Results of IMC-Denoise: a content aware pipeline to enhance Imaging Mass Cytometry
Peng Lu, Karolyn A. Oetjen, Diane E. Bender, Marianna B. Ruzinova, Daniel A.C. Fisher, Shim, Kevin, Russell Kent Pachynski, William Nathaniel Brennen, Stephen T. Oh, Daniel C. Link, Daniel L.J. Thorek · Zenodo (CERN European Organization for Nuclear Research) · 2022
Generated training sets: training_set_supp_table6.zip training_set_supp_table7.zip training_set_supp_table8.zip training_set_supp_table9.zip training_set_supp_table10.zip training_set_supp_table11.zip Trained weights of experimental data: training_result_supp_table7.zip training_result_supp_table8.zip training_result_supp_table9.zip training_result_supp_table10.zip training_result_supp_table11.zip Simulation results: Simulation_results.zip Experimental results: Human_bone_marrow_IMC_denoising_results.zip Human_breast_cancer_IMC_denoising_results.zip Human_pancreatic_cancer_IMC_denoising_results.zip MIBI_denoising_results.zip Ilastik-processed or manual-labeled results: DIMR_Ilastik_results.zip background_removal_results.zip manual_annotated_public_datasets.zip Extracted single cell data and the corresponding phenotyping results from both DIMR and DeepSNiF-based segmented cell masks: Single_cell_analysis.zip