BreastCancerSpatialProteomics

Data description

DSP_protein_discovery_cohort.txt - contains DSP protein counts used to generate Figures 1c-d, 2, 3, and 4.

bulk_RNA_data.txt - contains the bulk RNA expression data used for model comparison in Figure 4.

DSP_protien_validation_cohort.txt - contains the DSP protein counts for the 29 cases in the text cohort used to generate Figure 5 and corresponding Extended Data 9.

perimetric_complexity.txt - contains the perimetric complexity values per region used for Extended Data 8.

cd45_R_data.txt - contain the on-treatment CD45 IHC and DSP values using to compare single feature CD45 IHC and DSP in Figure 6.

cohort_features.xlsx - contains raw data for each clinical covariate for each case, used to generate Figure 6d.

Code description

volcano_waterfall.R is an R v3.6.0 cript with example functions used to run the linear mixed-effect models and generate the volcano plots and waterfall plots shown in Figures 2a, 2b, 2d, 2e as well as the Extended Data 1,3,4,5,7, and 9.

classifier.py is a Python v3.7.4 script used for model comparisons and evaluation of performance via internal cross-validation in Figure 4.

classifier_test.py is used for evaluation of model performance in an independent validation cohort as shown in Figure 5.

DSP_IHC_comparison.ipynb is a Python v3.7.4 Jupyter Notebook used for evaluation of the single feature CD45 IHC and DSP models.

Steps to run the code

To run the code please first make sure that you have miniconda or conda installed.

Install require softwares

Next step is to create a conda env bcsp and install Python v3.7.4, R v3.6.0 and required packages using the following command.

conda create --name bcsp -c conda-forge -c conda-forge -c r python=3.7.4 jupyter pandas=0.25.1 numpy=1.17.2 scipy=1.3.1 scikit-learn=0.21.3 pystan=2.19.1.1 seaborn=0.9.0 statsmodels=0.10.1 arviz=0.10.0 matplotlib=3.1.2 blackcellmagic r-base=3.6.0 r-vioplot=0.3.2 r-zoo=1.8-6 r-sm=2.2-5.6 r-ggrepel=0.8.1 r-ggplot2=3.3.0 r-reshape=0.8.8 r-tidyr=1.0.3 r-lmerTest=3.1-0 r-lme4=1.1-21 r-matrix=1.2-17 r-dplyr=0.8.5 r-ggeffects

Activate the conda env using this command

conda activate bcsp

Now clone the git repo

git clone https://github.com/cancersysbio/BreastCancerSpatialProteomics.git BCSP && cd BCSP

And run Jupyter Notebook using

jupeter notebook

And locate the DSP_IHC_comparison.ipynb Notebook in the Jupyter browser.

Other scripts can be run as follows:

Rscript volcano_waterfall.R

python classifier.py

python classifier_test.py