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.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

Multicomponent imaging of the

The gamma-ray sky as seen by the Large Area Telescope (LAT) on board the Fermi satellite is a superposition of emissions from many processes. To study them, a rich toolkit of analysis methods for gamma-ray observations has been developed, most of which rely on emission templates to model foreground emissions. Here, we aim to complement these methods by presenting a template-free spatio-spectral imaging approach for the gamma-ray sky, based on a phenomenological modeling of its emission components. It is formulated in a Bayesian variational inference framework and allows a simultaneous reconstruction and decomposition of the sky into multiple emission components, enabled by a self-consistent inference of their spatial and spectral correlation structures. Additionally, we formulated the extension of our imaging approach to template-informed imaging, which includes adding emission templates to our component models while retaining the “data-drivenness” of the reconstruction. We demonstrate the performance of the presented approach on the ten-year Fermi LAT data set. With both template-free and template-informed imaging, we achieve a high quality of fit and show a good agreement of our diffuse emission reconstructions with the current diffuse emission model published by the Fermi Collaboration. We quantitatively analyze the obtained data-driven reconstructions and critically evaluate the performance of our models, highlighting strengths, weaknesses, and potential improvements. All reconstructions have been released as data products

A simple procedure for the preparation of protected 2'-O-methyl or 2'-O-ethyl ribonucleoside-3'-O-phosphoramidites.

Protected 2'-O-methyl and 2'-O-ethyl ribonucleoside-3'-O-phosphoramidites were prepared via alkylation of the ribonucleosides at an early stage in the synthesis. Utilizing a strategy of minimal protection, the alkylation was performed with unprotected cytidine and adenosine, or with O6-protected guanosine and N3,5'-O-protected uridine using methyl or ethyl iodide and sodium hydride. In subsequent steps, the introduction of standard protective groups for oligonucleotide synthesis and the concomitant separation from 3'-O-alkylated isomers was accomplished. A modification of the phosphitylation procedure permitted facile isolation of the desired phosphoramidites which show high coupling efficiencies in oligomer assembly