{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multiscale-analysis-of-count-data-through","title":"Multiscale Analysis of Count Data through Topic Alignment","arxiv_id":"2109.05541","date":"2021-09-12","proceeding":null,"authors":["Julia Fukuyama","Kris Sankaran","Laura Symul"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2109.05541v2","url_pdf":"https://arxiv.org/pdf/2109.05541v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"multiscale-analysis-of-count-data-through","repo_url":"https://github.com/krisrs1128/topic_align","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.05541","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}