Improving stability of prediction models based on correlated omics data by using network approaches.
Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the prese...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2018-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC5819809?pdf=render |
id |
doaj-42f50a69fe984d24b08304eab5fec140 |
---|---|
record_format |
Article |
spelling |
doaj-42f50a69fe984d24b08304eab5fec1402020-11-25T02:08:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01132e019285310.1371/journal.pone.0192853Improving stability of prediction models based on correlated omics data by using network approaches.Renaud TissierJeanine Houwing-DuistermaatMar Rodríguez-GirondoBuilding prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.http://europepmc.org/articles/PMC5819809?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Renaud Tissier Jeanine Houwing-Duistermaat Mar Rodríguez-Girondo |
spellingShingle |
Renaud Tissier Jeanine Houwing-Duistermaat Mar Rodríguez-Girondo Improving stability of prediction models based on correlated omics data by using network approaches. PLoS ONE |
author_facet |
Renaud Tissier Jeanine Houwing-Duistermaat Mar Rodríguez-Girondo |
author_sort |
Renaud Tissier |
title |
Improving stability of prediction models based on correlated omics data by using network approaches. |
title_short |
Improving stability of prediction models based on correlated omics data by using network approaches. |
title_full |
Improving stability of prediction models based on correlated omics data by using network approaches. |
title_fullStr |
Improving stability of prediction models based on correlated omics data by using network approaches. |
title_full_unstemmed |
Improving stability of prediction models based on correlated omics data by using network approaches. |
title_sort |
improving stability of prediction models based on correlated omics data by using network approaches. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2018-01-01 |
description |
Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset. |
url |
http://europepmc.org/articles/PMC5819809?pdf=render |
work_keys_str_mv |
AT renaudtissier improvingstabilityofpredictionmodelsbasedoncorrelatedomicsdatabyusingnetworkapproaches AT jeaninehouwingduistermaat improvingstabilityofpredictionmodelsbasedoncorrelatedomicsdatabyusingnetworkapproaches AT marrodriguezgirondo improvingstabilityofpredictionmodelsbasedoncorrelatedomicsdatabyusingnetworkapproaches |
_version_ |
1724925204389429248 |