Quantitative Issues in Cancer Research Working Group Seminar

Phillip Nicol, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Estimation in Poisson log-bilinear models

Abstract: The Poisson log-bilinear model, also known as GLM-PCA, is a commonly used approach for dimension reduction in single-cell RNA-seq data. Model parameters are usually estimated via maximum likelihood. However, we show that the MLE can be undefined for some realistic single-cell datasets. In this talk, we show how this issue can be resolved by adding appropriate priors to the model parameters. Importantly, the prior information can be incorporated with minor adjustments to existing estimation algorithm. We demonstrate the approach on real and simulated single cell data and discuss extensions to spatial transcriptomics.