Author

K. Felt

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Assay:spatial transcriptomics· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Organism:human· 1

Frequent Co-Authors

A. Yeaton· 1 shared B. A. Goods· 1 shared B. Jiang· 1 shared B. M. Titchen· 1 shared B. Sharma· 1 shared C. Gao· 1 shared C. Labaki· 1 shared D. A. Braun· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperCancer Biology

A. Yeaton

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2 papers

Recent·Most cited

Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

H. Wang, R. Huang, J. Nelson, C. Gao, M. Tran, A. Yeaton, K. Felt, K. L. Pfaff, T. Bowman, S. J. Rodig, K. Wei, B. A. Goods, S. L. Farhi

This study benchmarks three imaging spatial transcriptomics platforms on FFPE tissues, finding 10x Xenium has higher transcript counts without sacrificing specificity, while all platforms align with RNA-seq data but vary in performance metrics.

bioRxiv · Dec 7, 2023Cancer Biology

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

J. Nyman, T. Denize, Z. Bakouny, C. Labaki, B. M. Titchen, K. Bi, S. N. Hari, J. Rosenthal, N. Mehta, B. Jiang, B. Sharma, K. Felt, R. Umeton, D. A. Braun, S. Rodig, , E. M. Van Allen

The paper uses spatially aware deep learning to identify tumor heterogeneity patterns in kidney cancer, revealing distinct states associated with different histological subtypes.

bioRxiv · Jan 18, 2023Cancer Biology