Choosing the Best Method for Gene Expression log- log Linear Models Using Multiple CART Trees
AbstractMicroarray and RNA-Seq techniques are used to infer genes showing differential expressions on treatment conditions through the analysis of log-log linear models for the expression with treatment compared with control condition. Due to costs and technical limitations usually the experiments present small-sized samples and high contamination; therefore, choosing the estimation method for coefficients of such models becomes a challenge . Herein, we simulate microarray and RNA-Seq experiments and analyze a log-log linear model with contaminations at both conditions, varying key features: the sample size n, contamination type (light-tailed or heavy-tailed), contamination proportion p, and error variance. For each features configuration we computed the accuracy at each method among least absolute deviations, ordinary least squares, and Huber M-Estimators. Using this information, we built a machine learning that, based on classification CART trees , automatically decides the best method depending on simple questions. ...
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