Papers › Multiscale Analysis of Count Data through Topic Alignment
Multiscale Analysis of Count Data through Topic Alignment
Julia Fukuyama, Kris Sankaran, Laura Symul
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Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics K. Since there is no definitive way to choose K and since a true value might not exist, we develop techniques to study the relationships across models with different K. This can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits in two when K increases. This strategy gives more insight into the process generating the data than choosing a single value of K would. We design a visual representation of these cross-model relationships, which we call a topic alignment, and present three diagnostics based on it. We show the effectiveness of these tools for interpreting the topics on simulated and real data, and we release an accompanying R package, alto, available at https://lasy.github.io/alto.
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