An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data

Abstract

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.

Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data

“…Outlier observations not only strongly impact parameter estimation but also variable selection (Alfons et al, 2013). Therefore, methods that are robust to observations that deviate from the remaining observations in the same group have been proposed, in particular, sparse modifications of the popular Least Trimmed Squares (LTS) robust estimator (Rousseeuw, 2013;Rousseeuw & Driessen, 2006) have been successful applied to high-dimensional data, namely gene expression cancer data (Alfons et al, 2013;Jensch et al, 2022;Segaert et al, 2019;Sun et al, 2021).…”