PT  - JOURNAL ARTICLE
AU  - Markus Lux
AU  - Barbara Hammer
AU  - Alexander Sczyrba
TI  - Automated Contamination Detection in Single-Cell Sequencing
AID  - 10.1101/020859
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 020859
4099  - http://biorxiv.org/content/early/2015/06/15/020859.short
4100  - http://biorxiv.org/content/early/2015/06/15/020859.full
AB  - 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.