Monitoring of technical variation in quantitative high-throughput datasets.
Lauss, Martin LU ; Visne, Ilhami ; Kriegner, Albert ; Ringnér, Markus LU
; Jönsson, Göran B
LU
and Höglund, Mattias
LU
(2013)
In Cancer Informatics
12(Sep 23).
p.193-201
- 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,... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4143705
- author
- Lauss, Martin
LU
; Visne, Ilhami
; Kriegner, Albert
; Ringnér, Markus
LU
; Jönsson, Göran B
LU
and Höglund, Mattias
LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Cancer Informatics
- volume
- 12
- issue
- Sep 23
- pages
- 193 - 201
- publisher
- Libertas Academica
- external identifiers
-
- pmid:24092958
- scopus:84884558982
- pmid:24092958
- ISSN
- 1176-9351
- DOI
- 10.4137/CIN.S12862
- language
- English
- LU publication?
- yes
- id
- 2d64ff30-ca1b-456d-957e-02968028efd8 (old id 4143705)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/24092958?dopt=Abstract
- date added to LUP
- 2016-04-01 14:21:16
- date last changed
- 2022-04-06 18:12:15
@article{2d64ff30-ca1b-456d-957e-02968028efd8,
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.}},
author = {{Lauss, Martin and Visne, Ilhami and Kriegner, Albert and Ringnér, Markus and Jönsson, Göran B and Höglund, Mattias}},
issn = {{1176-9351}},
language = {{eng}},
number = {{Sep 23}},
pages = {{193--201}},
publisher = {{Libertas Academica}},
series = {{Cancer Informatics}},
title = {{Monitoring of technical variation in quantitative high-throughput datasets.}},
url = {{https://lup.lub.lu.se/search/files/3930611/4253834}},
doi = {{10.4137/CIN.S12862}},
volume = {{12}},
year = {{2013}},
}