Yakir Reshef
@yakirreshef.bsky.social
Rheumatologist and computer scientist. Fellow at Brigham and Womens Hospital, researcher at @broadinstitute, alum of Harvard MD/PhD program.
9/11 And also!
We used VIMA to link TNF inhibition strongly to loss of lymphoid aggregates in ulcerative colitis (CODEX), to find new patterns of fibroblast organization in rheumatoid arthritis (IHC), and more.
We used VIMA to link TNF inhibition strongly to loss of lymphoid aggregates in ulcerative colitis (CODEX), to find new patterns of fibroblast organization in rheumatoid arthritis (IHC), and more.
February 12, 2025 at 4:21 AM
9/11 And also!
We used VIMA to link TNF inhibition strongly to loss of lymphoid aggregates in ulcerative colitis (CODEX), to find new patterns of fibroblast organization in rheumatoid arthritis (IHC), and more.
We used VIMA to link TNF inhibition strongly to loss of lymphoid aggregates in ulcerative colitis (CODEX), to find new patterns of fibroblast organization in rheumatoid arthritis (IHC), and more.
8/11 For example
In an Alzheimer’s spatial transcriptomics dataset, VIMA separated dementia cases from controls with high accuracy -- and the spatial structures that it found included both known signals and a novel oligodendrocyte-rich cortical layer 6 niche enriched in dementia.
In an Alzheimer’s spatial transcriptomics dataset, VIMA separated dementia cases from controls with high accuracy -- and the spatial structures that it found included both known signals and a novel oligodendrocyte-rich cortical layer 6 niche enriched in dementia.
February 12, 2025 at 4:21 AM
8/11 For example
In an Alzheimer’s spatial transcriptomics dataset, VIMA separated dementia cases from controls with high accuracy -- and the spatial structures that it found included both known signals and a novel oligodendrocyte-rich cortical layer 6 niche enriched in dementia.
In an Alzheimer’s spatial transcriptomics dataset, VIMA separated dementia cases from controls with high accuracy -- and the spatial structures that it found included both known signals and a novel oligodendrocyte-rich cortical layer 6 niche enriched in dementia.
6/11 Simulations
We showed in large-scale simulations that VIMA (blue) can powerfully and accurately identify many different kinds of spatial signals, and that it does much better than simpler alternatives that either don’t use a VAE or don’t use microniches.
We showed in large-scale simulations that VIMA (blue) can powerfully and accurately identify many different kinds of spatial signals, and that it does much better than simpler alternatives that either don’t use a VAE or don’t use microniches.
February 12, 2025 at 4:21 AM
6/11 Simulations
We showed in large-scale simulations that VIMA (blue) can powerfully and accurately identify many different kinds of spatial signals, and that it does much better than simpler alternatives that either don’t use a VAE or don’t use microniches.
We showed in large-scale simulations that VIMA (blue) can powerfully and accurately identify many different kinds of spatial signals, and that it does much better than simpler alternatives that either don’t use a VAE or don’t use microniches.
5/11 How does VIMA work?
- It learns fingerprints via a ResNet18-style conditional VAE that removes sample and batch effects, then builds a nearest-neighbor graph to define microniches (A-C).
- It rigorously tests for case-control associations at global & microniche levels (D-E).
- It learns fingerprints via a ResNet18-style conditional VAE that removes sample and batch effects, then builds a nearest-neighbor graph to define microniches (A-C).
- It rigorously tests for case-control associations at global & microniche levels (D-E).
February 12, 2025 at 4:21 AM
5/11 How does VIMA work?
- It learns fingerprints via a ResNet18-style conditional VAE that removes sample and batch effects, then builds a nearest-neighbor graph to define microniches (A-C).
- It rigorously tests for case-control associations at global & microniche levels (D-E).
- It learns fingerprints via a ResNet18-style conditional VAE that removes sample and batch effects, then builds a nearest-neighbor graph to define microniches (A-C).
- It rigorously tests for case-control associations at global & microniche levels (D-E).