Mayday--integrative analytics for expression data

Abstract

Background: DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.

Results: We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.

Conclusions: We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org.

Figure 1

Main window screenshot. Mayday's main window during the analyses of the Streptomyces coelicolor dataset. Meta-information is shown hierarchically on the left, the hierarchy of probe lists with their preview images on the right. Insets show interactive plots of hierarchical column-wise clustering (left) as well as profile plots of selected clusters (right).

Figure 2

R Terminal. Mayday's R terminal offers syntax highlighting, a multi-line editor with context-based auto-completion, a command history and an interactive list of user objects in the global R environment.

Figure 3

Rule editor for a dynamic probelist. Single rules can be arranged via drag & drop into a hierarchy of groups (a rule set) using boolean operations (AND, OR). The number of probes matching the rule set is indicated in the bottom-left corner. Descriptions are automatically created from each rule's content.

Figure 4

Heatmap of the clustered experiments. The heat map shows expression values mapped to a color gradient from low (green) to high expression (red). Experiments are arranged according to a hierarchical clustering dendrogram. The order of genes and the color of gene identifiers is determined by the QT clustering (for details refer to the text) which is also used in figure 5.

Figure 5

Profile plot after QT clustering. The profile colors are determined by the QT clustering (for details refer to the text). Values have been z-scored for presentation clarity.

Figure 6

Actinorhodin pathway. The actinorhodin pathway (based on ScoCyc pathway information) is visualized with overlaid enzyme expression values and and spectrometrically measured metabolite concentration colored using a red-blue gradient (red = low, blue = high). Consecutive time points are laid out from left to right in the gradient with the leftmost vertical line representing the first experiment (20 h after inoculation) and the right-most vertical line representing the final measurement (at 60 h after inoculation). Enzymes/reactions are marked with the letter "E", metabolites are marked with the image of an Erlenmeyer flask, both are labelled with the identifiers assigned by ScoCyc.

Figure 7

Mayday's Time series Analysis Window. The alignment of two time series experiments (F199, F202) shows a time shift by one hour. Our time series alignment tool shows the original datasets (left column), corresponding profiles of a gene in both datasets (top-right) as well as user-defined statistics computed from the corresponding profiles (here the fold-change is used, bottom-right).