Correcting Nuisance Variation using Wasserstein Distance

11/02/2017
by   Gil Tabak, et al.
0

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 (e.g. drug effects). We develop a method 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 the Wasserstein distance. We find for our transformed embeddings (1) the underlying geometric structure is preserved and (2) less domain-specific information is present.

READ FULL TEXT

page 4

page 8

research
11/15/2018

Adjusting for Confounding in Unsupervised Latent Representations of Images

Biological imaging data are often partially confounded or contain unwant...
research
12/21/2020

BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information

Traditional biomedical version of embeddings obtained from pre-trained l...
research
05/10/2021

DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learning

Drug repurposing can accelerate the identification of effective compound...
research
06/15/2020

Improved Conditional Flow Models for Molecule to Image Synthesis

In this paper, we aim to synthesize cell microscopy images under differe...
research
08/15/2017

GANs for Biological Image Synthesis

In this paper, we propose a novel application of Generative Adversarial ...
research
10/20/2017

Learning Wasserstein Embeddings

The Wasserstein distance received a lot of attention recently in the com...
research
12/06/2021

Anchoring to Exemplars for Training Mixture-of-Expert Cell Embeddings

Analyzing the morphology of cells in microscopy images can provide insig...

Please sign up or login with your details

Forgot password? Click here to reset