CytoNorm: A Normalization Algorithm for Cytometry Data.

ABSTRACT: High-dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high-content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross-sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single-cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure

SUBMITTER: Van Gassen S 

PROVIDER: S-EPMC7078957 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature