Open sa-lee opened 8 years ago
Since structure model is the same thing as LDA (with 'documents' being samples, 'topics' as latent populations, and 'words' as genetic markers) we could use some of the ideas in http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf https://github.com/cpsievert/LDAvis to create some more informative/compelling plots
See also https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
Then our fully interactive plot that is like LDAvis
Other ideas: clustering tree ala http://lazappi.id.au/building-a-clustering-tree/ including uncertainty estimates in Q matrix from admixture
Since structure model is the same thing as LDA (with 'documents' being samples, 'topics' as latent populations, and 'words' as genetic markers) we could use some of the ideas in http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf https://github.com/cpsievert/LDAvis to create some more informative/compelling plots
See also https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation