Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

1. Exploration-based Analysis on Tableau

1. You have a dataset and you want to explore the various relationships between columns within the data.
   1. (Figure 1) Import the dataset by clicking on ‘Connect to data’. Choose the data source type and follow instructions to import your dataset.
   2. (Figure 1) Make sure that the dimensions shelf contains categorical columns while the measures shelf contains numerical columns from your dataset. Sometimes a dimension column such as subject identifier may be listed as measure if it is numerated. Change it to dimensions by right-clicking on it and select ‘Convert to Dimension’. In Figure 1, categorical columns from the dataset such as stimulus concentration level and subject identifiers are correctly placed in dimensions shelf, and the observed concentration of cytokines are correctly placed in measures shelf.
   3. (Figure 2) If during the analysis process, a calculated field is needed, simply right click in either dimensions or measures shelf, and select ‘Create Calculated Field’. Input the calculation in the formula box with fields combined with functions or mathematical operations. Figure 2 shows how to create a new field called PFD > 2 by aggregating PFD2, PFD3, and PFD4 values.
   4. (Figure 3) Generate a 2-D plot in Tableau, typically with a dimension and a measure of interest. Since the interface offers simple drag-and-drop interaction, the choice of dimension and measure can be easily changed. Dimensions are typically placed in columns shelf, and measures in rows shelf. In Figure 3, the dimensions shelf contains stimulus concentration level, and the measures shelf contains observed concentration level of cytokines.
   5. (Figure 3) To differentiate the visualization by a specific dimension, place that dimension label in text, color or size shelf, depending on the nature of the dimension. Text shelf attempts to differentiate data in the visualization by labeling them with textual information provided by the dimension selected. Colour and size shelf will automatically differentiate data by color and size, which can be adjusted with the scroll bar located at the bottom of each shelf. In figure 3, placing genotype in color shelf leads to the separation of three genotypes by distinct colors.
   6. (Figure 3) Filter the visualization by the specific column variables in other dimension. This can be done by dragging the dimension label into the filter shelf. A window will appear with the list of values to choose from. The selected value will be the only values remaining in the visualization.
   7. (Figure 4) You may combine visualization with other dimension or measure to produce a matrix of visualization. This can be done by dropping multiple dimensions in columns shelf, and multiple measures in rows shelf. Figure 4 contains an example of two-column visualization matrix by placing two different dimensions in the columns shelf.
   8. To produce a similar visualization, right click on the current sheet on the bottom left hand corner, and selected ‘duplicate sheet’.
   9. Iterate the analysis process to identify interesting trends or outliers that may help you generate new hypothesis about the data.
2. You have multiple dataset generated from the same source, and want to explore the possible connections between these datasets.
   1. Place multiple datasets in a same source, i.e. place two spreadsheets into the same workbook, to allow Tableau to connect to these dataset through the same connection.
   2. (Figure 5) Connect the datasets through logical join of key values, making sure that the dimensions that are the same for multiple datasets are selected. In Figure 5, there are five key values for joining: cell type, stimulus concentration level, stage or group, stimulus, and subject identifier.
   3. Perform analysis similar to section 1.1.

2. Presentation-based Needs

1. You know the relationships between data, but you want to be able to quickly generate a list of visualizations for use in presentation.
   1. Follow protocol outlined in section 1.1, and annotate the visualizations accordingly.
   2. Use the export function in Tableau to produce images of visualizations.
2. You have generated a set of powerful visualizations in Tableau workbook and want to share that workbook.
   1. Save the workbook as Tableau packaged workbook and share this file.
   2. Colleagues without Tableau Desktop can download Tableau Reader to open the packaged workbook. Tableau Reader allows your colleagues to interact with the visualizations that you have created.

3. REPRESENTATIVE RESULTS

Figure 1. A screenshot of Tableau after importing the spreadsheet named NFKBIA from the Excel file demo.xls. The dimensions and measures shelves were properly populated with the categorical and numerical data, respectively.

Figure 2. The Calculated Field window is invoked to create a special calculated field to use in Tableau. The list on the bottom left-hand box helps identify possible fields, and the list on the right-hand side contains abbreviation of functions that can be used in the formula. In this example, we wanted to add the values for PFD4, PFD3 and PFD2 to obtain the final value that we refer to as PFD > 2

Figure 3. Visualization of stimulus concentration level vs. observed cytokine concentration. The visualization shows a plot of the different concentration levels of the stimulus 3M-002 against the observed concentration of the cytokine TNF-α. The colors of the lines refer to the different genotypes for a single-nucleotide polymorphism in the NFKBIA gene of the individuals in our innate immune study.

Figure 4. A screenshot of a two-column visualization matrix. We generated a two-column matrix to facilitate a side-by-side comparison of responses to two stimuli, 3M-003 and LPS. The x-axes are the different concentration levels of the two stimuli, and the y-axis plots the values of the calculated field, PFD > 2.

Figure 5. These Tableau dialogue windows illustrate how to connect data recorded in different spreadsheets. Connecting data from different spreadsheets can be accomplished by combining these using logical join clauses of key values.

Table 1. List of visual analytics tools and some of their features.