Measurement noise scaling laws for cellular representation learning

Abstract: Deep learning scaling laws predict how performance improves with increased model and dataset size. Here we identify measurement noise in data as another performance scaling axis, governed by a distinct logarithmic law. We focus on representation learning models of biological single cell genomic data, where a dominant source of measurement noise is due to molecular undersampling. We introduce an information-theoretic metric for cellular representation model quality, and find that it scales with sampling depth. A single quantitative relationship holds across several model types and across several datasets. We show that the analytical form of this relationship can be derived from a simple Gaussian noise model, which in turn provides an intuitive interpretation for the scaling law. Finally, we show that the same relationship emerges in image classification models with respect to two types of imaging noise, suggesting that measurement noise scaling may be a general phenomenon. Scaling with noise can serve as a guide in generating and curating data for deep learning models, particularly in fields where measurement quality can vary dramatically between datasets.