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A unified censored normal regression model for qPCR differential gene expression analysis

Peter Pipelers, Lieven Clement UGent, Matthijs Vynck UGent, JAN HELLEMANS, Jo Vandesompele UGent and Olivier Thas UGent (2017) PLOS ONE. 12(8).
abstract
Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as undetermined . Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
FALSE DISCOVERY RATE
journal title
PLOS ONE
PLoS One
volume
12
issue
8
article number
e0182832
pages
16 pages
Web of Science type
Article
Web of Science id
000407856600047
ISSN
1932-6203
DOI
10.1371/journal.pone.0182832
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
8529053
handle
http://hdl.handle.net/1854/LU-8529053
date created
2017-08-18 16:06:28
date last changed
2017-10-05 13:28:58
@article{8529053,
  abstract     = {Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as undetermined . Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision.},
  articleno    = {e0182832},
  author       = {Pipelers, Peter and Clement, Lieven and Vynck, Matthijs and HELLEMANS, JAN and Vandesompele, Jo and Thas, Olivier},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {FALSE DISCOVERY RATE},
  language     = {eng},
  number       = {8},
  pages        = {16},
  title        = {A unified censored normal regression model for qPCR differential gene expression analysis},
  url          = {http://dx.doi.org/10.1371/journal.pone.0182832},
  volume       = {12},
  year         = {2017},
}

Chicago
Pipelers, Peter, Lieven Clement, Matthijs Vynck, JAN HELLEMANS, Jo Vandesompele, and Olivier Thas. 2017. “A Unified Censored Normal Regression Model for qPCR Differential Gene Expression Analysis.” Plos One 12 (8).
APA
Pipelers, P., Clement, L., Vynck, M., HELLEMANS, J., Vandesompele, J., & Thas, O. (2017). A unified censored normal regression model for qPCR differential gene expression analysis. PLOS ONE, 12(8).
Vancouver
1.
Pipelers P, Clement L, Vynck M, HELLEMANS J, Vandesompele J, Thas O. A unified censored normal regression model for qPCR differential gene expression analysis. PLOS ONE. 2017;12(8).
MLA
Pipelers, Peter, Lieven Clement, Matthijs Vynck, et al. “A Unified Censored Normal Regression Model for qPCR Differential Gene Expression Analysis.” PLOS ONE 12.8 (2017): n. pag. Print.