Comparing baseline motif frequencies in repertoires

Not every motif is equally likely to occur in the sequences of an immune repertoire. The variability of immune receptors is for example restricted by which V, D and J genes are present, among other factors. Using immuneML, we can investigate the baseline motif frequencies of immune receptor or repertoire datasets.

One method for comparing the baseline motif frequency distributions between different classes (e.g., sick versus healthy, or antigen binding versus non-binding) is by encoding the dataset using the KmerFrequency encoder, and generating a FeatureComparison report. This analysis can be executed using the ExploratoryAnalysis instruction, see How to perform an exploratory data analysis for more details.

The figures below show an example of the FeatureComparison report plot executed on the Quickstart dataset when encoded with a 4-mer frequency encoding. In this dataset, the synthetic disease signal ‘VLEQ’ was implanted. The figure on the left shows the complete plot, where it can be seen that there is a subset of 4-mers which occur at a higher frequency in the repertoires where the disease signal is present. The figure on the right shows the data from the same figure, but zoomed in on the left lower corner. The generated figure is interactive, and it is possible to hover over the points to reveal which feature they represent. As can be seen in the right figure, the feature ‘VLEQ’ appears more frequently in the repertoires where signal_disease = True.

Alternatively, when investigating the occurrence of more complex motifs in repertoire datasets, the MatchedRegex encoder can be used in combination with the Matches report. This will produce a table summarizing how often a set of regular expressions are matched in the sequences of the repertoire dataset.