correlationcalculator: a bioinformatics tool for building single interaction metabolites network based on expression data

Addressing Metabolomics Data Analysis Using Advanced Bioinformatics Tools

In the last decade, metabolomics has emerged as an omics science due to advances in analytical technologies such as Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS). These techniques allow simultaneous measurement of hundreds to thousands of small molecule metabolites, creating complex multidimensional datasets. Metabolomics experiments can be performed in targeted or untargeted modes. Targeted metabolomics experiments measure specific classes of metabolites. They are usually hypothesis-driven, while untargeted approaches attempt to measure as many metabolites as possible and are hypothesis-generating in nature. Targeted assays usually include internal standards and thus allow for absolute quantification of metabolites of interest. In contrast, untargeted assays allow relative quantification and include many unknown metabolites1.
Analysis of metabolomics data is a multi-step process that leverages many specialized software tools1. It can be divided into the following three major steps: (1) data processing and quality control, (2) statistical analysis, and (3) biological data interpretation. The tools described here are designed to enable the latter step of the analysis.
An intuitive and popular way to interpret metabolomics data is to map the experimental measurements onto metabolic pathways. Numerous tools have been designed to achieve this2,3,4,5, including Metscape, developed by our group6. Pathway mapping is often combined with enrichment analysis, which helps identify the most significant pathways7,8. These techniques first gained prominence in the analysis of gene expression data and have been successfully applied for the analysis of proteomics and epigenomics data9,10,11,12,13. However, the analysis of metabolomics data presents a number of challenges for knowledge-based approaches. First, in addition to the endogenous metabolites, metabolomics assays measure exogenous compounds, including those that come from nutrition and other environmental sources. These compounds, as well as metabolites produced by bacteria, cannot be mapped onto human or metabolic pathways of other eukaryotic organisms. Further, pathway coverage of secondary metabolism and lipid metabolism currently does not allow high-resolution mapping at the level that would easily support the biological interpretation of the data14,15.
Data-driven network analysis techniques can help overcome these challenges. For example, correlation-based networks can help derive relationships among both known and unknown metabolites and facilitate the annotation of the unknowns16. While computing Pearson&#39;s correlation coefficients is the most straightforward approach to establishing the linear relationships between metabolites, the disadvantage is that it captures both direct and indirect associations17,18,19. An alternative is to compute partial correlation coefficients that can distinguish between direct and indirect associations. Gaussian graphical modeling (GGM) can be used to estimate partial correlation networks. However, GGM requires that the sample size and the number of features be comparable. This condition is rarely met in untargeted LC-MS data that contains measurements for thousands of metabolic features. Regularization techniques can be utilized to overcome this limitation. Graphical lasso (Glasso) and nodewise regression are popular methods for regularized estimation of the partial correlation network16,20.
The first of the bioinformatics tools presented here, CorrelationCalculator16, is based on the debiased sparse partial correlation (DSPC) algorithm. DSPC relies on de-sparsified graphical lasso modeling. The underlying assumption of the algorithm is that the number of connections among the metabolites is considerably smaller than the number of samples, i.e., the partial correlation network of metabolites is sparse. This assumption allows DSPC to discover the connectivity among large numbers of metabolites using fewer samples, leveraging regularized regression techniques. Further, using a debiasing step for the regularized regression estimates, it obtains sampling distributions for the edge parameters that can be used to construct confidence intervals and test hypotheses of interest (e.g., presence/absence of a single or a group of edges). The presence or absence of an edge in the partial correlation network can thus be formally tested using the computed p-values.
CorrelationCalculator proved to be very useful for single-group analysis16; however, the objective of many metabolomics experiments is the differential analysis of two or more conditions. While&#160;CorrelationCalculator can be employed on each of the groups separately to generate partial correlation networks for each condition, this approach limits the number of samples that can be used for network generation. Since a sufficiently large sample size is one of the biggest considerations in data-driven analysis, methods that can leverage all available samples in the data to construct networks are highly desirable. This approach is implemented in the second tool presented here, called Filigree21. Filigree relies on the previously published Differential Network Enrichment Analysis (DNEA) algorithm22. Table 1 shows the applications and the workflow of both tools.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Number of experimental conditions (k)</td>
			<td>k = 1</td>
			<td>k = 2</td>
		</tr>
		<tr>
			<td>Software tool</td>
			<td>CorrelationCalculator</td>
			<td>Filigree</td>
		</tr>
		<tr>
			<td>Input data</td>
			<td>&#8226; Metabolites x Samples data matrix</td>
			<td>&#8226; Metabolites x Samples data matrix 
			&#8226; Experimental groups</td>
		</tr>
		<tr>
			<td>Workflow 
			&#8226; Pretreatment 
			&#8226; Network estimation 
			&#8226; Network clustering 
			&#8226; Enrichment analysis</td>
			<td> 
			&#8226; Log transformation; autoscaling 
			&#8226; DSPC 
			&#8226; Via external apps 
			&#8226; No</td>
			<td> 
			&#8226; Log transformation; autoscaling 
			&#8226; Joint network estimation 
			&#8226; Consensus clustering 
			&#8226; NetGSA</td>
		</tr>
		<tr>
			<td>Data visualization</td>
			<td>Via external app, e.g., Cytoscape</td>
			<td>Via external app, e.g., Cytoscape</td>
		</tr>
		<tr>
			<td>Testing metabolic modules for the association with outcome of interest (optional)</td>
			<td>Via external apps</td>
			<td>Via external apps</td>
		</tr>
	</tbody>
</table>
Table 1: The scope of application and the workflow of CorrelationCalculator and Filigree.

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis 

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Metabolomics data analysis is a multi-step process that leverages many specialized software tools. This data analysis can be divided into three major steps, data processing and quality control, statistical analysis, and biological data interpretation. The tools described in this protocol are designed to enable the latter step in the analysis.In the last decade, metabolomics has emerged as anomic science due to advances in analytical technologies such as gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry. These techniques allow the simultaneous measurements of hundreds to thousands of small molecule metabolites creating complex multidimensional data sets. Metabolomics data analysis presents several challenges for pathway based enrichment approaches.First, a significant number of metabolites cannot be mapped onto metabolic pathways. Further, pathway coverage of secondary and lipid metabolism are not sufficient. Therefore, alternative approaches are needed for the biological interpretation of the data.Data-driven network analysis techniques can help overcome challenges associated with knowledge-based pathway enrichment analysis for metabolomics data. For example, correlation networks can help derive relationships among both known and unknown metabolites, and can thus facilitate annotations of the unknowns.

Watch this Scientific Journal Video about CorrelationCalculator: A Bioinformatics Tool for Building Single Interaction Metabolites Network Based on Expression Data at JoVE.com

CorrelationCalculator: A Bioinformatics Tool for Building Single Interaction Metabolites Network Based on Expression Data  

To begin, download a sample comma-delimited input file containing a list of metabolites with experimental measurements. Double click on the downloaded sample file to open it and verify that it contains labels for both the samples and the metabolites. Next, download the correlation calculator Java application, and double click on the downloaded JAR file to launch the application.On the input tab, click the browse button to upload the input file. Under specify file format, select samples in rows. Click on the next button from the bottom right of the window to move to the data normalization tab.Under select methods, check the box next to log to transform data and autoscale data. Under normalized data, click the run button. Once the normalization is complete, click the save button to save the new data file.Click on the next button to move to the data analysis tab and under Calculate Pearson's Correlation, click run to determine the best Pearson's correlation range for the data. Click on the view histogram button to review the frequency of the maximal Pearson's correlation scores per feature in the view heat map button to review the representation of Pearson's correlation matrix. Under Filter by Pearson's Correlations, leave the default numbers to filter by a range of 0.00 to 1.00.Then under Select Partial Correlation Method, select the desired method as DSPC method. And under Calculate Partial Correlations, click the run button. Click on the view CSV file to view the results, and click the save button to save the results.A representative network constructed from a subset of the KORA population study metabolomics data consisting of 151 metabolites across 240 subjects is shown. Consensus network clustering resulted in the identification of nine sub-networks or metabolic modules.

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JoVE Journal

Biology

468 조회수.  University of Michigan, Ann Arbor. 시작하려면 실험 측정값이 포함된 대사 산물 목록이 포함된 쉼표로 구분된 샘플 입력 파일을 다운로드하십시오. 다운로드한 샘플 파일을 두 번 클릭하여 열고 샘플과 대사 산물 모두에 대한 라벨이 포함되어 있는지 확인합니다. 그런 다음 상관 계산기 Java 응용 프로그램을 다운로드하고 다운로드한 JAR 파일을 두 번 클릭하여 응용 프로그램을 시작합니다.입력 탭에서 찾...