NDLI: Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Torres-García, Wandaliz Brown, Steven D. Johnson, Roger H. Zhang, Weiwen Runger, George C. Meldrum, Deirdre R.
Description	Country affiliation: United States Author Affiliation: Torres-García W ( School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287-5906, USA. George.Runger@asu.edu)
Abstract	Despite significant improvements in recent years, proteomic datasets currently available still suffer from large number of missing values. Integrative analyses based upon incomplete proteomic and transcriptomic datasets could seriously bias the biological interpretation. In this study, we applied a non-linear data-driven stochastic gradient boosted trees (GBT) model to impute missing proteomic values using a temporal transcriptomic and proteomic dataset of Shewanella oneidensis. In this dataset, genes' expression was measured after the cells were exposed to 1 mM potassium chromate for 5, 30, 60, and 90 min, while protein abundance was measured for 45 and 90 min. With the ultimate objective to impute protein values for experimentally undetected samples at 45 and 90 min, we applied a serial set of algorithms to capture relationships between temporal gene and protein expression. This work follows four main steps: (1) a quality control step for gene expression reliability, (2) mRNA imputation, (3) protein prediction, and (4) validation. Initially, an S control chart approach is performed on gene expression replicates to remove unwanted variability. Then, we focused on the missing measurements of gene expression through a nonlinear Smoothing Splines Curve Fitting. This method identifies temporal relationships among transcriptomic data at different time points and enables imputation of mRNA abundance at 45 min. After mRNA imputation was validated by biological constrains (i.e. operons), we used a data-driven GBT model to impute protein abundance for the proteins experimentally undetected in the 45 and 90 min samples, based on relevant predictors such as temporal mRNA gene expression data and cellular functional roles. The imputed protein values were validated using biological constraints such as operon and pathway information through a permutation test to investigate whether dispersion measures are indeed smaller for known biological groups than for any set of random genes. Finally, we demonstrated that such missing value imputation improved characterization of the temporal response of S. oneidensis to chromate.
File Format	HTM / HTML
ISSN	1742206X
Issue Number	4
Volume Number	7
e-ISSN	17422051
Journal	Molecular BioSystems
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2011-04-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Gene Expression Profiling Proteomics Shewanella Genetics Metabolism Algorithms Chromates Pharmacology Computational Biology Environmental Pollutants Gene Expression Regulation, Bacterial Drug Effects Models, Statistical Potassium Compounds Quality Control Time Factors Journal Article Research Support, N.i.h., Extramural Research Support, U.s. Gov't, Non-p.h.s.
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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