{"refrec":{"BRefID":334823,"RR":"<b>Rubbens, P.; Props, R.</b> (2021). Computational analysis of microbial flow cytometry data. <i>mSystems 6(1)</i>: e00895-20. <a href=\"https://dx.doi.org/10.1128/msystems.00895-20\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00895-20</a>","BEntID":331380,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>mSystems 6(1)</i>: e00895-20. <a href=\"https://dx.doi.org/10.1128/msystems.00895-20\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00895-20</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Rubbens, P.; Props, R.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Rubbens, P.; Props, R.","Englishabstract":"Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. 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