D. Lutter

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: elmar.lang@biologie.uni-regensburg.de

How to extract marker genes from microarray data sets

In this study we focus on classification tasks and apply matrix factorization techniques like principal component analysis (PCA), independent component analysis (ICA) and non-negative matrix factorization ( NMF) to a microarray data set. The latter monitors the gene expression levels (GEL) of mononcytes and macrophages during and after differentiation. We show that these tools are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles (GEPs) without the need for extensive data bank search for appropriate functional annotations. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories.

Blind matrix decomposition techniques to identify marker genes from microarrays

Exploratory matrix factorization methods like PCA, ICA and sparseNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [6] or the famous leucemia data set [10] are considered.

Routes to identify marker genes for microarray classification

Support vector machines are applied to extract marker genes from various microarray data sets: breast cancer, leukemia and monocyte - macrophage differentiation to ease classification of related pathologies or characterize related gene regulation pathways.

Comparison of unsupervised and supervised gene selection methods

Modern machine learning methods based on matrix decomposition techniques like Independent Component Analysis (ICA) provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield informative expression modes (ICA) which are considered indicative of underlying regulatory processes. Their most strongly expressed genes represent marker genes for classification of the tissue samples under investigation. Comparison with supervised gene selection methods based on statistical scores or support vector machines corroborate these findings. The method is applied to macrophages loaded/de-loaded with chemically modified low density lipids.

A Matrix Factorization Classifier for Knowledge-Based Microarray Analysis

In this study we analyze microarray data sets which monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that matrix decomposition techniques are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories. The latter correspond to either monocytes vs macrophages or healthy vs Niemann Pick C diseased patients. Our results demonstrate that these methods are able to identify suitable marker genes which can be used to classify the type of cell lines investigated.

Exploring Matrix Factorization Techniques for Classification of Gene Expression Profiles

In this study we focus on diagnostic classification tasks and the extraction of related marker genes from gene expression profiles. We apply ICA and sparse NMF to various microarray data sets. The latter monitor the gene expression levels of either human breast cancer (HBC) cell lines [1] or the famous leucemia data set [2] under various environmental conditions. We show that these matrix decomposition techniques are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles. With these marker genes corresponding test data sets can be classified into related diagnostic categories.