The cell composition of the hematopoietic tissue is highly heterogeneous and the characterization of phenotype and functionality of all the immune cell types has high impact on the treatment of diseases and increasing of life expectancy. The instrument of choice for both research and clinical environments that focuses on immunology is flow cytometry. The latest technology analyses up to 30 fluorescence signals and in general researchers struggle to obtain reproducible and robust results. We present flowAI, a software for cleaning the flow cytometry data from unwanted events. flowAI performs its task in three steps by detecting and removing: 1) surges in the flow rate, 2) shifts in the signal acquisition, and 3) margin events in the dynamic range. Our testing revealed that flowAI could be particularly useful to discern anomalies from rare cells, reveal outlier samples, and detect technical anomalies from less reliable instruments. Moreover, we are optimizing flowAI to implement it in automatic pipelines of analysis and to favor the switch from laborious and subjective manual analysis to more effective and reproducible ones. Here, we validated flowAI on a small flow cytometry dataset by evaluating the improvement of the calculation agreement of the cell types’ proportions by flow cytometry and the deconvolution algorithm CIBERSORT. flowAI is available from Bioconductor (http://bioconductor.org/packages/flowAI/) and ImmPort Galaxy (https://immportgalaxy.org/).
University of Liverpool