Figure 1.
Overview of the computational pipeline for the disease cohort analysis of MPNs. We characterized the megakaryocyte morphology and topography of each BMT sample. To effectively build an annotated library of megakaryocytes, assisted annotation tools for identification (A) and delineation (B) have been developed. (C) A library of 62 479 annotated megakaryocytes from reactive and MPN samples was generated, of which 37 284 have been validated by a hematopathologist. (D) Clustering analysis performed on the library of megakaryocytes identified candidate phenotypes. (E) The phenotypic and topographical profile of megakaryocytes was extracted and used to create abstract representations of each trephine sample. (F) Based on these abstract representations, the analyzed samples can be represented in 2-dimensional space with new samples indexed to annotated disease cohorts.

Overview of the computational pipeline for the disease cohort analysis of MPNs. We characterized the megakaryocyte morphology and topography of each BMT sample. To effectively build an annotated library of megakaryocytes, assisted annotation tools for identification (A) and delineation (B) have been developed. (C) A library of 62 479 annotated megakaryocytes from reactive and MPN samples was generated, of which 37 284 have been validated by a hematopathologist. (D) Clustering analysis performed on the library of megakaryocytes identified candidate phenotypes. (E) The phenotypic and topographical profile of megakaryocytes was extracted and used to create abstract representations of each trephine sample. (F) Based on these abstract representations, the analyzed samples can be represented in 2-dimensional space with new samples indexed to annotated disease cohorts.

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