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  1. AU="Vasilis Ntranos"
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  1. Article ; Online: Determining sequencing depth in a single-cell RNA-seq experiment

    Martin Jinye Zhang / Vasilis Ntranos / David Tse

    Nature Communications, Vol 11, Iss 1, Pp 1-

    2020  Volume 11

    Abstract: For single-cell RNA-seq experiments the sequencing budget is limited, and how it should be optimally allocated to maximize information is not clear. Here the authors develop a mathematical framework to show that, for estimating many gene properties, the ... ...

    Abstract For single-cell RNA-seq experiments the sequencing budget is limited, and how it should be optimally allocated to maximize information is not clear. Here the authors develop a mathematical framework to show that, for estimating many gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene.
    Keywords Science ; Q
    Language English
    Publishing date 2020-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Deterministic column subset selection for single-cell RNA-Seq.

    Shannon R McCurdy / Vasilis Ntranos / Lior Pachter

    PLoS ONE, Vol 14, Iss 1, p e

    2019  Volume 0210571

    Abstract: Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components ... ...

    Abstract Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods to filter genes avoid those pitfalls, but ignore collinearity and covariance in the original matrix. We show that a deterministic column subset selection (DCSS) method possesses many of the favorable properties of common thresholding methods and PCA, while avoiding pitfalls from both. We derive new spectral bounds for DCSS. We apply DCSS to two measures of gene expression from two scRNA-Seq experiments with different clustering workflows, and compare to three thresholding methods. In each case study, the clusters based on the small subset of the complete gene expression profile selected by DCSS are similar to clusters produced from the full set. The resulting clusters are informative for cell type.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2019-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Understanding Interdependency Through Complex Information Sharing

    Fernando Rosas / Vasilis Ntranos / Christopher J. Ellison / Sofie Pollin / Marian Verhelst

    Entropy, Vol 18, Iss 2, p

    2016  Volume 38

    Abstract: The interactions between three or more random variables are often nontrivial, poorly understood and, yet, are paramount for future advances in fields such as network information theory, neuroscience and genetics. In this work, we analyze these ... ...

    Abstract The interactions between three or more random variables are often nontrivial, poorly understood and, yet, are paramount for future advances in fields such as network information theory, neuroscience and genetics. In this work, we analyze these interactions as different modes of information sharing. Towards this end, and in contrast to most of the literature that focuses on analyzing the mutual information, we introduce an axiomatic framework for decomposing the joint entropy that characterizes the various ways in which random variables can share information. Our framework distinguishes between interdependencies where the information is shared redundantly and synergistic interdependencies where the sharing structure exists in the whole, but not between the parts. The key contribution of our approach is to focus on symmetric properties of this sharing, which do not depend on a specific point of view for differentiating roles between its components. We show that our axioms determine unique formulas for all of the terms of the proposed decomposition for systems of three variables in several cases of interest. Moreover, we show how these results can be applied to several network information theory problems, providing a more intuitive understanding of their fundamental limits.
    Keywords Shannon information ; multivariate dependencies ; mutual information ; synergy ; information decomposition ; shared information ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 006
    Language English
    Publishing date 2016-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Single-cell transcriptional profiling of human thymic stroma uncovers novel cellular heterogeneity in the thymic medulla

    Jhoanne L. Bautista / Nathan T. Cramer / Corey N. Miller / Jessica Chavez / David I. Berrios / Lauren E. Byrnes / Joe Germino / Vasilis Ntranos / Julie B. Sneddon / Trevor D. Burt / James M. Gardner / Chun J. Ye / Mark S. Anderson / Audrey V. Parent

    Nature Communications, Vol 12, Iss 1, Pp 1-

    2021  Volume 15

    Abstract: The thymus supports T cell immunity by providing the environment for thymocyte differentiation. Here the authors profile human thymic stroma at the single cell level, identifying ionocytes as a new medullary population and defining tissue specific ... ...

    Abstract The thymus supports T cell immunity by providing the environment for thymocyte differentiation. Here the authors profile human thymic stroma at the single cell level, identifying ionocytes as a new medullary population and defining tissue specific antigen expression in multiple stromal cell types.
    Keywords Science ; Q
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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