Data transformation plays an important role in mitigating some of these effects, both in manual and automated analysis setting. All of these characteristics can influence the output of both manual and automated gating, and subsequent downstream analysis. However, accurate automated gating of FCM data is complicated by asymmetric and overlapping cell populations, frequent outlier events, cell populations whose variance depend on their mean fluorescence intensity, and multiplicative errors in the fluorescence channels. An appropriate, auto-mated data pre-processing pipeline, including automated gating and matching of corresponding cell populations across replicated or similar samples is important for the accuracy of downstream analysis. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.įlow cytometry (FCM) is increasingly moving to-wards automated methods to deal with the quantities of data generated by high throughput, high-content screening. However, generally speaking, the choice of transformation remains data-dependent. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. Our results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts. We compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. ![]() Experience shows that the choice of transformation is data specific. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Consequently, the choice of data transformation can influence the output of automated gating. 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. 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. In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing.
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