All You Need is Color: Image based Spatial Gene Expression Prediction
using Neural Stain Learning
- URL: http://arxiv.org/abs/2108.10446v2
- Date: Thu, 26 Aug 2021 10:45:21 GMT
- Title: All You Need is Color: Image based Spatial Gene Expression Prediction
using Neural Stain Learning
- Authors: Muhammad Dawood, Kim Branson, Nasir M. Rajpoot, Fayyaz ul Amir Afsar
Minhas
- Abstract summary: We propose a "stain-aware" machine learning approach for prediction of spatial transcriptomic gene expression profiles.
We have found that the gene expression predictions from the proposed approach show higher correlations with true expression values obtained through sequencing.
- Score: 11.9045433112067
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: "Is it possible to predict expression levels of different genes at a given
spatial location in the routine histology image of a tumor section by modeling
its stain absorption characteristics?" In this work, we propose a "stain-aware"
machine learning approach for prediction of spatial transcriptomic gene
expression profiles using digital pathology image of a routine Hematoxylin &
Eosin (H&E) histology section. Unlike recent deep learning methods which are
used for gene expression prediction, our proposed approach termed Neural Stain
Learning (NSL) explicitly models the association of stain absorption
characteristics of the tissue with gene expression patterns in spatial
transcriptomics by learning a problem-specific stain deconvolution matrix in an
end-to-end manner. The proposed method with only 11 trainable weight parameters
outperforms both classical regression models with cellular composition and
morphological features as well as deep learning methods. We have found that the
gene expression predictions from the proposed approach show higher correlations
with true expression values obtained through sequencing for a larger set of
genes in comparison to other approaches.
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