Proper data transformation is an essential part of analysis. Choosing appropriate transformations for variables can enhance visualization, improve efficacy of analytical methods, and increase data interpretability. However determining appropriate transformations of variables from high-content imaging data poses new challenges. Imaging data produces hundreds of covariates from each of thousands of images in a corpus. Each of these covariates will have a different distribution and need a potentially different transformation. As such imaging data produces hundreds of covariates, determining an appropriate transformation for each of them is infeasible by hand. In this paper we explore simple, robust, and automatic transformations of high-content image data. A central application of our work is to microenvironment microarray bio-imaging data from the NIH LINCS program. We show that our robust transformations enhance visualization and improve the discovery of substantively relevant latent effects. These transformations enhance analysis of image features individually and also improve data integration approaches when combining together multiple features. We anticipate that the advantages of this work will likely also be realized in the analysis of data from other high-content and highly-multiplexed technologies like Cell Painting or Cyclic Immunofluorescence. Software and further analysis can be found at gjhunt.github.io/rr.
Keywords: PCA; automatic transformation; data integration; imaging; latent variables; visualization.