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Activity Number:
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121
- In the Pipeline: Statistical Advances to Preserve Biological Signal in High-Throughput, Single-Cell Imaging and Sequencing Methods
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #317586
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Title:
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Robust Re-Scaling of Imaging Data to Improve Discovery of Latent Effects
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Author(s):
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Gregory Hunt* and Johann Gagnon-Bartsch
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Companies:
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College of William & Mary and University of Michigan
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Keywords:
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transformation;
data integration;
imaging
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Abstract:
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Recent advances in high-throughput imaging technologies has enabled the automatic quantification of hundreds of image features for each cell in a biological sample. A challenge when analyzing such data is choosing appropriate data transformations to enhance visualization and discovery of important (and potentially latent) effects. A problem presented by such highly-multiplexed data is that each image feature may have have a different distribution thus require a different transformation. Since determining optimal transformations for each of hundreds of features is infeasible to do manually, we present a method that automatically, and robustly, re-scales image features. Our primary application is to the study of perturbations of cellular microenvironments using novel image-based cell-profiling technology called the microenvironment microarray (MEMA). Here, we discuss the effect of our robust re-scaling 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.
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Authors who are presenting talks have a * after their name.
