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 (e.g. drug effects). 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 (such as the Wasserstein distance) 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 (1) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal (2) less domain-specific information is present.
 This contribution deal with nuisance factors afflicting biological cell images with a domain adaptation approach: the embedding vectors generated from cell images show spurious correlation. The authors define a Wasserstein Distance Network to find a suitable affine transformation that reduces the nuisance factor. The evaluation on a real dataset yields correct results, this approach is quite general and could be applied to different problems. The contribution of this approach could be better highlighted. The early stopping criteria tend to favor suboptimal solution, indeed relying on the Cramer distance is possible improvement. As a side note, the k-NN MOA is central to for the evaluation of the proposed approach. A possible improvement is to try other means for the embedding instead of the Euclidean one.
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### Title + Abstract
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 (e.g. drug effects). 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 (such as the Wasserstein distance) 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 (1) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal (2) less domain-specific information is present.
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<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This contribution deal with nuisance factors afflicting biological cell images with a domain adaptation approach: the embedding vectors generated from cell images show spurious correlation. The authors define a Wasserstein Distance Network to find a suitable affine transformation that reduces the nuisance factor. The evaluation on a real dataset yields correct results, this approach is quite general and could be applied to different problems. The contribution of this approach could be better highlighted. The early stopping criteria tend to favor suboptimal solution, indeed relying on the Cramer distance is possible improvement. As a side note, the k-NN MOA is central to for the evaluation of the proposed approach. A possible improvement is to try other means for the embedding instead of the Euclidean one.
assistant
 This contribution deal with nuisance factors afflicting biological cell images with a domain adaptation approach: the embedding vectors generated from cell images show spurious correlation. The authors define a Wasserstein Distance Network to find a suitable affine transformation that reduces the nuisance factor. The evaluation on a real dataset yields correct results, this approach is quite general and could be applied to different problems. The contribution of this approach could be better highlighted. The early stopping criteria tend to favor suboptimal solution, indeed relying on the Cramer distance is possible improvement. As a side note, the k-NN MOA is central to for the evaluation of the proposed approach. A possible improvement is to try other means for the embedding instead of the Euclidean one.