While Graphia has the ability to load a wide variety of Graph based formats, one of the important features within Graphia Enterprise is the ability to perform a correlation analysis on numerical data. This features allows relationships to be identified within the data-set that were previously unseen. This capability is provided in Graphia Enterprise by the Correlation Plugin.
This page aims to give you an overview of creating your own correlation compatible file, performing your own correlation analysis and the process of exploring your own Graph.
Graphia Enterprise uses a Pearson correlation to identify relationships within a numerical data matrix. The Pearson correlation analysis will compare data between each row of the data and assign a correlation value between the two. 0 - 1, 0 being no correlation, 1 being very highly correlation. This correlation value represents how similar the data profile is between two rows (Nodes). A graph will then be created using this information, with each row being a Node and each Edge being correlation values over a specified threshold.
For more information on Pearson correlation, click here.
In order for numerical data to be eligible for correlation analysis by Graphia Enterprise, the data must first be formatted correctly. The base format is a .csv or .tsv file with data placed in a particular order. The minimum recommended amount of information required for an analysis is RowIDs, ColumnIDs, and Data matrix.
Excluding headers, each row represents a Node. With each column representing either a row attribute (meta data), or a numerical data sample for that Node.
Column Ids are used to identify the contents contained in the column below. Column Ids need to be unique. They can consist of any character. An example would be "Food Name".
Row Id / Node Id
Row Id's (or alternatively Node Ids) are used to identify the row of data. They must be unique. They can consist of any character. An example would be "Brown Bread".
Numerical Data Matrix
This should be any number of contiguous columns, containing only numerical characters or empty values (excluding the ColumnID). This is the data on which a pearson correlation analysis will be based.
Row Attributes (Optional)
Row attributes are additional columns placed between the row ids and the numerical data matrix. They represent additional data (metadata) for that node and can consist of any characters. This information will not influence the output of a pearson correlation analysis but will be included in the resultant graph as part of a node. As an example a Row attribute ID could be Description of food and the example entry might be "A bread made from wholewheat flour"
Column Attributes (Optional)
Column Attributes are additional rows placed above the data matrix, but below ColumnIDs. This represents additional data (metadata) for that numerical matrix sample column. As an example, column attribute "Time of sample" could be an ID (placed above RowID but below ColumnIDs) and potential entries above the data matrix columns could be "12:00" "15:00" "18:00". Column attributes are mostly used to group similar sample columns together visually when looking at results.
A simple example of a minimum compatible dataset could be nutritional data of food. In this example the first column would have a header (ColumnID) which would reflect the content of the first column, such as "Food Name". Each column should then have a header that labels the data contained within that column. For the data matrix in this example, the headers would be Protein, Energy (Kcals), Starch etc.
Once the ColumnID row has been established, each row beneath represents a Node of the final graph. The first column represents the RowID. The columns after can be, if required Row Attributes. The columns after are the numerical data matrix, this can be as many columns as required however all data must either be numerical or empty. It cannot contain any non-numerical data.
When loading a correlation compatible file, Graphia Enterprise will give you the option to load the file in a few different ways. When opening a correlation file, select "Correlation CSV/TSV file" from the pop-up to load a file in the correlation plugin.
After this, you'll be provided with the Correlation Parameters dialog. This dialog contains a number of data pre-processes that can be applied to your dataset. We will just be discussing the more important pre-processes of correlation analysis.
This page gives you the option to adjust the data matrix frame start. Graphia automatically detects where the numerical data matrix begins in the data, however if numerical row attributes columns are positioned directly next to the data matrix they can be wrongly included in the data matrix. This page also gives you the option to transpose data.
Correlation - Adjust Thresholds
From here, two thresholds are able to be adjusted. Minimum and Initial Threshold. Any correlation value below the Minimum Threshold will be not be included in the resultant Graph and will not create an edge. This is used to eliminate extremely weak relationships, which you do not wish to observe. Any correlation values above the minimum threshold will persist in the Graph and will be used to create edges. Initial Threshold describes the minimum correlation value required for Edge to be displayed, i.e An initial threshold of 0.8 means edges with a correlation value of 0.8 or above display an edge, any relationships below 0.8 correlation value will not be visible. The Initial Threshold can be changed dynamically later when the graph is created.
Graphia will provides a plot of of Node and Edge counts for a range of threshold values. The default values are a good baseline to begin your investigation, but it is worth observing the plot to evaluate if your edge count will massively exceed Node count and adjust accordingly. As a general rule of thumb, the point at which Edge counts begin to decrease substantially is good Initial threshold starting point.
After this, you can confirm and finalise the settings and Graphia will begin to generate your graph.
Once all parameters are confirmed, a Graph will be created. Each Node represents a row of data from your data-set and each edge will represent a relationship that is equal to or above your Initial correlation threshold.
Two transforms will be automatically added to the graph. "Remove edges where Pearson Correlation Value < [Initial Threshold]" and "Remove Components where Component Size <= 1"
All correlation values that are above the Minimum Threshold Value from the loading step are transformed into Edges in the graph. "The Remove edges where Pearson Correlation Value..." transform allows you to adjust the cut-off point at which Edges are removed. By adjusting the threshold using the slider up, you can remove less correlated edges and only preserve the most correlated relationships. By adjusting the value you down, you create edges from less similar data. Changing this value will alter the overall shape of the graph, and is a useful tool for filtering relationships.
The "Remove Components where Component Size <= 1" will remove individual, unconnected nodes from the display. This can be disabled or deleted if the information is still required.
Below the graph view, is the Correlation data viewer. This is a specific to the Correlation plugin. This can be used to inspect data within selected nodes in a Graph.
The table on the left displays Node attribute data, this is data that was contained within the "Row attribute" section of the dataset. On the right, the profile viewer consists of a plot created from the numerical matrix data provided. By selecting multiple nodes, you can compare profiles directly and hover over for exact values. This helps identify a cause for the relationship between nodes.
Transforming the Graph
From this point, you can begin adding transforms to better explore the data. One of the more useful transforms is the MCL Cluster transform. By clicking Add Transform and adding MCL Cluster to the graph, the Graph will be clustered into groups of various sizes. This helps identify groups of nodes which are similar based on the structure within the graph. This transform adds an attribute to each node, describing the cluster it belongs to. This is then shown with a colour visualisation which is automatically added. This transform has an adjustable parameter called "Granularity" which adjusts the size of the clusters.
PageRank transform adds an attribute that can be used to rank the importance of the nodes in the overall structure. Eccentricity can also be added, which adds another attribute which represents how central a node is in the overall structure. Node attributes can be visualised through the use of visualisations.