Correcting Nuisance Variation using Wasserstein Distance
Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drugs applied at different dosages can be quantified.The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images.An important known issue for such methods is separating relevant biological signal from nuisance variation.For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for cells from a different week, despite having identical drug compounds applied in both cases.In this case, the particular batch a set of experiments were conducted in constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information.We develop a general framework for adjusting the image embeddings in order to `forget domain-specific information while preserving relevant biological information.To do this, we minimize a loss function based on distances between marginal distributions of embeddings across domains for each replicated treatment.For the dataset presented, the replicated treatment is the negative control.We find that for our transformed embeddings the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal less domain-specific information is present.
We correct nuisance variation for image embeddings across different domains, preserving only relevant information.Discusses a method for adjusting image embeddings in order tease apart technical variation from biological signal.The authors present a method to remove domain-specific information while preserving the relevant biological information by training a network that minimizes the Wasserstein distance between distrbutions.