Hajihosseini, M.; Amini, P.; Voicu, D.; Dinu, I.; Pyne, S. Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape. Cancers2022, 14, 5235.
Hajihosseini, M.; Amini, P.; Voicu, D.; Dinu, I.; Pyne, S. Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape. Cancers 2022, 14, 5235.
Hajihosseini, M.; Amini, P.; Voicu, D.; Dinu, I.; Pyne, S. Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape. Cancers2022, 14, 5235.
Hajihosseini, M.; Amini, P.; Voicu, D.; Dinu, I.; Pyne, S. Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape. Cancers 2022, 14, 5235.
Abstract
Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
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