Valid inference after clustering with application to single-cell RNA-sequencing data Speaker: Lucy Gao, University of British Columbia Zoom link: https://uni-sydney.zoom.us/j/8695451737 Abstract: In single-cell RNA-sequencing studies, researchers often model the variation between cells with a latent variable, such as cell type or pseudotime, and investigate associations between the genes and the latent variable. As the latent variable is unobserved, a two-step procedure seems natural: first estimate the latent variable, then test the genes for association with the estimated latent variable. However, if the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to control the type I error rate. In my talk, I will introduce two different approaches to this problem. First, I will apply ideas from selective inference to develop a valid test for a difference in means between clusters obtained from the hierarchical clustering alg
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