Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation
Abstract Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expression in unmeasured regions and tissues can enhance biologists’ hi...
Main Authors: | Taku Monjo, Masaru Koido, Satoi Nagasawa, Yutaka Suzuki, Yoichiro Kamatani |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-03-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-07685-4 |
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