Proximogram-A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling.
1/5 보강
The increasing availability of patient-derived multimodal biological data for various diseases has opened up avenues for finding the optimal methods for jointly leveraging the information extracted in
APA
Krishnan SN, Ji S, et al. (2024). Proximogram-A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling.. Computers in biology and medicine, 182, 109082. https://doi.org/10.1016/j.compbiomed.2024.109082
MLA
Krishnan SN, et al.. "Proximogram-A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling.." Computers in biology and medicine, vol. 182, 2024, pp. 109082.
PMID
39255657 ↗
Abstract 한글 요약
The increasing availability of patient-derived multimodal biological data for various diseases has opened up avenues for finding the optimal methods for jointly leveraging the information extracted in a customizable and scalable manner. Here, we propose the Proximogram, a graph-based representation that provides a joint construct for embedding independently obtained omics and spatial data. To evaluate the representation, we generated proximograms from 2 distinct biological sources, namely, multiplexed immunofluorescence images and single-cell RNA-seq data obtained from patients across two pancreatic diseases that include normal and chronic Pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC). The generated proximograms were used as inputs to 2 distinct graph deep-learning models. The improved classification results over simpler spatial-data-based input graphs point to the increased discriminatory power obtained by integrating structural information from single-cell ligand-receptor signaling data and the spatial architecture of cells in each disease class, which can help point to markers of high diagnostic significance.
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