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  "Description": "Mass cytometry enables the simultaneous measurement of\ndozens of protein markers at the single-cell level, producing\nhigh dimensional datasets that provide deep insights into\ncellular heterogeneity and function. However, these datasets\noften contain unwanted covariance introduced by technical\nvariations, such as differences in cell size, staining\nefficiency, and instrument-specific artifacts, which can\nobscure biological signals and complicate downstream analysis.\nThis package addresses this challenge by implementing a robust\nframework of linear models designed to identify and remove\nthese sources of unwanted covariance. By systematically\nmodeling and correcting for technical noise, the package\nenhances the quality and interpretability of mass cytometry\ndata, enabling researchers to focus on biologically relevant\nsignals.",
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