This repository contains a WebMeV-compatible tool for running normalization on spatial transcriptomics using the spatialGE R package (https://github.com/FridleyLab/spatialGE).
To run this tool, pull the Docker container (see https://github.com/web-mev/mev-spatialge-normalize/pkgs/container/mev-spatialge-normalize). Change to the directory where your data lives, then run:
docker run -d -v $PWD:/work ghcr.io/web-mev/mev-spatialge-normalize:<tag> \
Rscript /usr/local/bin/stnormalize.R \
-f /work/<COUNTS FILE> \
-c /work/<COORDS FILE> \
-s <SAMPLE_NAME> \
-n <METHOD> \
-o <OUTPUT_PREFIX>
If successful, this should create a normalized expression file on your current working directory.
Note:
<tag>
: This is the fully-qualified reference to the Docker image, which is tagged to correspond to the Git commit hash. This ensures each Docker image is clearly linked with a specific state of the repository./work/<COUNTS FILE>
: This is a path to your tab-delimited count file, with genes in rows and barcodes/spots in columns. Since we assume you have changed to the directory containins your raw data and mounted it in the Docker container at/work
, we write this as/work/<COUNTS FILE>
./work/<COORDS FILE>
: This is a path to your tab-delimited coordinates file which has three columns (in order):- The barcodes. These barcodes must match those in the count matrix (the column headers there).
- The x/horizontal position of the spot.
- The y/vertical position of the spot.
As with the counts file, we assume you have changed to the directory containins your raw data and mounted it in the Docker container at
/work
, we write this as/work/<COORDS FILE>
.
<SAMPLE_NAME>
is the name of the sample. We incorporate this name into the name/path of the output normalized expression.<METHOD>
is eitherlog
orSCTransform
, verbatim. Case does not matter.<OUTPUT_PREFIX>
is an optional prefix which will be added to the front of the output file name.