TissueMosaic enables cross-sample differential analysis of spatial transcriptomics datasets through self-supervised representation learning Article Swipe
Sandeep Kambhampati
,
Luca D’Alessio
,
Fedor Grab
,
Stephen J. Fleming
,
Fei Chen
,
Mehrtash Babadi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.11.07.622479
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.11.07.622479
Spatial transcriptomics allows for the measurement of gene expression within native tissue context, thereby improving our understanding of how cell states are modulated by their microenvironment. Despite technological advancements, computational methods to link cell states with their microenvironment and perform comparative analysis across different samples and conditions are still underdeveloped. To address this, we introduce TissueMosaic ( Tissue MOtif-based SpAtial Inference across Conditions ), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets ( https://github.com/broadinstitute/TissueMosaic ). TissueMosaic effectively maps structurally similar tissue motifs close together in a learned latent space. TissueMosaic further links these motifs to gene expression, enabling the study of how changes in tissue structure impact function. TissueMosaic increases the signal-to-noise ratio of differential expression analysis through a motif enrichment strategy, resulting in more reliable detection of genes that covary with tissue structure. Here, we demonstrate TissueMosaic on high resolution spatial transcriptomics datasets across tissues, learning representations that outperform neighborhood cell-type composition baselines and existing methods on downstream tasks. We highlight genes and pathways in these tissues that are associated with changes in tissue structure across external conditions. These findings underscore the potential of self-supervised learning to significantly advance spatial transcriptomics research.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.11.07.622479
- https://www.biorxiv.org/content/biorxiv/early/2024/11/09/2024.11.07.622479.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404204966
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https://openalex.org/W4404204966Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.11.07.622479Digital Object Identifier
- Title
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TissueMosaic enables cross-sample differential analysis of spatial transcriptomics datasets through self-supervised representation learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-09Full publication date if available
- Authors
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Sandeep Kambhampati, Luca D’Alessio, Fedor Grab, Stephen J. Fleming, Fei Chen, Mehrtash BabadiList of authors in order
- Landing page
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https://doi.org/10.1101/2024.11.07.622479Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/11/09/2024.11.07.622479.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/11/09/2024.11.07.622479.full.pdfDirect OA link when available
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Representation (politics), Sample (material), Computer science, Pattern recognition (psychology), Differential (mechanical device), Artificial intelligence, Feature learning, Machine learning, Engineering, Chemistry, Chromatography, Aerospace engineering, Law, Political science, PoliticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3023215041, https://openalex.org/W3120430728, https://openalex.org/W3112563877, https://openalex.org/W4229033508, https://openalex.org/W2471536144, https://openalex.org/W4389670531, https://openalex.org/W3129476455, https://openalex.org/W3023371261, https://openalex.org/W3159481202, https://openalex.org/W2046275054, https://openalex.org/W2341978434, https://openalex.org/W1977066411, https://openalex.org/W4226192560, https://openalex.org/W3108118546, https://openalex.org/W4390841990, https://openalex.org/W2890218282, https://openalex.org/W2130410032, https://openalex.org/W3211114294, https://openalex.org/W2024903234, https://openalex.org/W4385828130, https://openalex.org/W2560609797, https://openalex.org/W2624503621, https://openalex.org/W2194775991, https://openalex.org/W2099001231, https://openalex.org/W3035965352, https://openalex.org/W2889326414, https://openalex.org/W2897249806, https://openalex.org/W4386463918, https://openalex.org/W4386707745, https://openalex.org/W3106188259, https://openalex.org/W3099878876 |
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