CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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JavaScript Object Notation (JSON) is a lightweight, text-based, data interchange format (Crockford, 2006). Even though JSON derives straight from JavaScript, it is language-independent, hence it can be used by many different programming languages. JSON was employed in combination with jQuery and AJAX for the asynchronous and indiscernible exchange of data in the background between the webbrowser and the web-server. On the server-side, Perl (http://www.perl.org/) version 5.8.0 was used as the supporting scripting language. Even though Perl is not part of the RIA approach, it still constitutes a powerful tool in the field of bioinformatics. 6.2.4 The iWebPlots package Over the past years, there have been numerous efforts to implement interactive graphics via R. In contrast to static graphs, with interactive plots the users have the ability to select, query and interact with the area defined by the plot as well as make use of dynamic features such as rotations and zooming. Only a miniscule number of R packages that implement this functionality have been released and they usually act as standalone programs. Therefore, the probability of them being embedded within web pages is highly diminished. This drawback can usually be overridden, when the plots are used in combination with other programming languages and/or plug-ins that add the interactive functionality. The iWebPlots package (<strong>Chatzimichali</strong> and Bessant, 2011) is a novel R package for the creation of interactive web-based plots, developed during this project. The first version of the package is available on CRAN, the official R repository, at http://CRAN.R-project.org/package=iWebPlots. However, the functionality of the package has been expanded since the first release, therefore new releases will be uploaded in the near future. In this package, interactivity is implemented using the fundamental HTML image maps methodology. An image-map can be defined as an image with clickable regions, commonly referred to as “hot spots”. These interactive areas may consist of 181
ectangles, circles and/or irregular polygons. iWebPlots is mainly dependent on the geneplotter Bioconductor (Gentleman et al., 2004; Gentleman and Biocore, 2012) package, which, among its other features, generates interactive heatmaps for microarray data. The functionality of the interactive image maps, as provided by the geneplotter package, was further extended towards building a variety of different types of plots such as scatterplots, histograms and barplots, among many others. The functions of iWebPlots take as input a matrix of coordinates with associated metadata, which are subsequently drawn in a plot using the R platform. The image that contains the plot is subsequently saved as a bitmap or PNG image. Prior to adding the image maps’ interactive features, the coordinates of the plot have to be converted from the Cartesian coordinate system into the user graphics coordinate system, which is measured in pixels. Once the corresponding pixels are calculated, an HTML layer is constructed upon the PNG image. At the end of this process, every point in the plot corresponds to a clickable area within the map. Additional features provided by the iWebPlots package include dynamic tooltips and text annotations as well as asynchronous alternations between two- dimensional and three-dimensional scatterplots. Finally, each plot can be interlinked with a static (HTML) or fully interactive data table (DataTables), which displays additional information about the data in the graph. The text of the tooltips and the data tables is customised directly by the users. The greatest strength of the iWebPlots package is its simplicity and ease of use; the package is based on pure HTML image maps methodology, so additional specialised software such as applets, libraries, graphical user interfaces (GUIs) or plug-ins are required by the end user. This allows the generated web-based plots to run problemfree in every web browser with minimum execution times. The output of iWebPlots can be embedded within any HTML page or web project outside the realms of this project. Furthermore, the users can easily modify and expand the functionality of the package and of the generated HTML pages, therefore great extensibility is ensured. There are other R packages that implement similar functionality. The googleVis (Gesmann and de Castillo, 2011) package provides an R interface for the popular 182
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CRANFIELD UNIVERSITY Eleni Anthippi
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ABSTRACT Muscle foods such as meat,
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TABLE OF CONTENTS ABSTRACT ........
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6.2.4 The iWebPlots package .......
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Figure 3-12 Speedup produced by the
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Figure 6-9 Sweave example for the d
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TABLE OF EQUATIONS Equation 1 Mean-
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RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
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Figure 1-1 Evolution from molecular
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1.1.2.1 Fourier Transform Infrared
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1.1.3 Microbial Spoilage in Meat Sy
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The multivariate techniques applied
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1.4 Multivariate Analysis: Unsuperv
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1.4.2 Cluster Analysis Cluster anal
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1.5 Multivariate Analysis: Supervis
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In any linearly separable binary da
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Where ( ) are the Lagrange multipli
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Every kernel is characterised by a
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The “one-against-all” approach
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Furthermore, metrics such as the bi
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1.6.3 Leave-One-Out Cross-Validatio
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In such cases, the model becomes qu
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1.8 Aims and objectives The overall
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2 Development of the multivariate a
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Sensory panel scores were of the hi
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DF (Hz) 2.2.1.5 Electronic Nose (e-
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Figure 2-3 Data intersection The fi
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3. Validation Techniques In the cas
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Figure 2-4 The process of construct
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2.3 Results and Discussion 2.3.1 Pr
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(b) HPLC data (c) e-nose data Figur
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In the case of FTIR, linear classif
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2.3.2.2 Class Prediction Accuracies
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Figure 2-7 Class prediction rates o
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The topology of the three-dimension
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Figure 2-9 Three-dimensional error
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Furthermore, a thorough comparison
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In the master/slave architecture, a
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precision of the classification acc
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Figure 3-3 The steps of the Nelder-
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3.3 Results and Discussion 3.3.1 Li
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3.3.2 Nonlinear Models In order to
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9) 10) 11) 12) 13) 14) 15) 16) Figu
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As an additional visual aid, these
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a) Number of iterations b) Number o
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Figure 3-10 Comparison of the execu
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Figure 3-12 Speedup produced by the
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4 Integration of Heterogeneous Data
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4.2.2 Procrustes Analysis Procruste
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symmetric method since the ordering
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Figure 4-3 Steps of CPCA for the da
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4.2.5 Data Integration and Analysis
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Figure 4-4 Data integration workflo
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a) GPA b) CPCA Figure 4-6 The conse
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accuracies as the linear models; po
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Figure 4-8 Classification Results f
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Similar to FTIR, the PLS-DA ensembl
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4.3.3 Permutation Tests Even though
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Even though the interpretation of t
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Figure 4-12 Distribution plots of t
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RBF SVMs Datasets Original %CC Mean
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Figure 4-14 Boxplots representing t
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5 Application of the multivariate a
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Figure 5-1 Mean FTIR spectra for ca
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5.2.2.2 Fourier Transform Infrared
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5.2.2.4 Data Overview For each expe
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5.2.3.3 High Throughput Liquid Chro
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Figure 5-5 displays the PCA scores
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(a) GPA (b) CPCA Figure 5-6 The con
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observation confirms that indeed CP
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Page 145 and 146: (a) RBF SVMs (b) PLS-DA Figure 5-9
Page 147 and 148: RBF SVMs Datasets Original %CC Mean
Page 149 and 150: Figure 5-12 Execution times of the
Page 151 and 152: (a) FTIR data (b) Raman data Figure
Page 153 and 154: The classification results for the
Page 155 and 156: Figure 5-16 Class prediction rates
Page 157 and 158: (a) RBF SVMs (b) PLS-DA Figure 5-17
Page 161 and 162: Figure 5-20 Execution times of the
Page 163 and 164: (a) FTIR data (b) HPLC data 148
Page 165 and 166: (a) GPA (b) CPCA Figure 5-22 The co
Page 167 and 168: The classification results of the f
Page 169 and 170: Figure 5-24 Classification Results
Page 171 and 172: Figure 5-25 Class prediction rates
Page 173 and 174: The permutation results for the dat
Page 175 and 176: Figure 5-28 Distribution plots of t
Page 179 and 180: Figure 5-30 Boxplots representing t
Page 181 and 182: 5.4 Comparison of the individual ca
Page 183 and 184: Figure 5-32 Investigating the commo
Page 185 and 186: 6 Development of improved visualisa
Page 187 and 188: 6.2.2 Generating static graphs As d
Page 189 and 190: Figure 6-2 Construction process of
Page 191 and 192: 6.2.2.2 Generating dynamic reports
Page 193 and 194: activity, while simultaneously the
Page 195: or disappeared (Wang et al., 2008).
Page 199 and 200: In addition to the static figures g
Page 201 and 202: c) graphics package d) ggplot2 pack
Page 203 and 204: In addition to aesthetics, faceting
Page 205 and 206: Datasets FTIR HPLC e-nose PLS-DA (L
Page 207 and 208: Figure 6-10 Interactive dendrogram
Page 209 and 210: 6.4 Conclusion The work reported in
Page 211 and 212: large number of individual models i
Page 213 and 214: The generated suite of tools, as pr
Page 215 and 216: In addition, the difficulty to corr
Page 217 and 218: REFERENCES Almeida, J. A. S., Barbo
Page 219 and 220: Processing., Proceedings of the 12t
Page 221 and 222: Clarke, B., Fokoué, E. and Zhang,
Page 223 and 224: spectroscopy and machine learning",
Page 225 and 226: Gower, J. C. (1975), "Generalized p
Page 227 and 228: Jardon, M. (2006), Systems Biology:
Page 229 and 230: Nicolaou, N., Xu, Y. and Goodacre,
Page 231 and 232: Shah, A. A., Barthel, D., Lukasiak,
Page 233 and 234: Vera, G., Jansen, R. and Suppi, R.
Page 235: Xu, Y. and Goodacre, R. (2012), "Mu
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