OWL Stat App is a Shiny-based Web application, accessible independently of the operating system and without the need to install programs locally. It has been implemented entirely in the R language (R v.3.1.1; R Development Core Team, 2011; http://cran.r-project.org). All calculations are performed with caret package to classification training and ROCR package for visualizing classifier performance in R. The pheatmap package is used for drawing heatmaps.

This app combines the R-based analytical tools with metabolite identification and pathway mapping tools, overlaying the users data on the pathway mapping libraries of SMPDB (The Small Molecule Pathway Database) and pathway outputs originally developed in our laboratory.

The analysis and plots in this application can be configured from the

- Groups: Check/uncheck the groups you want to show/hide on the Volcano plot.
- Volcano - Range of X-axis: Using the slider bar, you can enter the minimum and maximum value for the X-axis range.
- Volcano - Range of Y-axis: Using the slider bar, you can enter the minimum and maximum value for the Y-axis range.
- Volcano - Dot size: The marker size can be set using this slider bar.
- Volcano - Axis: Other settings for Volcano plot's plotting area are:
- Horizontal and vertical lines: Places a certain number of grid lines on the plot.
- p-value = 0.05: Plots a horizontal dashed red line showing where p-value = 0.05 is.
- p-value = 0.01: Plots a horizontal dashed red line showing where p-value = 0.01 is.
- p-value = 0.001: Plots a horizontal dashed red line showing where p-value = 0.001 is.
- Plain figure: All points are plotted in black dots, showing no distinction between groups.

- y = x
- y = x
^{2} - y = x
^{1/2} - y = 1/x
- y = 1/x
^{2} - y = 1/x
^{1/2} - y = log(x+1)
- y = sh(x)

- Boxplot - Color Groups: This panel allows setting the colors and marker's shapes in which comparison groups are represented in the Boxplot analysis. Two drop down menus will be presented for each comparison group.
- Boxplot - Samples:
- Show sample distribution: This check box controls whether the sample distribution will be shown on the Boxplot. The width of the area in which the distribution is plotted can be changed through the sliding bar below.

- Correlation between samples: Different values for the correlation coefficients that can be used in the correlation plot can be selected here.
- pearson
- kendall
- spearman
- Distance: The distance that is used in the correlation plot can be changed in this drop down menu.
- euclidean
- maximum
- manhattan
- minkowski

- Fold-change - Subsample: The value selected in this sliding bar sets the percentage of samples in the subsample of each group.
- Fold-change - Repeat: Sets the number of times the process is repeated.

- X component: sets the component plotted in the X axis.
- Y component: sets the component plotted in the Y axis.
- Scale: Scaling is applied to the plot if this option is checked.
- Horizontal and vertical lines: Sets whether grid lines are to be plotted.
- Show code: Selecting this field, sample codes are shown.

- Plot width: Plot width can be adjusted with this field.
- Plot height: Plot height can be adjusted with this field.
- Rows clustered: This field sets whether rows in the heatmap are clustered or not.
- Columns clustered: This field sets whether columns in the heatmap are clustered or not.

- Plot width: Plot width can be adjusted with this field.
- Plot height: Plot height can be adjusted with this field.

**AA**Amino acids**AC**Acylcarnitines**ArAA**Aromatic amino acids**BA**Bile acids**BCAA**Branched chain amino acids**Cer**Ceramides**ChoE**Cholesteryl esters**CMH**Monohexosylceramides**DAG**Diacylglycerols**FAA**Fatty acid amides (Primary Fatty Amides)**FFA**Free fatty acids (Non-esterified fatty acids)**FFAox**Oxidized fatty acids**FSB**Free sphingoid bases**MAG**Monoacylglycerides**MUFA**Monounsaturated fatty acids**NAE**N-acyl ethanolamines**OPLS**Orthogonal partial least-squares to latent structures**PC**Phosphatidylcholines**PCA**Principal Component Analysis**PE**Phosphatidylethanolamines**PG**Phosphatidylglycerols**PI**Phosphatidylinositols**PUFA**Polyunsaturated fatty acids**SFA**Saturated fatty acids**SM**Sphingomyelins**TAG**Triacylglycerols**UFA**Unsaturated fatty acids**UPLC®-MS**Ultra performance liquid chromatography-mass spectrometry

Poster presented at the 11

OWL Stat App, the web application for metabolomics data analysis is presented during the Science+ meeting.

The activity of the company is centered in the area of health, with pioneering applications in the international scientific panorama, and whose objective is to identify, validate, patent and commercialize diagnostic and/or prognostic systems, as well as therapeutic targets involved in the development of complex diseases.

Thus,

Besides,

Since its foundation

Furthermore, a number of strategic alliances with biotechnology and bioinformatics companies, hospitals, research centers and universities has also enhanced

On the other hand the leading position of

Barr, Jonathan, J. Caballería, I. Martínez-Arranz, A. Domínguez-Díez, C. Alonso, J. Muntané, M. Pérez-Cormenzana, et al. 2012. **Obesity-Dependent Metabolic Signatures Associated with Nonalcoholic Fatty Liver Disease Progression.** *J Proteome Res* 11 (4). OWL, Derio, Bizkaia, Spain.: 2521-32. doi:10.1021/pr201223p. http://dx.doi.org/10.1021/pr201223p.

Barr, Jonathan, Mercedes Vázquez-Chantada, Cristina Alonso, Miriam Pérez-Cormenzana, Rebeca Mayo, Asier Galán, Juan Caballería, et al. 2010. **Liquid Chromatography-Mass Spectrometry-Based Parallel Metabolic Profiling of Human and Mouse Model Serum Reveals Putative Biomarkers Associated with the Progression of Nonalcoholic Fatty Liver Disease.** *J Proteome Res* 9 (9). OWL, Bizkaia Technology Park, 48160-Derio, Bizkaia, Spain.: 4501-12. doi:10.1021/pr1002593. http://dx.doi.org/10.1021/pr1002593.

Martínez-Arranz, Ibon, Rebeca Mayo, Miriam Pérez-Cormenzana, Itziar Mincholé, Lorena Salazar, Cristina Alonso, and José M. Mato. 2015. **Enhancing Metabolomics Research Through Data Mining.** *J Proteomics*, doi:10.1016/j.jprot.2015.01.019. http://dx.doi.org/10.1016/j.jprot.2015.01.019.

Genz, Alan, and Frank Bretz. 2009. **Computation of Multivariate Normal and T Probabilities**. Lecture Notes in Statistics. Heidelberg: Springer-Verlag.

Genz, Alan, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, Friedrich Leisch, Fabian Scheipl, and Torsten Hothorn. 2014. **mvtnorm: Multivariate Normal and T Distributions**. http://CRAN.R-project.org/package=mvtnorm.

Gesmann, Markus, and Diego de Castillo. 2011. **googleVis: Interface Between R and the Google Visualisation API.** *The R Journal* 3 (2): 40-44. http://journal.r-project.org/archive/2011-2/RJournal_2011-2_Gesmann+de~Castillo.pdf.

Jarek, Slawomir. 2012. **mvnormtest: Normality Test for Multivariate Variables**. http://CRAN.R-project.org/package=mvnormtest.

Mevik, Bjørn-Helge, and Ron Wehrens. 2007. **The Pls Package: Principal Component and Partial Least Squares Regression in R.** *Journal of Statistical Software* 18 (2): 1-24. http://www.jstatsoft.org/v18/i02.

Mevik, Bjørn-Helge. 2006. **The Pls Package.** *R News* 6 (3): 12-17. http://CRAN.R-project.org/doc/Rnews/.

Mevik, Bjørn-Helge, Ron Wehrens, and Kristian Hovde Liland. 2013. **pls: Partial Least Squares and Principal Component Regression**. http://CRAN.R-project.org/package=pls.

R Core Team. 2014. **R: A Language and Environment for Statistical Computing**. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.

RStudio Team. 2012. **RStudio: Integrated Development Environment for R**. Boston, MA: RStudio, Inc. http://www.rstudio.com/.

RStudio, and Inc. 2014. **shiny: Web Application Framework for R**. http://CRAN.R-project.org/package=shiny.

Xie, Yihui. 2013. **Dynamic Documents with R and Knitr**. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.name/knitr/.

**knitr: A General-Purpose Package for Dynamic Report Generation in R**. http://yihui.name/knitr/.

**knitr: A Comprehensive Tool for Reproducible Research in R.** In *Implementing Reproducible Computational Research*, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595/.

Thank you for your interest in our application for metabolomics data analysis!

If you are looking for information about OWL Stat App you can contact us by email at owlstatapp@owlmetabolomics.com.

OWL is a trading name of

ONE WAY LIVER, S.L

Parque Tecnológico de Bizkaia

Edificio 502 - Planta 0

48160 Derio - Bizkaia - Spain

Phone: +34 94 431 85 40

Fax: +34 94 431 71 40

Please, cite OWL in your Material and Methods as "Barr et al. J Proteome Res. 2012;11;2521-32" for sample preparation and UHPLC-MS analysis; and "Martínez-Arranz et al. J Proteomics. 2015;127(B):275-88" for data processing.

The volcano plot is an effective and easy-to-interpret graph that summarizes both fold-change and t-test criteria. It is a scatter-plot of the negative log

Metabolites with statistically significant differential levels according to the t-test will lie above a horizontal threshold line. Metabolites with large fold-change values will lie far from the vertical threshold line at log

Selecting a metabolite in this plot, its description, distribution between the two groups (boxplot), ROC analysis and pathways in which it is involved are shown in the following windows

SMPDB is offered to the public as a freely available resource. Use and re-distribution of the data, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgment of the source material (SMPDB) and the original publication (see below). We ask that users who download significant portions of the database cite the SMPDB paper in any resulting publications.

1. Wishart DS, Frolkis A, Knox C,

2. Jewison T, Su Y, Disfany FM,

Box plots display differences between populations without making any assumptions of the underlying statistical distribution: they are non-parametric. The spacings between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers. In addition to the points themselves, they allow one to visually estimate various L-estimators, notably the interquartile range, midhinge, range, mid-range, and trimean.

If the two distributions being compared are similar, the points in the

A

Box plots display differences between populations without making any assumptions of the underlying statistical distribution: they are non-parametric. The spacings between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers. In addition to the points themselves, they allow one to visually estimate various L-estimators, notably the interquartile range, midhinge, range, mid-range, and trimean.

If the two distributions being compared are similar, the points in the

A

SMPDB is offered to the public as a freely
available resource. Use and re-distribution of the
data, in whole or in part, for commercial purposes
requires explicit permission of the authors and
explicit acknowledgment of the source material
(SMPDB) and the original publication (see below).
We ask that users who download significant portions
of the database cite the SMPDB paper in any resulting
publications.

1. Wishart DS, Frolkis A, Knox C,*et al.*, SMPDB: The Small Molecule Pathway Database. *Nucleic Acids Res. 2010 Jan;38(Database issue):D480-7.*

2. Jewison T, Su Y, Disfany FM,*et al.*, SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database *Nucleic Acids Res. 2013 Submitted.*

1. Wishart DS, Frolkis A, Knox C,

2. Jewison T, Su Y, Disfany FM,

Diacylglycerides (DAG), delta-6 desaturase (Δ6D), delta-5 desaturase (Δ5D), elongase (ELOVL), Β-oxidation (Β-ox), cyclooxygenase-2 (COX-2), phosholipases (PL).