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

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". These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. The datasets are as follows: cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches ctrl_annotated.RDa : the annotated cohort of seven healthy donors data_umap.RDa : UMAP representation of the data used to generate the figures in our paper DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper patient_annotated.RDa : the annotated cohort of leukemia patients (n=43) We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH