@MHemingNeuro@ScienceScottT@tangming2005@hoffman_steven Also, we have more information about using count splitting more picking the number of clusters in our new preprint, which expands the original count splitting paper to the negative binomial setting: https://t.co/3GzB6obzZC
@MHemingNeuro@ScienceScottT@tangming2005@hoffman_steven Yes! We have one in the data thin package! I also just added one to the count split tutorials package on simulated data! I am working on updating the "real data" Seurat/Monocle/etc. tutorials to include more on model evaluation! https://t.co/mNcx6mYHDZ
Best part of being a @UWBiostat & @UWStat student is learning from great Profs.
Best part of being a Prof is learning from great students!
Thanks @AnnaCNeufeld (@daniela_witten lab) for presenting your work on data thinning to our group!
@DarwinAwdWinner@daniela_witten@LucyGao We have been exploring this in the context of the negative binomial distribution in an upcoming paper about analyzing scRNA-seq data! We found surprisingly good performance with estimated overdispersion parameters, but there is definitely more to explore!
@daniela_witten, @lucygao, Ameer Dharamshi, and I are excited to share our new preprint (https://t.co/jCvwACAGdo). We introduce *data thinning*, a flexible framework that splits a single observation into independent parts, providing an alternative to cross-validation. (1/11)
@DarwinAwdWinner@daniela_witten@LucyGao Great question! This is addressed in our previous paper, which was all about Poisson data thinning for scRNA-seq data! https://t.co/gj71ETyHw5
Data thinning is broadly applicable: any time you might perform sample splitting or cross-validation, you can use data thinning instead. And you can use data thinning in settings where sample splitting and cross-validation are NOT applicable (e.g. unsupervised learning). (10/11)
Congrats to @AnnaCNeufeld for her first dissertation paper (with @lucylgao) being published in JMLR, and for winning the Birnbaum Award from @UWStat for an excellent general exam!! 🥳 🎉 👏
https://t.co/tv06I5BJ0I