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Informing biologically relevant signal from spatial transcriptomic data

Radkevich, E.; D'Souza, D.; Mattiuz, R.; Merand, R.; Chen, R.; Morgenroth-Rebin, J.; Angeliadis, K.; Nelson, D.; Kara, A.; Dawson, T.; De Souza, I.; Nie, K.; Hamon, P.; Hegde, S.; Boumelha, J.; Brody, R.; Ozbey, S.; Hennequin, C.; Feng, D.; Dai, J.; Gonzalez-Kozlova, E.; Chen, Z.; Kim-Schulze, S.; Gnjatic, S.; Merad, M.; Roudko, V.

2024-09-13 bioinformatics
10.1101/2024.09.09.610361 bioRxiv
Show abstract

Visium is a spatial sequencing technology that utilizes messenger RNA (mRNA) to spatially map gene expression within tissues. Despite its potential, research utilizing deconvolution tools and exploring microenvironment dynamics remains challenging. We address this gap by benchmarking deconvolution tools across diverse biological contexts, identifying optimal methodologies. Subsequently, we introduce a novel pipeline integrating advanced deconvolution techniques and novel tools for reproducible tissue microenvironment analysis. Through this approach, we uncover intricate immune aggregate biology, highlighting the power of our methodology in unraveling complex biological phenomena.

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The top 7 journals account for 50% of the predicted probability mass.

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18
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1.3%
20
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21
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25
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Top 3%
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0.6%
27
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28
Cell Genomics
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.