Utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.
1 . A computer-implemented method comprising:
receiving, from a client device, a similarity request for two or more cell perturbations;
generating similarity measures between the two or more cell perturbations by comparing aggregated embeddings corresponding to the two or more cell perturbations, wherein the aggregated embeddings are aggregated from perturbation experiment unit embeddings generated utilizing a machine learning model from perturbation data; and
in response to the similarity request, providing, for display via the client device, a perturbation similarity map reflecting the similarity measures between the two or more cell perturbations.
2 . The computer-implemented method of claim 1 , wherein receiving, from the client device, the similarity request for two or more cell perturbations comprises:
receiving a compound-gene similarity request between a compound perturbation and a gene perturbation; or
receiving a compound-compound similarity request between a first compound perturbation and a second compound perturbation.
3 . The computer-implemented method of claim 1 , wherein generating the similarity measures comprises comparing a first aggregated embedding for a first perturbation and a second aggregated embedding for a second perturbation in a machine learning feature space to generate a similarity measure.
4 . The computer-implemented method of claim 1 , further comprising generating the perturbation experiment unit embeddings utilizing the machine learning model from the perturbation data by:
capturing phenomic images of cells exposed to perturbations; and
generating, utilizing the machine learning model, perturbation experiment unit embeddings from the phenomic images of the cells.
5 . The computer-implemented method of claim 1 , further comprising generating the perturbation experiment unit embeddings utilizing the machine learning model from the perturbation data by:
generating transcriptomic profiles of cells exposed to perturbations; and
generating the perturbation experiment unit embeddings from the transcriptomic profiles.
6 . The computer-implemented method of claim 1 , wherein providing the perturbation similarity map reflecting the similarity measures between the two or more cell perturbations for display comprises providing, for display via the client device, a heatmap comprising cells having shading that represents the similarity measures.
7 . The computer-implemented method of claim 1 , wherein providing the perturbation similarity map further comprises providing, for display via the client device, a confidence measure for one or more of the aggregated embeddings.
8 . A system comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
receive, from a client device, a similarity request for two or more cell perturbations;
generate similarity measures between the two or more cell perturbations by comparing aggregated embeddings corresponding to the two or more cell perturbations, wherein the aggregated embeddings are aggregated from perturbation experiment unit embeddings generated utilizing a machine learning model from perturbation data; and
in response to the similarity request, provide, for display via the client device, a perturbation similarity map reflecting the similarity measures between the two or more cell perturbations.
9 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
receive a compound-gene similarity request between a compound perturbation and a gene perturbation;
receive a compound-compound similarity request between a first compound perturbation and a second compound perturbation; and
provide, for display via the client device, the perturbation similarity map reflecting a first similarity measure between the compound perturbation and the gene perturbation and a second similarity measure between the first compound perturbation and the second compound perturbation.
10 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the similarity measures by comparing a first aggregated embedding for a first perturbation and a second aggregated embedding for a second perturbation in a machine learning feature space to generate a similarity measure.
11 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the perturbation experiment unit embeddings utilizing the machine learning model from the perturbation data by:
capturing phenomic images of cells exposed to perturbations; and
generating, utilizing the machine learning model, perturbation experiment unit embeddings from the phenomic images of the cells.
12 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the perturbation experiment unit embeddings utilizing the machine learning model from the perturbation data by:
generating transcriptomic profiles of cells exposed to perturbations; and
generating the perturbation experiment unit embeddings from the transcriptomic profiles.
13 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the perturbation similarity map reflecting the similarity measures between the two or more cell perturbations for display by providing, for display via the client device, a heatmap comprising cells having shading that represents the similarity measures.
14 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the perturbation similarity map by providing, for display via the client device, a confidence measure for one or more of the aggregated embeddings.
15 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:
receive, from a client device, a similarity request for two or more cell perturbations;
generate similarity measures between the two or more cell perturbations by comparing aggregated embeddings corresponding to the two or more cell perturbations, wherein the aggregated embeddings are aggregated from perturbation experiment unit embeddings generated utilizing a machine learning model from perturbation data; and
in response to the similarity request, provide, for display via the client device, a perturbation similarity map reflecting the similarity measures between the two or more cell perturbations.
16 . The non-transitory computer-readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
receive a compound-gene similarity request between a compound perturbation and a gene perturbation;
receive a compound-compound similarity request between a first compound perturbation and a second compound perturbation; and
provide, for display via the client device, the perturbation similarity map reflecting a first similarity measure between the compound perturbation and the gene perturbation and a second similarity measure between the first compound perturbation and the second compound perturbation.
17 . The non-transitory computer-readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the similarity measures by comparing a first aggregated embedding for a first perturbation and a second aggregated embedding for a second perturbation in a machine learning feature space to generate a similarity measure.
18 . The non-transitory computer-readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the perturbation experiment unit embeddings utilizing the machine learning model from the perturbation data by:
capturing phenomic images of cells exposed to perturbations; and
generating, utilizing the machine learning model, perturbation experiment unit embeddings from the phenomic images of the cells.
19 . The non-transitory computer-readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to provide the perturbation similarity map reflecting the similarity measures between the two or more cell perturbations for display by providing, for display via the client device, a heatmap comprising cells having shading that represents the similarity measures.
20 . The non-transitory computer-readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to provide the perturbation similarity map by providing, for display via the client device, a confidence measure for one or more of the aggregated embeddings.