We developed a dedicated pipeline for mibi data: github.com/angelolab/to..., but for other data modalities I’m not as familiar what people generally do. Right now there isn’t a good cross platform solution for data normalization, at least not that we’ve found

Great point. We spent a lot of effort addressing batch effects earlier in our processing pipeline so that SpaceCat wouldn't have to deal with them. In general, I would say the earlier you can address your batch correction issues, the better, but there aren't as many options for spatial data

Thanks Kieran!

If you run into any problems getting the codebase to work, have questions about what we found, or want to chat, please don’t hesitate to reach out (/end) bsky.app/profile/noah...

This wouldn’t have been possible without an amazing team (most of whom have not migrated over to the good place yet!), including Iris, Cami, Seongyeol, Manon, as well as Christina, Marleen and Mike (9/x)

This was just a sampling of what we found; for the full details, please check out the paper, as well as our github, where we’ve made all the underlying code open source and available (8/x)
github.com/angelolab/Sp...

Finally, to look at how these features could be combined together, as well as to compare modalities, we built multivariate models to predict outcome from each data type at each timepoint. We found large differences across both assay types and sample timepoints! (7/x)

When we looked at the specific features we defined, we found some that were temporally dependent, with good predictive power at one timepoint but poor predictive power at another timepoint (6/x)

We then tested which of the 800+ features from SpaceCat could predict response to immunotherapy, finding numerous strong predictors. Interestingly, features defined in specific regions of the tumor did an especially good job at predicting outcome (5/x)

To help us make sense of this spatially-resolved data, we built SpaceCat, an algorithm to quantify and summarize the key features from spatial datasets. SpaceCat can be applied to processed imaging data from any multiplexed imaging platform! (4/x)

We then generated highly multiplexed imaging data using an antibody panel of 37 antibodies. This allowed us to identify 22 cell types across the more than 650 TMA cores we imaged from 117 total patients (3/x)

Our awesome collaborators at NKI put together a unique cohort spanning primary disease, pre-treatment metastases, and on-treatment metastases from triple negative breast cancer patients enrolled in the TONIC clinical trial (2/x)

Hi Erik, I work on tissue imaging, spatial biology, and cancer research. Could you please add me to the feed? Thanks!https://scholar.google.com/citations?user=ajvnimEAAAAJ&hl=en