A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data
Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. T...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2018-02-01
|
Series: | Frontiers in Genetics |
Subjects: | |
Online Access: | http://journal.frontiersin.org/article/10.3389/fgene.2018.00024/full |
id |
doaj-7b20106b87874f379fbc3795099848ae |
---|---|
record_format |
Article |
spelling |
doaj-7b20106b87874f379fbc3795099848ae2020-11-24T21:37:05ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-02-01910.3389/fgene.2018.00024316891A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional DataYolanda Larriba0Cristina Rueda1Miguel A. Fernández2Shyamal D. Peddada3Shyamal D. Peddada4Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, SpainDepartamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, SpainDepartamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, SpainBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United StatesDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United StatesMotivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method.Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset.Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.htmlhttp://journal.frontiersin.org/article/10.3389/fgene.2018.00024/fullrhythmicityhigh-throughput technologiesnormalizationoscillatory systemscircadian genes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yolanda Larriba Cristina Rueda Miguel A. Fernández Shyamal D. Peddada Shyamal D. Peddada |
spellingShingle |
Yolanda Larriba Cristina Rueda Miguel A. Fernández Shyamal D. Peddada Shyamal D. Peddada A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data Frontiers in Genetics rhythmicity high-throughput technologies normalization oscillatory systems circadian genes |
author_facet |
Yolanda Larriba Cristina Rueda Miguel A. Fernández Shyamal D. Peddada Shyamal D. Peddada |
author_sort |
Yolanda Larriba |
title |
A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data |
title_short |
A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data |
title_full |
A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data |
title_fullStr |
A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data |
title_full_unstemmed |
A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data |
title_sort |
bootstrap based measure robust to the choice of normalization methods for detecting rhythmic features in high dimensional data |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2018-02-01 |
description |
Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method.Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset.Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html |
topic |
rhythmicity high-throughput technologies normalization oscillatory systems circadian genes |
url |
http://journal.frontiersin.org/article/10.3389/fgene.2018.00024/full |
work_keys_str_mv |
AT yolandalarriba abootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT cristinarueda abootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT miguelafernandez abootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT shyamaldpeddada abootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT shyamaldpeddada abootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT yolandalarriba bootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT cristinarueda bootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT miguelafernandez bootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT shyamaldpeddada bootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata AT shyamaldpeddada bootstrapbasedmeasurerobusttothechoiceofnormalizationmethodsfordetectingrhythmicfeaturesinhighdimensionaldata |
_version_ |
1725938354450595840 |