Jaesik Jeong

An optimal peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry using mixture similarity measure

MOTIVATION Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) brings much increased separation capacity, chemical selectivity and sensitivity for metabolomics and provides more accurate information about metabolite retention times and mass spectra. However, there is always a shift of retention times in the two columns that makes it difficult to compare metabolic profiles obtained from multiple samples exposed to different experimental conditions. RESULTS The existing peak alignment algorithms for GC × GC-MS data use the peak distance and the spectra similarity sequentially and require predefined either distance-based window and/or spectral similarity-based window. To overcome the limitations of the current alignment methods, we developed an optimal peak alignment using a novel mixture similarity by employing the peak distance and the spectral similarity measures simultaneously without any variation windows. In addition, we examined the effect of the four different distance measures such as Euclidean, Maximum, Manhattan and Canberra distances on the peak alignment. The performance of our proposed peak alignment algorithm was compared with the existing alignment methods on the two sets of GC × GC-MS data. Our analysis showed that Canberra distance performed better than other distances and the proposed mixture similarity peak alignment algorithm prevailed against all literature reported methods. AVAILABILITY The data and software mSPA are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development.

Compound identification using partial and semipartial correlations for gas chromatography-mass spectrometry data.

Compound identification is a key component of data analysis in the applications of gas chromatography-mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and m/z value, respectively.

A Novel Wavelet-based Thresholding Method for the Pre-processing of Mass Spectrometry Data that Accounts for Heterogeneous Noise

In recent years there has been an increased interest in using protein mass spectroscopy to discriminate diseased from healthy individuals with the aim of discovering molecular markers for disease. A crucial step before any statistical analysis is the pre‐processing of the mass spectrometry data. Statistical results are typically strongly affected by the specific pre‐processing techniques used. One important pre‐processing step is the removal of chemical and instrumental noise from the mass spectra. Wavelet denoising techniques are a standard method for denoising. Existing techniques, however, do not accommodate errors that vary across the mass spectrum, but instead assume a homogeneous error structure. In this paper we propose a novel wavelet denoising approach that deals with heterogeneous errors by incorporating a variance change point detection method in the thresholding procedure. We study our method on real and simulated mass specrometry data and show that it improves on performances of peak detection methods.

Selective Inhibition of Pancreatic Ductal Adenocarcinoma Cell Growth by the Mitotic MPS1 Kinase Inhibitor NMS-P715

Most solid tumors, including pancreatic ductal adenocarcinoma (PDAC), exhibit structural and numerical chromosome instability (CIN). Although often implicated as a driver of tumor progression and drug resistance, CIN also reduces cell fitness and poses a vulnerability that can be exploited therapeutically. The spindle assembly checkpoint (SAC) ensures correct chromosome-microtubule attachment, thereby minimizing chromosome segregation errors. Many tumors exhibit upregulation of SAC components such as MPS1, which may help contain CIN within survivable limits. Prior studies showed that MPS1 inhibition with the small molecule NMS-P715 limits tumor growth in xenograft models. In cancer cell lines, NMS-P715 causes cell death associated with impaired SAC function and increased chromosome missegregation. Although normal cells appeared more resistant, effects on stem cells, which are the dose-limiting toxicity of most chemotherapeutics, were not examined. Elevated expression of 70 genes (CIN70), including MPS1, provides a surrogate measure of CIN and predicts poor patient survival in multiple tumor types. Our new findings show that the degree of CIN70 upregulation varies considerably among PDAC tumors, with higher CIN70 gene expression predictive of poor outcome. We identified a 25 gene subset (PDAC CIN25) whose overexpression was most strongly correlated with poor survival and included MPS1. In vitro, growth of human and murine PDAC cells is inhibited by NMS-P715 treatment, whereas adipose-derived human mesenchymal stem cells are relatively resistant and maintain chromosome stability upon exposure to NMS-P715. These studies suggest that NMS-P715 could have a favorable therapeutic index and warrant further investigation of MPS1 inhibition as a new PDAC treatment strategy. Mol Cancer Ther; 13(2); 307–15. ©2013 AACR.

Model-based peak alignment of metabolomic profiling from comprehensive two-dimensional gas chromatography mass spectrometry

BackgroundComprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS) has been used for metabolite profiling in metabolomics. However, there is still much experimental variation to be controlled including both within-experiment and between-experiment variation. For efficient analysis, an ideal peak alignment method to deal with such variations is in great need.ResultsUsing experimental data of a mixture of metabolite standards, we demonstrated that our method has better performance than other existing method which is not model-based. We then applied our method to the data generated from the plasma of a rat, which also demonstrates good performance of our model.ConclusionsWe developed a model-based peak alignment method to process both homogeneous and heterogeneous experimental data. The unique feature of our method is the only model-based peak alignment method coupled with metabolite identification in an unified framework. Through the comparison with other existing method, we demonstrated that our method has better performance. Data are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development/mspa. The R source codes are available at http://www.biostat.iupui.edu/~ChangyuShen/CodesPeakAlignment.zip.Trial Registration2136949528613691

An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer

BackgroundThe nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a period of time. Because of the association of gene transcription alteration and drug resistance and the emerging evidence on the role of DNA methylation on transcription regulation, understanding of these relationships can facilitate development of approaches to re-sensitize breast cancer cells to treatment by restoring DNA methylation patterns.MethodsWe constructed a hierarchical empirical Bayes model to investigate the simultaneous change of gene expression and promoter DNA methylation profiles among wild type (WT) and OHT/ICI resistant MCF7 breast cancer cell lines.ResultsWe found that compared with the WT cell lines, almost all of the genes in OHT or ICI resistant cell lines either do not show methylation change or hypomethylated. Moreover, the correlations between gene expression and methylation are quite heterogeneous across genes, suggesting the involvement of other factors in regulating transcription. Analysis of our results in combination with H3K4me2 data on OHT resistant cell lines suggests a clear interplay between DNA methylation and H3K4me2 in the regulation of gene expression. For hypomethylated genes with alteration of gene expression, most (~80%) are up-regulated, consistent with current view on the relationship between promoter methylation and gene expression.ConclusionsWe developed an empirical Bayes model to study the association between DNA methylation in the promoter region and gene expression. Our approach generates both global (across all genes) and local (individual gene) views of the interplay. It provides important insight on future effort to develop therapeutic agent to re-sensitize breast cancer cells to treatment.

An empirical Bayes model using a competition score for metabolite identification in gas chromatography mass spectrometry

BackgroundMass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolites similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum.ResultsWith a mixture of metabolite standards, we demonstrated that our method has better identification accuracy than other four existing methods. Moreover, our method has reliable false discovery rate estimate. We also applied our method to the data collected from the plasma of a rat and identified some metabolites from the plasma under the control of false discovery rate.ConclusionsWe developed an empirical Bayes model for metabolite identification and validated the method through a mixture of metabolite standards and rat plasma. The results show that our hierarchical model improves identification accuracy as compared with methods that do not structurally model the involved variables. The improvement in identification accuracy is likely to facilitate downstream analysis such as peak alignment and biomarker identification. Raw data and result matrices can be found at http://www.biostat.iupui.edu/~ChangyuShen/index.htmTrial Registration2123938128573429

A 1H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various Oryza sativa L. Cultivars

Rice grain metabolites are important for better understanding of the plant physiology of various rice cultivars and thus for developing rice cultivars aimed at providing diverse processed products. However, the variation of global metabolites in rice grains has rarely been explored. Here, we report the identification of intra- or intercellular metabolites in rice (Oryza sativa L.) grain powder using a (1)H high-resolution magic angle spinning (HR-MAS) NMR-based metabolomic approach. Compared with nonwaxy rice cultivars, marked accumulation of lipid metabolites such as fatty acids, phospholipids, and glycerophosphocholine in the grains of waxy rice cultivars demonstrated the distinct metabolic regulation and adaptation of each cultivar for effective growth during future germination, which may be reflected by high levels of glutamate, aspartate, asparagine, alanine, and sucrose. Therefore, this study provides important insights into the metabolic variations of diverse rice cultivars and their associations with environmental conditions and genetic backgrounds, with the aim of facilitating efficient development and the improvement of rice grain quality through inbreeding with genetic or chemical modification and mutation.

A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data

This article considers linear regression models with long memory errors. These models have been proven useful for application in many areas, such as medical imaging, signal processing, and econometrics. Wavelets, being self-similar, have a strong connection to long memory data. Here we employ discrete wavelet transforms as whitening filters to simplify the dense variance-covariance matrix of the data. We then adopt a Bayesian approach for the estimation of the model parameters. Our inferential procedure uses exact wavelet coefficients variances and leads to accurate estimates of the model parameters. We explore performances on simulated data and present an application to an fMRI data set. In the application we produce posterior probability maps (PPMs) that aid interpretation by identifying voxels that are likely activated with a given confidence.

An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS

BackgroundSince peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment.ResultsThe proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Compared to the existing methods, the proposed approach improved peak alignment in terms of various performance measures. Also, post-hoc approach was verified to improve peak alignment by manual inspection.ConclusionsThe proposed approach, which combines the information of metabolite identification and alignment, clearly improves the accuracy of peak alignment in terms of several performance measures. R package and examples using a dataset are available at http://mrr.sourceforge.net/download.html.