{"doi":"10.5281/zenodo.18088974","title":"CellFuse enables Multi-modal Integration of Single-cell and Spatial Proteomics Data for Systems-level Analysis in Cancer","abstract":"Koladiya A, Good Z, Varra SR, Domizi P, Bendall SC, Davis KL. CellFuse Enables Multimodal Integration of Single-cell and Spatial Proteomics Data for Systems-level Analysis in Cancer. Cancer Research. 2026. DOI: 10.1158/0008-5472.CAN-25-3699.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) Fig2/BM/Reference/ Fig2_BM_prepare_data.R: Prepare bone marrow for CellFuse Fig2/BM/ BM_CellFuse_Integration.R: Run CellFuse Fig2/BM/BM_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN) Fig2/BM/BM_scVI_scnorama.ipynb: Run scanorama and scVI Fig2/BM/BM_scIB.ipynb: Evaluate methods using scIB and save results Fig2/BM/BM_Data_visualisation.R: tSNE visualization Fig2/BM/Sequential_Feature_drop/Prepare_data.R: Prepare data for evaluating sequential feature drop Fig2/BM/Sequential_Feature_drop/ Run_FastMNN_Seurat_Harmony.R: Run CellFuse, Harmony, Seurat and FastMNN for sequential feature drop Fig2/BM/Sequential_Feature_drop/ BM_scVI_scnorama_feature_drop.ipynb: Run scVI and Scanorama for sequential feature drop Fig2/BM/Sequential_Feature_drop/ BM_scIB_feature_drop.ipynb: Evaluate feature dropping methods using scIB and save results Fig2/BM/Sequential_Feature_drop/ BM_scIB_Data_viz.R: visualize scIB results PBMC (Fig 2B,F,G, H, Supplementary Fig: 3 and 4) Fig2/PBMC/Reference/ Fig2_PBMC_prepare_data.R: Prepare PBMC data for CellFuse Fig2/ PBMC / PBMC_CellFuse_Integration.R: Run CellFuse Fig2/ PBMC /PBMC_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN) Fig2/PBMC/PBMC_scVI_scnorama_feature_drop.ipynb: Run scVI and Scanorama Fig2/PBMC/PBMC_scIB.ipynb: Evaluate methods using scIB and save results Fig2/PBMC/PBMC_Data_visualisation.R: tSNE visualization Fig2/ PBMC/ RunTime_benchmark/ Prepare_data.R: Prepare data 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 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 Fig3/ Good_et_al/Reference/ Fig3_CyTOF_prepare_data.R: Prepare CyTOF and CITE-Seq data for CellFuse Fig3/ Good_et_al/CellFuse_Integration_CyTOF.R: Run CellFuse to remove batch effect and integrate CyTOF data from day 7 post-infusion Fig3/ Good_et_al/CellFuse_Integration_CITESeq.R: Run CellFuse to integrate CyTOF and CITE-Seq data Fig3/ Good_et_al/CART_Data_visualisation.R: Visualize data Fig 3 Domizi et al. CART: Fig 3G and H and Supplementary Fig 6C 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) Fig4/CODEX_colorectal/Reference/ CODEX_HuBMAP_prepare_data.R: Prepare CODEX data from annotated and unannotated donor Fig4/ CODEX_colorectal/ CODEX_HuBMAP_CellFuse_Predict.R: Run CellFuse on cells from from annotated and unannotated donor Fig4/ CODEX_colorectal/CODEX_HuBMAP_Data_visualisation.R: Visualize data and prepare figures. Fig4/ CODEX_colorectal/ Benchmarking/Astir/Astrir.ipynb: Run Astir Fig4/ CODEX_colorectal/ Benchmarking/SpatialAnno.R: run SpatialAnno Fig4/ CODEX_colorectal/ CODEX_HuBMAP_Benchmark.R: Benchmarking CellFuse against CELESTA, SVM, SpatialAnno, Astir and Seurat using cells from annotated donors and prepare figures. 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 Suppl","journal":"Zenodo (CERN European Organization for Nuclear Research)","year":2025,"id":587421,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.802,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1076737,"name":"Abhishek Koladiya","orcid":"0000-0002-7348-4048","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:59:36.020030Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}