Dear Dr Singh,

We are pleased to inform you that your manuscript 'Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning' has been provisionally accepted for publication in PLOS Computational Biology.

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Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Dear Authors,

In my eyes, you have successfully addressed all my comments. I do believe that the manuscript gained a lot in clarity and openness. And as such will be useful to a wider audience. Again, it might be interesting to see how the model would perform on the JUMP-CP CRISPR data for retrieval of functionally related genes. Yet, this is even more challenging as cell-to-cell heterogeneity plays a larger role. However, this might be done in another manuscript.

Best,

Reviewer 3

Reviewer #2: The authors have addressed my concerns. In my opinion, the manuscript is ready for publication and will be a nice contribution to the field.

There is one last issue that I recommend but should be decided whether to include by the authors. Fig. K1 in the Supplementary Material shows an “application appropriate” measurement (PMID: 37433995) in the number of predictable MoAs as a function of an increasing mAP threshold. In my opinion this is the direct measurement that matters the most in the context of MoA prediction. Thus I highly recommend the following: (1) move it to the main text, (2) calculate the improvement in percentage according to this measurement (and report their range in the abstract instead of the mPA %), (3) include a direct comparison to the contribution of contrastive learning alone, shown in Table 1 “mAP improvement (population average)” (nice to have).

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes: Alon Shpigler and Assaf Zaritsky