Rmd]
2-2:05pm 5min break; pick 1 of labs below to read through until 2:25pm
🌴 Lab (Human DLPFC workflow) [html]
🌴 Lab (Mouse coronal workflow) [html]
2:25-2:30pm Wrap-up; next steps
Jul 7 (9-10:30am) 9-9:50am 👩‍💻 Python for R Users [html] [Rmd]
9:50-10am 10min break
10-10:30am Open question time; Wrap-up