PT  - JOURNAL ARTICLE
AU  - Gregory J. Hunt
AU  - Mark A. Dane
AU  - James E. Korkola
AU  - Laura M. Heiser
AU  - Johann A. Gagnon-Bartsch
TI  - Transformation and Integration of Microenvironment Microarray Data Improves Discovery of Latent Effects
AID  - 10.1101/627802
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 627802
4099  - http://biorxiv.org/content/early/2019/05/05/627802.short
4100  - http://biorxiv.org/content/early/2019/05/05/627802.full
AB  - The immediate physical and bio-chemical surroundings of a cell, the cellular microenvironment, is an important component of many fundamental cell and tissue level processes and is implicated in many diseases and dysfunctions. Thus understanding the interaction of cells with their microenvironment can further both basic research and aid the discovery of therapeutic agents. To study perturbations of cellular microenvironments a novel image-based cell-profiling technology called the microenvironment microarray (MEMA) has been recently employed. In this paper we explore the effect of preprocessing transformations for MEMA data on the discovery of biological and technical latent effects. We find that Gaussianizing the data and carefully removing outliers can enhance discovery of important biological effects. In particular, these transformations help reveal a relationship between cell morphological features and the extra-cellular-matrix protein THBS1 in MCF10A breast tissue. More broadly, MEMAs are part of a recent and wide-spread adoption of image-based cell-profiling technologies in the quantification of phenotypic differences among cell populations (Caicedo et al., 2017). Thus we anticipate that the advantages of the proposed preprocessing transformations will likely also be realized in the analysis of data from other highly-multiplexed technologies like Cyclic Immunofluorescence. All code and supplementary analysis for this paper is available at gjhunt.github.io/rr.