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DESCRIPTION:Single-cell RNA-sequencing data analysis without double-dipping
 \n\nDaniela Witten\, University of Wahsington\n	Tuesday January 10\, 12-1pm
 \n	Zoom Link: https://mcgill.zoom.us/j/86855481591\n\nAbstract: When analyz
 ing single-cell RNA-sequencing data\, we often wish to perform unsupervise
 d learning of latent structure among the cells\, and then to test for asso
 ciation between this latent structure and gene expression. For example\, w
 e might cluster the cells into cell types\, and then test whether gene exp
 ression differs between the clusters. Or we might estimate a low-dimension
 al subspace representing a cellular developmental trajectory\, and then te
 st whether gene expression is correlated with this trajectory. However\, a
  classical statistical test of the association between gene expression and
  the latent structure will not control the Type 1 error\, since the latent
  structure was estimated on the same data used for hypothesis testing. Fur
 thermore\, a straightforward sample splitting approach does not fix the pr
 oblem.\n\nIn this talk\, I will discuss two solutions to this problem. The
  first involves selective inference\, and the second involves 'count split
 ting'\, a simple variant of sample splitting that does control the Type 1 
 error.\n\nThis is joint work with PhD student Anna Neufeld\, PhD alumni Lu
 cy Gao (now at U. British Columbia) and Yiqun Chen (now at Stanford)\, and
  collaborators Jacob Bien (USC) and Alexis Battle and Joshua Popp (Johns H
 opkins).\n
DTSTART:20230110T170000Z
DTEND:20230110T180000Z
SUMMARY:QLS Seminar Series -Daniela Witten
URL:https://www.mcgill.ca/qls/channels/event/qls-seminar-series-daniela-wit
 ten-344255
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