TY - JOUR T1 - Automated Contamination Detection in Single-Cell Sequencing JF - bioRxiv DO - 10.1101/020859 SP - 020859 AU - Markus Lux AU - Barbara Hammer AU - Alexander Sczyrba Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/06/15/020859.abstract N2 - Novel methods for the sequencing of single-cell DNA offer tremendous opportunities. However, many techniques are still in their infancy and a major obstacle is given by sample contamination with foreign DNA. In this contribution, we present a pipeline that allows for fast, automated detection of contaminated samples by the use of modern machine learning methods. First, a vectorial representation of the genomic data is obtained using oligonucleotide signatures. Using non-linear subspace projections, data is transformed to be suitable for automatic clustering. This allows for the detection of one vs. more genomes (clusters) in a sample. As clustering is an ill-posed problem, the pipeline relies on a thorough choice of all involved methods and parameters. We give an overview of the problem and evaluate techniques suitable for this task. ER -