BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250626T203027EDT-6682XCEvLA@132.216.98.100 DTSTAMP:20250627T003027Z 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:聽https://mcgill.zoom.us/j/86855481591\n\nAbstract:聽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:/qls/channels/event/qls-seminar-series-daniela-wit ten-344255 END:VEVENT END:VCALENDAR