Biostatistics Weekly Seminar

Combining batches of high-dimensional multiplexed images

Coleman Harris
Vanderbilt University Medical Center

High-dimensional multiplexed imaging methods can help quantify the heterogeneity of cell populations in healthy and tumorous tissues, offering insight into tumor progression and improved treatment strategies. However, implicit biases exist in the imaging pipeline - images are distorted by optical effects, slide and batch effects, and instrument variability. Normalization of this data is compounded by the number of markers and natural tissue variability within each image, introducing systematic differences that impact inference. In this work, we introduce an image normalization pipeline to reduce systematic variability in multiplexed images by correcting for batch effects. We build on existing methods to compare the following approaches to correct for the batch effects in the data: a logarithmic transformation, a simple standardization (division by the mean), and two approaches using functional data registration. We demonstrate these methods by analyzing multiplexed immunofluorescence (MxIF) images of human colorectal tissue samples to quantify the reduction in variability of the data, namely using thresholding methods and random effects models. We further demonstrate the method's ability to retain biological signal by evaluating prediction accuracy of models for specific regions containing tumors in the tissue samples.

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14 April 2021

Topic revision: r1 - 07 Apr 2021, AndrewSpieker

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