Analysis of multiplex imaging data from breast cancer tissue microarrays. Images and large data files available at https://www.synapse.org/#!Synapse:syn50134757/. (Free account required).
Used to generate all figures in paper.
https://github.com/engjen/cycIF_TMAs/blob/main/20220912_JP-IMC-MIBI-TMAs_survival_spatial.ipynb
Image smoothing, registration, single cell segmentation and feature extraction.
https://github.com/engjen/cycIF_TMAs/blob/main/20201005_JP-TMA_Pipeline.py
https://github.com/engjen/cycIF_TMAs/blob/main/20220103_IMC_pipeline.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20220315_MIBI_pipeline.ipynb
Cluster cells based on biomarker mean intensity using the leiden algorithm, and annotate.
Breast cancer datasets:
https://github.com/engjen/cycIF_TMAs/blob/main/20220118_JP-TMA_both_cluster.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20220201_IMC_cluster_Mesmer_both.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20220410_MIBI_cluster.ipynb
Control datasets:
https://github.com/engjen/cycIF_TMAs/blob/main/20240520_NP-DCIS_cluster.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20240523_HER2-TMA_cluster.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20240528_U54-TMA_MIBI_cluster.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20240528_U54-TMA_cycIF_cluster.ipynb
Spatstat package used for Ripley's L, K cross, G cross, Occupancy. spatialLDA used for neighborhood analysis.
https://github.com/engjen/cycIF_TMAs/blob/main/20230419_spatstat_cycIF_TMAs.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/20220922_spatstat_IMC_MIBI.ipynb
https://github.com/engjen/cycIF_TMAs/blob/main/BC_Spatial_LDA_1.ipynb
For QC and ROI selection.
https://github.com/engjen/cycIF_TMAs/blob/main/20201018_JP-TMAs_napari.py
To run the main analysis, instal python3/miniconda, and enter the following in the terminal to set up an analysis environment.
git clone https://github.com/engjen/cycIF_TMAs.git
cd cycIF_TMAs
conda env create -f environment.yml
conda create -n analysis
conda activate analysis
conda install seaborn scikit-learn statsmodels numba pytables pandas ipykernel openpyxl
conda install -c conda-forge jupyterlab matplotlib python-igraph leidenalg scikit-image opencv tifffile libpysal shapely lifelines umap-learn napari scanpy statsmodels nodejs matplotlib-venn
conda install -c anaconda psutil pysal pillow
conda install -c bioconda anndata
Finally, clone my repo for processing, visualization and analysis of multiplex imaging data
git clone https://gitlab.com/engje/mplex_image.git
To run image processing of cycIF images, set up environment to run our mplexable pipeline as described here: https://gitlab.com/engje/mplexable
To run image processing of IMC and MIBI images (image smoothing, segmentation and feature extraction), set up an enviroment to run DeepCell, available here: https://pypi.org/project/DeepCell/
To run spatstat analysis, create an environment with a r kernel.