Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning

<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification.&nbsp;</p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p>&nbsp;</p>