Inference after latent variable estimation for single-cell RNA sequencing data

Alexis Battle Co-Author
Johns Hopkins University
Joshua Popp Co-Author
Johns Hopkins University
Daniela Witten Co-Author
University of Washington
Anna Neufeld Co-Author
Lucy Gao Speaker
Wednesday, Aug 9: 8:55 AM - 9:15 AM
Topic-Contributed Paper Session 
Metro Toronto Convention Centre 
In the analysis of single-cell RNA sequencing data, researchers often first characterize the variation between cells by estimating a latent variable, representing some aspect of the individual cell state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values and confidence intervals in the second step will fail to achieve statistical guarantees such as Type 1 error control or nominal coverage. Furthermore, approaches such as sample splitting that can be fruitfully applied to solve similar problems in other settings are not applicable in this context. We introduce count splitting, an extremely flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study, and apply count splitting to a dataset of pluripotent stem cells differentiating to cardiomyocytes.