Maximizing the Reusability of Public Gene Expression Data by Predicting Missing Metadata

AbstractReusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we develop a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We propose a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we show that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.

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Maximizing the reusability of gene expression data by predicting missing metadata

PLoS Computational Biology ◽

10.1371/journal.pcbi.1007450 ◽

2020 ◽

Vol 16 (11) ◽

pp. e1007450

Author(s):

Pei-Yau Lung ◽

Dongrui Zhong ◽

Xiaodong Pang ◽

Yan Li ◽

Jinfeng Zhang

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Missing Values ◽

Expression Data ◽

New Approach ◽

Machine Learning Methods ◽

Differential Gene ◽

Missing Variables ◽

Better Than

Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we developed a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We found that when using predicted data to conduct other analyses, it is not optimal to use all the predicted data. Instead, one should only use the subset of data, which can be predicted accurately. We proposed a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we showed that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.

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3145 An Evaluation of Machine Learning and Traditional Statistical Methods for Discovery in Large-Scale Translational Data

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.8 ◽

2019 ◽

Vol 3 (s1) ◽

pp. 2-2

Author(s):

Megan C Hollister ◽

Jeffrey D. Blume

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Random Forest ◽

Gene Expression Data ◽

Large Scale ◽

Second Generation ◽

A Priori ◽

Expression Data ◽

P Values ◽

Machine Learning Methods

OBJECTIVES/SPECIFIC AIMS: To examine and compare the claims in Bzdok, Altman, and Brzywinski under a broader set of conditions by using unbiased methods of comparison. To explore how to accurately use various machine learning and traditional statistical methods in large-scale translational research by estimating their accuracy statistics. Then we will identify the methods with the best performance characteristics. METHODS/STUDY POPULATION: We conducted a simulation study with a microarray of gene expression data. We maintained the original structure proposed by Bzdok, Altman, and Brzywinski. The structure for gene expression data includes a total of 40 genes from 20 people, in which 10 people are phenotype positive and 10 are phenotype negative. In order to find a statistical difference 25% of the genes were set to be dysregulated across phenotype. This dysregulation forced the positive and negative phenotypes to have different mean population expressions. Additional variance was included to simulate genetic variation across the population. We also allowed for within person correlation across genes, which was not done in the original simulations. The following methods were used to determine the number of dysregulated genes in simulated data set: unadjusted p-values, Benjamini-Hochberg adjusted p-values, Bonferroni adjusted p-values, random forest importance levels, neural net prediction weights, and second-generation p-values. RESULTS/ANTICIPATED RESULTS: Results vary depending on whether a pre-specified significance level is used or the top 10 ranked values are taken. When all methods are given the same prior information of 10 dysregulated genes, the Benjamini-Hochberg adjusted p-values and the second-generation p-values generally outperform all other methods. We were not able to reproduce or validate the finding that random forest importance levels via a machine learning algorithm outperform classical methods. Almost uniformly, the machine learning methods did not yield improved accuracy statistics and they depend heavily on the a priori chosen number of dysregulated genes. DISCUSSION/SIGNIFICANCE OF IMPACT: In this context, machine learning methods do not outperform standard methods. Because of this and their additional complexity, machine learning approaches would not be preferable. Of all the approaches the second-generation p-value appears to offer significant benefit for the cost of a priori defining a region of trivially null effect sizes. The choice of an analysis method for large-scale translational data is critical to the success of any statistical investigation, and our simulations clearly highlight the various tradeoffs among the available methods.

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A comparative study of different machine learning methods on microarray gene expression data

BMC Genomics ◽

10.1186/1471-2164-9-s1-s13 ◽

2008 ◽

Vol 9 (Suppl 1) ◽

pp. S13 ◽

Cited By ~ 111

Author(s):

Mehdi Pirooznia ◽

Jack Y Yang ◽

Mary Qu Yang ◽

Youping Deng

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Comparative Study ◽

Gene Expression Data ◽

Microarray Gene Expression Data ◽

Expression Data ◽

Learning Methods ◽

Microarray Gene Expression ◽

Machine Learning Methods ◽

Microarray Gene

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Bayesian Machine-Learning Methods for Tumor Classification Using Gene Expression Data

Introduction to Machine Learning and Bioinformatics ◽

10.1201/b17186-13 ◽

2008 ◽

pp. 321-344

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Tumor Classification ◽

Expression Data ◽

Learning Methods ◽

Machine Learning Methods ◽

Bayesian Machine Learning

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Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

BMC Bioinformatics ◽

10.1186/s12859-020-03755-4 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Elisabetta Manduchi ◽

Weixuan Fu ◽

Joseph D. Romano ◽

Stefano Ruberto ◽

Jason H. Moore

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Big Data ◽

Gene Expression Data ◽

Data Sets ◽

Biomedical Data ◽

Training Procedure ◽

Expression Data ◽

Differential Gene ◽

Automated Machine Learning

Abstract Background A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. Results We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. Conclusions In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

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Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

10.1101/2020.08.24.265116 ◽

2020 ◽

Author(s):

Elisabetta Manduchi ◽

Weixuan Fu ◽

Joseph D. Romano ◽

Stefano Ruberto ◽

Jason H. Moore

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Big Data ◽

Gene Expression Data ◽

Data Sets ◽

Biomedical Data ◽

Training Procedure ◽

Expression Data ◽

Differential Gene ◽

Automated Machine Learning

AbstractBackgroundA typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis.ResultsWe present an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We then describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj.ConclusionsIn this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

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Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1153 ◽

2007 ◽

Vol 6 (1) ◽

Cited By ~ 4

Author(s):

Qiang Zhao ◽

Jianguo Sun

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Principal Component Regression ◽

Predictive Ability ◽

Principal Component ◽

Microarray Gene Expression Data ◽

Expression Data ◽

Microarray Gene Expression ◽

New Approach ◽

Microarray Gene

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.

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