The Experts below are selected from a list of 9402 Experts worldwide ranked by ideXlab platform

Evan W Newell - One of the best experts on this subject based on the ideXlab platform.

  • Dimensionality Reduction for visualizing single cell data using umap
    Nature Biotechnology, 2019
    Co-Authors: Etienne Becht, Charlesantoine Dutertre, Florent Ginhoux, Evan W Newell, Leland Mcinnes, John Healy, Immanuel Kwok
    Abstract:

    Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for Dimensionality Reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear Dimensionality-Reduction Technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

  • evaluation of umap as an alternative to t sne for single cell data
    bioRxiv, 2018
    Co-Authors: Etienne Becht, Charlesantoine Dutertre, Iwh Kwok, Florent Ginhoux, Evan W Newell
    Abstract:

    Uniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear Dimensionality Reduction Technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

Amaury Lendasse - One of the best experts on this subject based on the ideXlab platform.

  • elm som a continuous mapping for visualization
    Neurocomputing, 2019
    Co-Authors: Karl Ratner, Edward Ratner, Yoan Miche, Kajmikael Bjork, Amaury Lendasse
    Abstract:

    Abstract This paper presents a novel Dimensionality Reduction Technique based on ELM and SOM: ELM-SOM+. This Technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the Dimensionality Reduction and the second one to perform the reconstruction. ELM-SOM+ is tested successfully on nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.

  • elm som a continuous self organizing map for visualization
    International Joint Conference on Neural Network, 2018
    Co-Authors: Venous Roshdibenam, Yoan Miche, Kajmikael Bjork, Hans J Johnson, Emil Eirola, Anton Akusok, Amaury Lendasse
    Abstract:

    This paper presents a novel Dimensionality Reduction Technique: ELM-SOM. This Technique preserves the intrinsic quality of Self-Organizing Maps (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the Dimensionality Reduction and the second one to perform the reconstruction. ELM-SOM is tested successfully on six diverse datasets. Regarding reconstruction error, ELM-SOM is comparable to SOM while bringing continuity.

Etienne Becht - One of the best experts on this subject based on the ideXlab platform.

  • Dimensionality Reduction for visualizing single cell data using umap
    Nature Biotechnology, 2019
    Co-Authors: Etienne Becht, Charlesantoine Dutertre, Florent Ginhoux, Evan W Newell, Leland Mcinnes, John Healy, Immanuel Kwok
    Abstract:

    Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for Dimensionality Reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear Dimensionality-Reduction Technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

  • evaluation of umap as an alternative to t sne for single cell data
    bioRxiv, 2018
    Co-Authors: Etienne Becht, Charlesantoine Dutertre, Iwh Kwok, Florent Ginhoux, Evan W Newell
    Abstract:

    Uniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear Dimensionality Reduction Technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

Richard D Braatz - One of the best experts on this subject based on the ideXlab platform.

  • fault diagnosis in chemical processes using fisher discriminant analysis discriminant partial least squares and principal component analysis
    Chemometrics and Intelligent Laboratory Systems, 2000
    Co-Authors: Leo H Chiang, Evan L Russell, Richard D Braatz
    Abstract:

    Abstract Principal component analysis (PCA) is the most commonly used Dimensionality Reduction Technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares (DPLS) has been shown to improve fault diagnosis for small-scale classification problems as compared with PCA. Fisher's discriminant analysis (FDA) has advantages from a theoretical point of view. In this paper, we develop an information criterion that automatically determines the order of the Dimensionality Reduction for FDA and DPLS, and show that FDA and DPLS are more proficient than PCA for diagnosing faults, both theoretically and by applying these Techniques to simulated data collected from the Tennessee Eastman chemical plant simulator.

Kajmikael Bjork - One of the best experts on this subject based on the ideXlab platform.

  • elm som a continuous mapping for visualization
    Neurocomputing, 2019
    Co-Authors: Karl Ratner, Edward Ratner, Yoan Miche, Kajmikael Bjork, Amaury Lendasse
    Abstract:

    Abstract This paper presents a novel Dimensionality Reduction Technique based on ELM and SOM: ELM-SOM+. This Technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the Dimensionality Reduction and the second one to perform the reconstruction. ELM-SOM+ is tested successfully on nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.

  • elm som a continuous self organizing map for visualization
    International Joint Conference on Neural Network, 2018
    Co-Authors: Venous Roshdibenam, Yoan Miche, Kajmikael Bjork, Hans J Johnson, Emil Eirola, Anton Akusok, Amaury Lendasse
    Abstract:

    This paper presents a novel Dimensionality Reduction Technique: ELM-SOM. This Technique preserves the intrinsic quality of Self-Organizing Maps (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the Dimensionality Reduction and the second one to perform the reconstruction. ELM-SOM is tested successfully on six diverse datasets. Regarding reconstruction error, ELM-SOM is comparable to SOM while bringing continuity.