Figure 2

Evolution of principal component analysis during the workflow of the standardization procedure of flow cytometry data in multicentric prospective studies. Peripheral blood of 2,559 individuals was labeled with the dry panel 1 and panel 2 antibody formulations and then analyzed by flow cytometry using 11 different, previously harmonized instruments. The data of each instrument was then standardized by the R script. The frequencies of the leukocyte populations (panel 1) and mononuclear cells (panel 2) (a) and the mean fluorescence intensities of the cell surface markers (b) were collected from all flow cytometry files by automaton having learned the gating strategy through machine learning and were analyzed by principal component analysis (PCA). The mean fluorescence intensities of the cell surface markers were corrected by a Python script to adjust the median values and eliminate antibody batches variations in each instrument, before being analyzed by PCA (c). The mean fluorescence intensities were finally corrected by an additional Python script to eliminate the variations of the median values between the instruments, before being analyzed by PCA (d).