 Welcome. My name is Victoria Quiroga and this presentation shows how FloDIV achieves reproducible cytometric diversity estimates. Bruno, Andre, Fernando and I are the people behind FloDIV. Some background. The Flocytometer acquires single cell multiple data called channels like scattered light and fluorescence and storage in FCS files. Cytometric data can be visualized through cytograms which are by plots of channels where dots represent cells and 3D color their abundance. We generate gates to remove noise and identify the standard and the cytometric population of interest and cell populations. Each sample has a characteristic cytogram that can be considered as a cytometric fingerprint but comparing cytograms is not straightforward because environmental samples are generally acquired with different instrumental settings and deletion factors. Regarding that approach, before FloDIV, we generated gates for the sub-populations and the standards in the FloDIV software and exported their channel median values and cell counts into Excel. Then data handling involved transforming the channel median values of each sub-population into relative values according to the standard and applying deletion factors for each sample. The output had reduced that dimension and was further used for multivariate analysis. With FloDIV, we only need to generate the gates for the population and the standard. Here, we delete the subjective sub-gating and FloDIV directly imports the gates into R containing single cell data with multiple channel values. FloDIV implements channel normalization and volume correction before calculating diversity indexes. This output has higher data dimensions and can be further used for multivariate analysis in R. Some FloDIV arguments use this to activate the channel normalization step. Cell's position in the cytograms depends on the instrumental settings. So FloDIV centers all cytograms based on their standard mean. This is an interactive code that suggests the best number of things for channel clustering. In this example, the channels were clustered into five bins and the contingency table shows the cell count for each cytometric category. The deletion argument activates the volume correction step. Here, the input is a vector containing the deletion factor for all samples. The channels unfit the goals. Each time FloDIV implements a change, we need to wait for dependency updates, mainly these three bioconductor packages, and then update FloDIV accordingly. We believe that the path forward involves developing a suitable gating approach in R and also a FloDIV tutorial with the LearnR package. For questions and feedback, please contact us and thank you.