Multiscale Analysis of Count Data through Topic Alignment
Abstract
Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics . Since there is no definitive way to choose and since a true value might not exist, we develop techniques to study the relationships across models with different . 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 increases. This strategy gives more insight into the process generating the data than choosing a single value of 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.
Cite
@article{arxiv.2109.05541,
title = {Multiscale Analysis of Count Data through Topic Alignment},
author = {Julia Fukuyama and Kris Sankaran and Laura Symul},
journal= {arXiv preprint arXiv:2109.05541},
year = {2022}
}