Spatial Variance Components Analysis (SVCA)

Dependencies

Python

• numpy
• scipy
• pandas
• rpy2 (for the notebooks)
• limix (local version in this repository)

R (for plotting)

• ggplot2
• reshape2
• gplots
• plyr
• pheatmap

Others

• gcc / g++

Installation

Installing limix

SVCA relies on a specific version of limix found in svca_limix. You should first install this package using the setup file in svca_limix.

NB: If you are already a limix user, we recommend you install svca_limix and svca in a dedicated conda environment so there is no interference between your limix versions

cd svca_limix
python setup.py develop

Installing svca

Then install svca

cd ..
cd SVCA
python setup.py develop

Basic usage

Computing spatial variance signatures for single images

Running SVCA on single image and single protein can be done as illustrated in the bash script SVCA/svca/run/call_run_indiv.sh. The script calls the run_indiv.py script with the following inputs:

1. data_dir='../../examples/data/IMC_example/Cy1x7/' directory with IMC input data
2. output_dir='./test_svca' the output of the analysis is saved here
3. protein_index=23 select the protein to be modelled
4. normalisation='quantile' select the normalisation method.

For the analysis of all the images and proteins we recommend to use a cluster, this is explained in the next section.

Computing spatial variance signatures for multiple images

NB: For data format, look at the example in the data/IMC_example directory, which should correspond to your analysis_dir folder

We recommend using a cluster for this.

1. Adapt the file SVCA/svca/run_cluster/run_all_cluster.py, to the queuing system used by your cluster.
2. Your analysis directory analysis_dir should contain one directory per image on which you are fitting svca
3. Each image folder should contain a positions.txt and an expressions.txt. Rows are cells and columns are (x,y) coordinates for the positions and genes for the expressions, with the gene names as the header for the expression file. No header for the positions.
4. Run python run_all_cluster.py in the run_cluster directory.
5. Results are in a results directory in each image directory

Visualising variance signatures

1. Adapt the file SVCA/svca/plot_scripts/call_plot_signatures.sh. in_dir should be your analysis directory and plot_dir the directory in which you want to save your plots.
2. run the file

Cross validation

We recommend using a cluster for this. The procedure is the exact same procedure as the one for computing variance signatures, but the file used is SVCA/svca/run_cluster/run_cross_validation_cluster.py.

Visualising cross validation results

1. Adapt the file SVCA/svca/plot_scripts/plot_r2_cross_validation.R (bottom). working_dir should be your analysis directory and plot_dir the directory in which you want to save your plots.
2. run the file plot_r2_cross_validation.R