2019
DOI: 10.1101/632208
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High risk glioblastoma cells revealed by machine learning and single cell signaling profiles

Abstract: Recent developments in machine learning implemented dimensionality reduction and clustering tools to classify the cellular composition of patient-derived tissue in multi-dimensional, single cell studies. Current approaches, however, require prior knowledge of either categorical clinical outcomes or cell type identities. These algorithms are not well suited for application in tumor biology, where clinical outcomes can be continuous and censored and cell identities may be novel and plastic. Risk Assessment Popul… Show more

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Cited by 4 publications
(5 citation statements)
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References 70 publications
(117 reference statements)
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“…To identify immune subsets and visualize all cells in a 2D map where position represents local phenotypic similarity, we used two different dimensionality reduction tools depending on the strategy: the viSNE implementation of t-SNE (29) and the UMAP (30). Cells were also grouped in phenotypically homogenous clusters using either SPADE (31) or FlowSOM (32,33). To phenotypically characterize these clusters, Marker Enrichment Modeling (MEM) (34,35) was used.…”
Section: Computational Data Analysismentioning
confidence: 99%
“…To identify immune subsets and visualize all cells in a 2D map where position represents local phenotypic similarity, we used two different dimensionality reduction tools depending on the strategy: the viSNE implementation of t-SNE (29) and the UMAP (30). Cells were also grouped in phenotypically homogenous clusters using either SPADE (31) or FlowSOM (32,33). To phenotypically characterize these clusters, Marker Enrichment Modeling (MEM) (34,35) was used.…”
Section: Computational Data Analysismentioning
confidence: 99%
“…In the validation strategy, Uniform Manifold Approximation and Projection (UMAP) (25) was used to create a single, common map of neutrophils across all samples (18,800 cells x 67 samples) using the 7 markers previously used (Supplemental Table3). Once a common two-dimensional representation of all samples was established, a FlowSOM clustering optimization was applied to determine optimal cluster number based on relative homogeneity of marker expression in clusters (42). The self-organized map generated in an unsupervised way with the optimized FlowSOM algorithm produced clusters that were stable and contained phenotypically homogenous cells (26).…”
Section: Computational Data Analysis Of Neutrophilsmentioning
confidence: 99%
“…Further, multiplexed single-cell proteomic profiles were measured in pediatric diseases and a machine-learning model (regularized elastic-net approach) was used to incorporate cellular developmental aberrations to predict the relapse of leukemias 113 . Similarly, a subset of glioblastoma cells were identified as the response predicter from single-cell signaling profiles without the need for prior information about the expected cell clusters on bioinformatics maps 114 . When combined with spatial multi-omics technologies, bioimaging features (cellular positions, interaction frequencies, tissue structural variations, and subcellular distributions) from single-cell analysis and molecular imaging will continue impacting predictive outcome studies in precision medicine.…”
Section: Precision Medicine By Spatial Multi-omics and Artificial Int...mentioning
confidence: 99%