J Biomed Inform - Statistical file matching of flow cytometry data.

Tópicos

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Resumo

Flow cytometry is a technology that rapidly measures antigen-based markers associated to cells in a cell population. Although analysis of flow cytometry data has traditionally considered one or two markers at a time, there has been increasing interest in multidimensional analysis. However, flow cytometers are limited in the number of markers they can jointly observe, which is typically a fraction of the number of markers of interest. For this reason, practitioners often perform multiple assays based on different, overlapping combinations of markers. In this paper, we address the challenge of imputing the high-dimensional jointly distributed values of marker attributes based on overlapping marginal observations. We show that simple nearest neighbor based imputation can lead to spurious subpopulations in the imputed data and introduce an alternative approach based on nearest neighbor imputation restricted to a cell's subpopulation. This requires us to perform clustering with missing data, which we address with a mixture model approach and novel EM algorithm. Since mixture model fitting may be ill-posed in this context, we also develop techniques to initialize the EM algorithm using domain knowledge. We demonstrate our approach on real flow cytometry data.

Resumo Limpo

flow cytometri technolog rapid measur antigenbas marker associ cell cell popul although analysi flow cytometri data tradit consid one two marker time increas interest multidimension analysi howev flow cytomet limit number marker can joint observ typic fraction number marker interest reason practition often perform multipl assay base differ overlap combin marker paper address challeng imput highdimension joint distribut valu marker attribut base overlap margin observ show simpl nearest neighbor base imput can lead spurious subpopul imput data introduc altern approach base nearest neighbor imput restrict cell subpopul requir us perform cluster miss data address mixtur model approach novel em algorithm sinc mixtur model fit may illpos context also develop techniqu initi em algorithm use domain knowledg demonstr approach real flow cytometri data

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