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Monitoring of technical variation in quantitative high-throughput datasets.

In English

Författare

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.

Avdelning/ar

  • Bröstcancer-genetik
  • BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation

Publiceringsår

2013

Språk

Engelska

Sidor

193-201

Publikation/Tidskrift/Serie

Cancer Informatics

Volym

12

Issue

Sep 23

Dokumenttyp

Artikel i tidskrift

Förlag

Libertas Academica

Ämne

  • Cancer and Oncology

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1176-9351