Spot defects detection in cDNA microarray images,

M. G. LARESE; P. M. GRANITTO; J. C. GÓMEZ

PATTERN ANALYSIS AND APPLICATIONS

SPRINGER

Año: 2013 vol. 16 p. 307 - 319

1433-7541

Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain improved accuracies over previous studies in the literature. Next, we analyze the untackled problem of identifying specific spot defects. One spot may have several faults simultaneously (or none of them) yielding a multilabel classification problem. We propose several extra features in addition to those used for binary classification, and we use three different methods to perform the classification task: five independent binary classifiers, the recent Convex Multi-task Feature Learning (CMFL) algorithm and Convex Multi-task Independent Learning (CMIL). We analyze the Hamming loss and areas under the receiver operating characteristic curves (ROCs) to quantify the accuracies of the methods. We find that the three strategies achieve similar results leading to a successful identification of particular defects. Also, using a Random forests based analysis we show that the newly introduced features are highly relevant for this performance.