In this computational lab, we will go through data analysis and visualization for spatial transcriptomics data using the example data in the DBiT-seq Paper.
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Download "DBiT_seq_analysis.R" under "3-6-2024_ L7-1 _ Spatial transcriptomics mapping (1)/" from Files on Canvas.
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Upload the files to your project folder on HPC:
Open McCleary OnDemand at beng469.ycrc.yale.edu in a browser window, and go to Files menu and click open your project folder (/gpfs/gibbs/project/beng469/beng469_YourNetID).
Then create a new directory called "Lab7_1" and upload the files ("DBiT_seq_analysis.R") under this new directory.
- Open OOD at beng469.ycrc.yale.edu in your web browser (make sure that you are on Yale Secure Network or Yale VPN).
- Launch an Rstudio-server session: Go to the Rstudio-server initialization page, and specify the parameters/resources as follows:
| Parameters | Values |
|---|---|
| R version | R/4.2.0-foss-2020b |
| Number of hours | 2 |
| Number of CPU cores per node | 1 |
| Memory per CPU core in GiB | 10 |
| Partitions | devel / day / education |
Then click Launch to launch an Rstudio session, and connect the Rstudio session once it’s started
- Open the R code:
Once you are inside Rstudio, use the file navigation panel at the bottom right to click open your project/ folder then the Lab7_1/ folder you created, then click open “DBiT_seq_analysis.R” .