Published today in Cancer Informatics is a new original research article by Martin Lauss, Ilhami Visne, Albert Kriegner, Markus Ringnér, Göran Jönsson and Mattias Höglund.  Read more about this paper below:

Title

Monitoring of Technical Variation in Quantitative High-Throughput Datasets

Abstract

High-dimensional datasets can be confounded by variation from technical sources, such as batches. Undetected batch effects can have severe consequences for the validity of a study’s conclusion(s). We evaluate high-throughput RNAseq and miRNAseq as well as DNA methylation and gene expression microarray datasets, mainly from the Cancer Genome Atlas (TCGA) project, in respect to technical and biological annotations. We observe technical bias in these datasets and discuss corrective interventions. We then suggest a general procedure to control study design, detect technical bias using linear regression of principal components, correct for batch effects, and re-evaluate principal components. This procedure is implemented in the R package swamp, and as graphical user interface software. In conclusion, high-throughput platforms that generate continuous measurements are sensitive to various forms of technical bias. For such data, monitoring of technical variation is an important analysis step.

Click here to learn more about the article, download it and comment