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Michelle D. Tadmor

Researcher at Columbia University

Publications -  7
Citations -  4693

Michelle D. Tadmor is an academic researcher from Columbia University. The author has contributed to research in topics: Population & Engineering. The author has an hindex of 6, co-authored 6 publications receiving 3768 citations.

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Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

TL;DR: Using hematopoietic progenitors, a signaling-based measure of cellular phenotype was defined, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts, yielding insights into AML pathophysiology.
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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

TL;DR: In this article, the authors present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the highdimensional structure of the data by using all pairwise distances in high dimension to determine each cell's location in the plot.
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Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

TL;DR: This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.
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Wishbone identifies bifurcating developmental trajectories from single-cell data.

TL;DR: Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution, is presented and it is shown that it outperforms diffusion maps, SCUBA and Monocle both in the accuracy of ordering cells and in the correct identification of branch points.
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Trajectories of cell-cycle progression from fixed cell populations

TL;DR: Cycler is presented, a robust method that constructs a continuous trajectory of cell-cycle progression from images of fixed cells that handles heterogeneous microenvironments and does not require perturbations or genetic markers, making it generally applicable to quantifying multiple sources ofcell-to-cell variability in mammalian cells.