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Cytometry inference through adaptive atomic deconvolution

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  • Costa, Manon
  • Gadat, Sébastien
  • Gonnord, Pauline
  • Risser, Laurent

Abstract

In this paper we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological data produced by flow cytometers. In these experiments, biologists measure the fluorescence emission of treated cells and compare them with their natural emission to study the presence of specific molecules on the cells' surface. They observe a signal which is composed of a noise (the natural fluorescence) plus some additional signal related to the quantity of molecule present on the surface if any. From a statistical point of view, we aim at inferring the percentage of cells expressing the selected molecule and the probability distribution function associated with its fluorescence emission. We propose here an adaptive estimation procedure based on a previous deconvolution procedure introduced by [vEGS08, GvES11]. For both estimating the mixing parameter and the mixing density automatically, we use the Lepskii method based on the optimal choice of a bandwidth using a bias-variance decomposition. We then derive some concentration inequalities for our estimators and obtain the convergence rates, that are shown to be minimax optimal (up to some log terms) in Sobolev classes. Finally, we apply our algorithm on simulated and real biological data.

Suggested Citation

  • Costa, Manon & Gadat, Sébastien & Gonnord, Pauline & Risser, Laurent, 2018. "Cytometry inference through adaptive atomic deconvolution," TSE Working Papers 18-905, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:32575
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    References listed on IDEAS

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    1. Delaigle, Aurore & Hall, Peter, 2006. "On optimal kernel choice for deconvolution," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1594-1602, September.
    2. Shota Gugushvili & Bert van Es & Peter Spreij, 2011. "Deconvolution for an atomic distribution: rates of convergence," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 1003-1029.
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    More about this item

    Keywords

    Mixture models; Atomic deconvolution; Adaptive kernel estimators; Inverse problems;
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