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Robust Algorithm With Contrastive Learning for Identifying Spatial Domains From Noised Spatial Transcriptomics Data
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DOI：10.1109/TCBBIO.2026.3683521.png)
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Abstract
En 中文
Spatial transcriptomics (ST) technologies capture transcriptomics of genens with spatial context, enabling systematic exploration of micro-environment of tissues that is highly associated with spatial domains. And, noise of ST data poses a great challenge on designing algorithms for identifying spatial domains, whereas available methods remove noise by employing pre-processing procedure, resulting in the undesirable performance. To overcome this limitation, we propose a robust and joint framework, called jNFACL (<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</u>oint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u>etwork-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u>eature–<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>ffinity <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>ontrastive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u>earning), for identifying spatial domains of noised ST data, where deniosing of ST data and identifying spatial domains are simultaneously integrated. Specifically, jNFACL first constructs expression and spatial graphs with transcriptomics and spatial coordinates of spots, which removes heterogeneity of ST data. And, jNFACL separates noise of ST data by jointly projecting these constructed graphs into the shared subspace, where noise of ST data is separated from feature level with nonnegative matrix factorization. To further enhance quality of features of spots, contrastive learning is adopted to leverages spatial neighborhoods by pulling similar spots together and pushing dissimilar spots apart, where self-supervision information is incorporated, thereby improving the characterization and identification of spatial domains. Experiments on various datasets with different noise levels from multiple platforms and species demonstrate that jNFACL is much more accurate and robust than state-of-the-art methods, providing alternatives for analyzing noised ST data.
Keywords:
Spatial transcriptomic
spatial domain
non-negative matrix factorization
contrastive learning
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