Optimizing Transformations for Automated, High Throughput Analysis of Flow Cytometry Data

Background: In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of a...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Greg Finak, Juan-Manuel Perez, Andrew Weng, Raphael Gottardo
• Keywords: BMCBI 2010, Business, Cytometry Data, Data Transformations, Flow Cytometry