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SUMMARY:Single-cell RNA-sequencing analysis without double-dipping
DTSTART:20231130T151500
DTEND:20231130T163000
DTSTAMP:20260507T133836Z
UID:8d3d43e2a1c0d87b2b265880ffc3dcd5042444a861b281bf2a0787bf
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Daniela M. Witten\nDorothy Gilford Endowed Chair\nUniver
 sity of Washington\nWhen analyzing single-cell RNA-sequencing data\, we of
 ten wish to learn some latent structure among the cells\, and then validat
 e this structure on the same set of cells. For example\, we might cluster 
 the cells into cell types\, and then test whether gene expression differs 
 between the clusters. Or we might estimate a low-dimensional subspace repr
 esenting a cellular developmental trajectory\, and then test whether gene 
 expression is correlated with this trajectory. However\, a classical stati
 stical test to validate the latent structure will not control the Type 1 e
 rror\, since the latent structure was estimated on the same data used for 
 hypothesis testing. Furthermore\, a straightforward sample splitting app
 roach does not fix the problem.\n\nIn this talk\, I will present "count s
 plitting"\, a simple variant of sample splitting that does control the T
 ype 1 error. The idea is simple but powerful: rather than splitting the n 
 cells in the data matrix into a separate training set of m<n cells and a t
 est set of n-m cells\, we instead split the n cells into a training set of
  n cells and a test set of n cells\, in a very particular way such that th
 e training and test sets are independent and follow the same distribution 
 as the original n cells. This allows us to\, for instance\, define cell ty
 pes on the training cells and validate them on the test cells\, without th
 e pitfalls that arise due to double dipping. \n\nThis is joint work with 
 PhD alumni Anna Neufeld (now at Fred Hutch) and Lucy Gao (now at U. Britis
 h Columbia) and collaborators Jacob Bien (USC)\, Alexis Battle\, Joshua Po
 pp (Johns Hopkins).
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717 https://epfl.zoom.u
 s/j/66776552543?pwd=UVU5cDdLK05MZzgxSjVBczVzdDZ5QT09
STATUS:CONFIRMED
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