BMC Bioinformatics (Nov 2019)

An EM algorithm to improve the estimation of the probability of clonal relatedness of pairs of tumors in cancer patients

  • Audrey Mauguen,
  • Venkatraman E. Seshan,
  • Irina Ostrovnaya,
  • Colin B. Begg

DOI
https://doi.org/10.1186/s12859-019-3148-z
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 8

Abstract

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Abstract Background We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metastasis of the other, i.e. where the tumors are clonally related. Matches of mutations within a tumor pair provide the evidence for clonal relatedness. In this article, using simulations, we compare two estimation approaches that we considered for our model: use of a constrained quasi-Newton algorithm to maximize the likelihood conditional on the random effect, and an Expectation-Maximization algorithm where we further condition the random-effect distribution on the data. Results In some specific settings, especially with sparse information, the estimation of the parameter of interest is at the boundary a non-negligible number of times using the first approach, while the EM algorithm gives more satisfactory estimates. This is of considerable importance for our application, since an estimate of either 0 or 1 for the proportion of cases that are clonal leads to individual probabilities being 0 or 1 in settings where the evidence is clearly not sufficient for such definitive probability estimates. Conclusions The EM algorithm is a preferable approach for our clonality random-effect model. It is now the method implemented in our R package Clonality, making available an easy and fast way to estimate this model on a range of applications.

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