Results of IMC-Denoise: a content aware pipeline to enhance Imaging Mass Cytometry

<ul> <li>Generated training sets: <ul> <li>training_set_supp_table6.zip</li> <li>training_set_supp_table7.zip</li> <li>training_set_supp_table8.zip</li> <li>training_set_supp_table9.zip</li> <li>training_set_supp_table10.zip</li> <li>training_set_supp_table11.zip</li> </ul> </li> <li>Trained weights of experimental data: <ul> <li>training_result_supp_table7.zip</li> <li>training_result_supp_table8.zip</li> <li>training_result_supp_table9.zip</li> <li>training_result_supp_table10.zip</li> <li>training_result_supp_table11.zip</li> </ul> </li> <li>Simulation results: <ul> <li>Simulation_results.zip</li> </ul> </li> <li>Experimental results: <ul> <li>Human_bone_marrow_IMC_denoising_results.zip</li> <li>Human_breast_cancer_IMC_denoising_results.zip</li> <li>Human_pancreatic_cancer_IMC_denoising_results.zip</li> <li>MIBI_denoising_results.zip</li> </ul> </li> <li>Ilastik-processed or manual-labeled results: <ul> <li>DIMR_Ilastik_results.zip</li> <li>background_removal_results.zip</li> <li>manual_annotated_public_datasets.zip</li> </ul> </li> <li>Extracted single cell data and the corresponding phenotyping results from both DIMR and DeepSNiF-based segmented cell masks: <ul> <li>Single_cell_analysis.zip</li> </ul> </li> </ul>