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Quantifying and correcting slide-to-slide variation in multiplexed
Harris CR, McKinley ET, Roland JT, Liu Q, Shrubsole MJ, Lau KS, Coffey RJ,
Wrobel J, Vandekar SN
Submitted Externally on 6/21/2022
Bioinformatics (Oxford, England)
Volume : Pages
Multiplexed imaging is a nascent single-cell assay with a complex data structure
susceptible to technical variability that disrupts inference. These in situ
methods are valuable in understanding cell-cell interactions, but few
standardized processing steps or normalization techniques of multiplexed imaging
data are available., We implement and compare data transformations and
normalization algorithms in multiplexed imaging data. Our methods adapt the
ComBat and functional data registration methods to remove slide effects in this
domain, and we present an evaluation framework to compare the proposed
approaches. We present clear slide-to-slide variation in the raw, unadjusted
data, and show that many of the proposed normalization methods reduce this
variation while preserving and improving the biological signal. Further, we find
that dividing multiplexed imaging data by its slide mean, and the functional
data registration methods, perform the best under our proposed evaluation
framework. In summary, this approach provides a foundation for better data
quality and evaluation criteria in multiplexed imaging., Source code is provided
at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to
implement these methods is available here:
https://github.com/ColemanRHarris/mxnorm., Supplementary data are available at
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Financial support for this work was provided by the NIDDK Mouse Metabolic Phenotyping Centers (National MMPC, RRID:SCR_008997,
) under the MICROMouse Program, grants DK076169.
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