Dario Righelli1
Francesca Calanca2,3
Emanuela Scafuri2,3
1 Department of Electrical Engineer and Information Technology, University of Naples “Federico II”, Italy
2 Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Italy
3 Institute for Applied Mathematics “M. Picone”, IAC-CNR, Naples, Italy
This tutorial strives to be of interest to experimental biologists wanting to analyze their data and to bioinformaticians approaching spatial transcriptomics data.
We use as a “textbook” the online book “Orchestrating Spatial Transcriptomics Analysis with Bioconductor” (OSTA), which provides reproducible examples and discussion on computational analysis workflows for spatial omics data using Bioconductor in R.
Like the book, this tutorial has been structured to provide a theoretical and practical overview of spatial transcriptomics analysis. Specifically, we will examine Cosmx’s molecule-based approach, paying close attention to the key stages of the analysis process.
In particular, participants will learn: * Overview of Spatial Transcriptomics’ methodologies (spot-based & molecule-based) * Cosmx’s approach in detail * Downloading, uploading and data preprocessing with Bioconductor * Annotation of cell types * Identification of marker genes * Analysis of Neighbourhood-based gene expression * Cell-cell interaction using Neighbourhood graph * Point-pattern Analysis
| Activity | Time |
|---|---|
| Introduction and Setup | 9:00-9:30 |
| Theoretical overview | 9:30-10:30 |
| Introduction to the workflow | 10:30-11:00 |
| Break | 11:00-11:15 |
| Exploratory Data Analysis and Quality Control (QC) | 11:15-11:45 |
| Cell type annotation & marker genes identification | 11:45-12:15 |
| Neighbourhood-based analyses | 12:15-13:00 |
| Downstream Analyses | 13:00-13:45 |
| Q&A + Discussion session | 13:45-14:00 |
Download Docker desktop.
Open the docker app.
Ensure that Docker is running!!
To locally run this tutorial in a Docker container, pull the Docker image via
docker pull ghcr.io/drighelli/bbcc_osta:latest
docker tag ghcr.io/drighelli/bbcc_osta:latest bbcc_ostaNote. The download can take some time, the image is around 9 GB.
and then run the image via
docker run -e PASSWORD=bioc -p 8787:8787 bbcc_ostaOnce running, navigate to http://localhost:8787/ in your browser and login with username rstudio and password bioc.
This tutorial can be installed like an ordinary R package via:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
BiocManager::install("Bioconductor/BBCC.OSTA",
dependencies = TRUE,
build_vignettes = TRUE)