A Coding Basis and Three-in-One Integrated Data Visualization Method ‘Ana’ for the Rapid Analysis of Multidimensional Omics Dataset

Abstract

With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB® coding basis and three-in-one integrated method, ‘Ana’, was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research.

An Insight into Wheat Germ Oil Nutrition, Identification of Its Bioactive Constituents and Computer-Aided Multidimensional Data Analysis of Its Potential Anti-Inflammatory Effect via Molecular Connections

“…Figure 2 shows the heat map cluster data that were created for the bioactive components of WGO by digitizing SwissADME data on physical and chemical properties to identify significant relationships with the new parameters [ 38 ]. Each heat map cell was a numeric count represented by a color code, with darker colors denoting larger linked physiochemical features and drug likelihood.…”

“…Using Microsoft ® Excel, data were saved in the ".xlsx" file format; each variable (21 physiochemical characteristics) was chosen as a row, and each constituent (12 active constituents) was chosen as a column. Statistical analysis was performed using a MATLAB ® coding base and the 3 in 1 "Ana" integrated method as described by Zhao and Wang [ 38 ]. The omics dataset from an Excel ® file was used to create a 3D heat map and hierarchical cluster heat map.…”