Monitoring of technical variation in quantitative high-throughput datasets.
Author
Summary, in English
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.
Department/s
- Breastcancer-genetics
- BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
Publishing year
2013
Language
English
Pages
193-201
Publication/Series
Cancer Informatics
Volume
12
Issue
Sep 23
Full text
Links
Document type
Journal article
Publisher
Libertas Academica
Topic
- Cancer and Oncology
Status
Published
ISBN/ISSN/Other
- ISSN: 1176-9351