2 research outputs found
Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>‑SNE of Mass Spectrometry Imaging Data
Mass
spectrometry imaging (MSI), which quantifies the underlying
chemistry with molecular spatial information in tissue, represents
an emerging tool for the functional exploration of pathological progression.
Unsupervised machine learning of MSI datasets usually gives an overall
interpretation of the metabolic features derived from the abundant
ions. However, the features related to the latent lesions are always
concealed by the abundant ion features, which hinders precise delineation
of the lesions. Herein, we report a data-driven MSI data segmentation
approach for recognizing the hidden lesions in the heterogeneous tissue
without prior knowledge, which utilizes one-step prediction for feature
selection to generate function-specific segmentation maps of the tissue.
The performance and robustness of this approach are demonstrated on
the MSI datasets of the ischemic rat brain tissues and the human glioma
tissue, both possessing different structural complexity and metabolic
heterogeneity. Application of the approach to the MSI datasets of
the ischemic rat brain tissues reveals the location of the ischemic
penumbra, a hidden zone between the ischemic core and the healthy
tissue, and instantly discovers the metabolic signatures related to
the penumbra. In view of the precise demarcation of latent lesions
and the screening of lesion-specific metabolic signatures in tissues,
this approach has great potential for in-depth exploration of the
metabolic organization of complex tissue
Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>‑SNE of Mass Spectrometry Imaging Data
Mass
spectrometry imaging (MSI), which quantifies the underlying
chemistry with molecular spatial information in tissue, represents
an emerging tool for the functional exploration of pathological progression.
Unsupervised machine learning of MSI datasets usually gives an overall
interpretation of the metabolic features derived from the abundant
ions. However, the features related to the latent lesions are always
concealed by the abundant ion features, which hinders precise delineation
of the lesions. Herein, we report a data-driven MSI data segmentation
approach for recognizing the hidden lesions in the heterogeneous tissue
without prior knowledge, which utilizes one-step prediction for feature
selection to generate function-specific segmentation maps of the tissue.
The performance and robustness of this approach are demonstrated on
the MSI datasets of the ischemic rat brain tissues and the human glioma
tissue, both possessing different structural complexity and metabolic
heterogeneity. Application of the approach to the MSI datasets of
the ischemic rat brain tissues reveals the location of the ischemic
penumbra, a hidden zone between the ischemic core and the healthy
tissue, and instantly discovers the metabolic signatures related to
the penumbra. In view of the precise demarcation of latent lesions
and the screening of lesion-specific metabolic signatures in tissues,
this approach has great potential for in-depth exploration of the
metabolic organization of complex tissue