Biology subjects

Schimek, M. G.

Publications and source records attributed to Schimek, M. G..

3 recordsLinked to original sources

bioRxiv2020

A hierarchical clustering and data fusion approach for disease subtype discovery

Recent advances in multi-omics clustering methods enable a more fine-tuned separation of cancer patients into clinical relevant clusters. These advancements have the potential to provide a deeper understanding of cancer progression and may facilitate the treatment of cancer patients. Here, we present a simple hierarchical clustering and data fusion approach, named HC-fused, for the detection of disease subtypes. Unlike other methods, the proposed approach naturally reports on the individual contribution of each single-omic to the data fusion process. We perform multi-view simulations with disjoint and disjunct cluster elements across the views to highlight fundamentally different data integration behaviour of various state-of-the-art methods. HC-fused combines the strengths of some recently published methods and shows superior performance on real world cancer data from the TCGA (The Cancer Genome Atlas) database. An R implementation of our method is available on GitHub (pievos101/HC-fused).

bioinformatics↗

bioRxiv2019

Genome Scans for Selection and Introgression based on k-nearest Neighbor Techniques

In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Here, we introduce a series of versatile genome-scan methods that are based on non-parametric k-nearest neighbors (kNN) techniques, while incorporating pairwise Fixation Index (FST) estimates and pairwise nucleotide differences (dxy) as features. Simulations were performed for both positive directional selection and introgression, with varying parameters, such as recombination rates, population background histories, the proportion of introgression, and the time of gene flow. We find that kNN-based methods perform remarkably well while yielding stable results almost over the entire range of k. We provide a GitHub repository (pievos101/kNN-Genome-Scans) containing R source code to demonstrate how to apply the proposed methods to real-world genomic data using the population genomics R-package PopGenome.

evolutionary biology↗

bioRxiv2018

Inference of tumor cell-specific transcription factor binding from cell-free DNA

Alterations in transcription factors are important drivers of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here, we evaluated the feasibility of inferring transcription factor binding in solid tumors from their nucleosome footprint in circulating cell-free DNA. We developed a novel analysis pipeline to determine accessibility of transcription factor binding sites and applied it to 244 cell-free DNA samples from patients with prostate, breast and colon cancer. We observed patient-specific as well as tumor-specific patterns. Specifically, inferred binding patterns for the transcription factors AR, HOXB13, and NKX3-1 allowed us to classify patients by tumor type, including subtypes of prostate cancer, which has clinical implications for the management of patients. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable for clinical analysis.

cancer biology↗