CellFuse enables Multi-modal Integration of Single-cell and Spatial Proteomics Data for Systems-level Analysis in Cancer

This repo contains code and data to reproduce CellFuse manuscript's figure. As a starter install CellFuse pacakges from https://github.com/karadavis-lab/CellFuse and then download this repo.

Fig 2 Bone marrow (Fig 2A, C, D, E, I, Supplementary Fig 1 and 2)

1. Fig2/BM/Reference/ Fig2_BM_prepare_data.R: Prepare bone marrow for CellFuse
2. Fig2/BM/ BM_CellFuse_Integration.R: Run CellFuse
3. Fig2/BM/BM_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN)
4. Fig2/BM/BM_scVI_scnorama.ipynb: Run scanorama and scVI
5. Fig2/BM/BM_scIB.ipynb: Evaluate methods using scIB and save results
6. Fig2/BM/BM_Data_visualisation.R: tSNE visualization
7. Fig2/BM/Sequential_Feature_drop/Prepare_data.R: Prepare data for evaluating sequential feature drop
8. Fig2/BM/Sequential_Feature_drop/ Run_FastMNN_Seurat_Harmony.R: Run CellFuse, Harmony, Seurat and FastMNN for sequential feature drop
9. Fig2/BM/Sequential_Feature_drop/ BM_scVI_scnorama_feature_drop.ipynb: Run scVI and Scanorama for sequential feature drop
10. Fig2/BM/Sequential_Feature_drop/ BM_scIB_feature_drop.ipynb: Evaluate feature dropping methods using scIB and save results
11. Fig2/BM/Sequential_Feature_drop/ BM_scIB_Data_viz.R: visualize scIB results PBMC (Fig 2B,F,G, H, Supplementary Fig: 3 and 4)
12. Fig2/PBMC/Reference/ Fig2_PBMC_prepare_data.R: Prepare PBMC data for CellFuse
13. Fig2/ PBMC / PBMC_CellFuse_Integration.R: Run CellFuse
14. Fig2/ PBMC /PBMC_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN)
15. Fig2/PBMC/PBMC_scVI_scnorama_feature_drop.ipynb: Run scVI and Scanorama
16. Fig2/PBMC/PBMC_scIB.ipynb: Evaluate methods using scIB and save results
17. Fig2/PBMC/PBMC_Data_visualisation.R: tSNE visualization
18. Fig2/ PBMC/ RunTime_benchmark/ Prepare_data.R: Prepare data
19. Fig2/ PBMC/ RunTime_benchmark/ run_all_methods.txt.R: This file contain info how to run time and memory usage for each method. This file requires following files: a. cellfuse_run_measure.R b. fastmnn_run_measure.R c. seurat_run_measure.R d. harmony_run_measure.R e. scanorama_runtime.py f. scvi_scanvi_runtime.py
20. Fig2/ PBMC/ RunTime_benchmark/ Runtime_Data_viz.R: Visualize runtime and memory usage data

Fig 3 Good et al. CART: Fig 3A-F and Supplementary Fig 5, 6A and B

1. Fig3/ Good_et_al/Reference/ Fig3_CyTOF_prepare_data.R: Prepare CyTOF and CITE-Seq data for CellFuse
2. Fig3/ Good_et_al/CellFuse_Integration_CyTOF.R: Run CellFuse to remove batch effect and integrate CyTOF data from day 7 post-infusion
3. Fig3/ Good_et_al/CellFuse_Integration_CITESeq.R: Run CellFuse to integrate CyTOF and CITE-Seq data
4. Fig3/ Good_et_al/CART_Data_visualisation.R: Visualize data

Fig 3 Domizi et al. CART: Fig 3G and H and Supplementary Fig 6C

1. Fig3/Domizi_et_al/ Data_Analysis.R: this file contains all code for prepaprocessing, CellFuse run and data visualization

Fig 4 HuBMAP CODEX data (Fig. 4A, B, C, D and Supplementary Fig 7)

1. Fig4/CODEX_colorectal/Reference/ CODEX_HuBMAP_prepare_data.R: Prepare CODEX data from annotated and unannotated donor
2. Fig4/ CODEX_colorectal/ CODEX_HuBMAP_CellFuse_Predict.R: Run CellFuse on cells from from annotated and unannotated donor
3. Fig4/ CODEX_colorectal/CODEX_HuBMAP_Data_visualisation.R: Visualize data and prepare figures.
4. Fig4/ CODEX_colorectal/ Benchmarking/Astir/Astrir.ipynb: Run Astir
5. Fig4/ CODEX_colorectal/ Benchmarking/SpatialAnno.R: run SpatialAnno
6. Fig4/ CODEX_colorectal/ CODEX_HuBMAP_Benchmark.R: Benchmarking CellFuse against CELESTA, SVM, SpatialAnno, Astir and Seurat using cells from annotated donors and prepare figures.
7. Fig4/ CODEX_colorectal/CODEX_HuBMAP_Suppl_figure_heatmap.R: F1score calculation per celltype per Benchmarking methods and heatmap comparing celltypes from annotated and unannotated donors (Supplementary Fig 7) IMC Breast cancer data (Fig. 4E,F, G and Supplementary Fig 7)
8. Fig4/ IMC_Breast_Cancer/ IMC_prepare_data.R: Prepare CODEX data from annotated and unannotated donor
9. Fig4/ IMC_Breast_Cancer/ IMC_CellFuse_Predict.R: Run CellFuse to predict cell types
10. Fig4/ IMC_Breast_Cancer/ IMC_dat_visualization.R: Visualize data and prepare figures.
11. Fig4/ IMC_Breast_Cancer/ Suppl_Per_Patient_Confusion_Matrix.R: Suppl. Fig8
12. Fig4/ IMC_Breast_Cancer/ Benchmark_random_split.R: Suppl. Fig 9B
13. Fig4/ Concordance.R: Spatial concordance analysis for IMC and CODEX data

Fig 5

1. Fig5/ Reference/ Fig5_CyTOF_Data_prep.R: Prepare CyTOF data from healthy PBMC and healthy colon single cells
2. Fig5/ MIBI_CellFuse_Predict.R: Run CellFuse to predicte cells from colon cancer patients
3. Fig5/ MIBI_PostPrediction.R: Visualize data and prepare figures
4. Fig5/ Predicted_Data/ mask_generation.ipynb: Post CellFuse prediction annotated cell types in segmented images. This will generate Fig5C and D