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A Nonnegative Robust Linear Model for Deconvolution of Proportions

  • Hyonho ChunEmail author
  • Hyuna Yang
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Estimating mixing rates of a sample mixture is a popular problem in biomedical studies. Recently, it is applied to find immune cell infiltration in tumor samples. The main methodological challenge is tackling the non-Gaussian nature of gene expression data. Although a probabilistic model via Multinomial or Poisson distributions would be a solution, such a model often becomes un-identifiable. An alternative is using robust regression because non-Gaussianity is often manifested as too high or too small expression values. In this article, we propose a non-negative robust linear model (NRLM) approach that yields robust yet interpretable mixing rate estimates. In our simulation study, NRLM shows a robust performance for finding the relative abundance of specified components when a large amount of noise is present. More importantly, our approach accurately estimates the absolute level of the specified components in the presence of un-specified ones. Finally, it shows a superior performance when applied to deep deconvolution of blood samples.

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsBoston UniversityBostonUSA
  2. 2.IBM Watson HealthCambridgeUSA

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