Showing papers by "Tianwei Yu published in 2013"

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

Abstract: Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data.

Abstract: Feature detection is a critical step in the preprocessing of liquid chromatography–mass spectrometry (LC–MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC–MS data. With high-resolution mass spectrometry such as liquid chromatograph–Fourier transform mass spectrometry (LC–FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of f...

Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences

Abstract: High-throughput expression technologies, including gene expression array and liquid chromatography--mass spectrometry (LC-MS) and so on, measure thousands of features, i.e., genes or metabolites, on a continuous scale. In such data, both linear and nonlinear relations exist between features. Nonlinear relations can reflect critical regulation patterns in the biological system. However, they are not identified and utilized by traditional clustering methods based on linear associations. Clustering based on general dependences, i.e., both linear and nonlinear relations, is hampered by the high dimensionality and high noise level of the data. We developed a sensitive nonparametric measure of general dependence between (groups of) random variables in high dimensions. Based on this dependence measure, we developed a hierarchical clustering method. In simulation studies, the method outperformed correlation- and mutual information (MI)-based hierarchical clustering methods in clustering features with nonlinear dependences. We applied the method to a microarray data set measuring the gene expression in cell-cycle time series to show it generates biologically relevant results. The R code is available at http://userwww.service.emory.edu/~tyu8/GDHC.

AAPL: Assessing Association between P-value Lists.

Abstract: Joint analyses of high-throughput datasets generate the need to assess the association between two long lists of p-values. In such p-value lists, the vast majority of the features are insignificant. Ideally contributions of features that are null in both tests should be minimized. However, by random chance their p-values are uniformly distributed between zero and one, and weak correlations of the p-values may exist due to inherent biases in the high-throughput technology used to generate the multiple datasets. Rank-based agreement test may capture such unwanted effects. Testing contingency tables generated using hard cutoffs may be sensitive to arbitrary threshold choice. We develop a novel method based on feature-level concordance using local false discovery rate. The association score enjoys straight-forward interpretation. The method shows higher statistical power to detect association between p-value lists in simulation. We demonstrate its utility using real data analysis. The R implementation of the method is available at http://userwww.service.emory.edu/~tyu8/AAPL/.