The Experts below are selected from a list of 201 Experts worldwide ranked by ideXlab platform
Debarka Sengupta - One of the best experts on this subject based on the ideXlab platform.
-
Discovery of rare cells from voluminous single cell expression data
Nature Communications, 2018Co-Authors: Aashi Jindal, Prashant Gupta, Debarka SenguptaAbstract:Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage. Algorithms designed to find rare cells in single cell RNA-seq data sets cannot cope with data sets containing tens of thousands of cells. Here the authors present Finder of Rare Entities (FiRE), an algorithm that uses the Sketching Technique to assign a rareness score to every expression profile in large RNA-seq data sets.
-
Discovery of rare cells from voluminous single cell expression data
Nature Publishing Group, 2018Co-Authors: Aashi Jindal, Prashant Gupta, Debarka SenguptaAbstract:Algorithms designed to find rare cells in single cell RNA-seq data sets cannot cope with data sets containing tens of thousands of cells. Here the authors present Finder of Rare Entities (FiRE), an algorithm that uses the Sketching Technique to assign a rareness score to every expression profile in large RNA-seq data sets
Aashi Jindal - One of the best experts on this subject based on the ideXlab platform.
-
Discovery of rare cells from voluminous single cell expression data
Nature Communications, 2018Co-Authors: Aashi Jindal, Prashant Gupta, Debarka SenguptaAbstract:Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage. Algorithms designed to find rare cells in single cell RNA-seq data sets cannot cope with data sets containing tens of thousands of cells. Here the authors present Finder of Rare Entities (FiRE), an algorithm that uses the Sketching Technique to assign a rareness score to every expression profile in large RNA-seq data sets.
-
Discovery of rare cells from voluminous single cell expression data
Nature Publishing Group, 2018Co-Authors: Aashi Jindal, Prashant Gupta, Debarka SenguptaAbstract:Algorithms designed to find rare cells in single cell RNA-seq data sets cannot cope with data sets containing tens of thousands of cells. Here the authors present Finder of Rare Entities (FiRE), an algorithm that uses the Sketching Technique to assign a rareness score to every expression profile in large RNA-seq data sets
Prashant Gupta - One of the best experts on this subject based on the ideXlab platform.
-
Discovery of rare cells from voluminous single cell expression data
Nature Communications, 2018Co-Authors: Aashi Jindal, Prashant Gupta, Debarka SenguptaAbstract:Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage. Algorithms designed to find rare cells in single cell RNA-seq data sets cannot cope with data sets containing tens of thousands of cells. Here the authors present Finder of Rare Entities (FiRE), an algorithm that uses the Sketching Technique to assign a rareness score to every expression profile in large RNA-seq data sets.
-
Discovery of rare cells from voluminous single cell expression data
Nature Publishing Group, 2018Co-Authors: Aashi Jindal, Prashant Gupta, Debarka SenguptaAbstract:Algorithms designed to find rare cells in single cell RNA-seq data sets cannot cope with data sets containing tens of thousands of cells. Here the authors present Finder of Rare Entities (FiRE), an algorithm that uses the Sketching Technique to assign a rareness score to every expression profile in large RNA-seq data sets
Doug L James - One of the best experts on this subject based on the ideXlab platform.
-
real time subspace integration for st venant kirchhoff deformable models
International Conference on Computer Graphics and Interactive Techniques, 2005Co-Authors: Jernej Barbic, Doug L JamesAbstract:In this paper, we present an approach for fast subspace integration of reduced-coordinate nonlinear deformable models that is suitable for interactive applications in computer graphics and haptics. Our approach exploits dimensional model reduction to build reduced-coordinate deformable models for objects with complex geometry. We exploit the fact that model reduction on large deformation models with linear materials (as commonly used in graphics) result in internal force models that are simply cubic polynomials in reduced coordinates. Coefficients of these polynomials can be precomputed, for efficient runtime evaluation. This allows simulation of nonlinear dynamics using fast implicit Newmark subspace integrators, with subspace integration costs independent of geometric complexity. We present two useful approaches for generating low-dimensional subspace bases: modal derivatives and an interactive Sketching Technique. Mass-scaled principal component analysis (mass-PCA) is suggested for dimensionality reduction. Finally, several examples are given from computer animation to illustrate high performance, including force-feedback haptic rendering of a complicated object undergoing large deformations.
Muhammad Abdullah Adnan - One of the best experts on this subject based on the ideXlab platform.
-
ssketch a scalable Sketching Technique for pca in the cloud
Web Search and Data Mining, 2018Co-Authors: Md Mehrab Tanjim, Muhammad Abdullah AdnanAbstract:Multidimensional data appear frequently in many web-related applications, e.g., product ratings, the bag-of-words representation of web pages, etc. Principal Component Analysis (PCA) has been widely used for discovering patterns in relationships among entities in multidimensional data. However, existing algorithms for PCA have limited scalability since they explicitly materialize intermediate data, whose size rapidly grows as the dimension increases. To avoid scalability issues, we propose sSketch, a scalable Sketching Technique for PCA that employs several optimization ideas, such as mean propagation, efficient sparse matrix operations, and effective job consolidation to minimize intermediate data. Using sSketch, we also provide two other scalable methods for deriving singular value and 2-norm of reconstruction error, both of which are used for data analysis purpose. We provide our implementation on popular Spark framework for distributed platform. We compare our method against state-of-the-art library functions available for distributed settings, namely MLlib-PCA and Mahout-PCA with real big datasets. Our experiments show that our method outperforms both of them by a wide margin. To encourage reproducibility, the source code of sSketch is made publicly available at \hrefhttps://github.com/DataMiningResearch/sSketch https://github.com/DataMiningResearch/sSketch.
-
WSDM - sSketch: A Scalable Sketching Technique for PCA in the Cloud
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018Co-Authors: Md Mehrab Tanjim, Muhammad Abdullah AdnanAbstract:Multidimensional data appear frequently in many web-related applications, e.g., product ratings, the bag-of-words representation of web pages, etc. Principal Component Analysis (PCA) has been widely used for discovering patterns in relationships among entities in multidimensional data. However, existing algorithms for PCA have limited scalability since they explicitly materialize intermediate data, whose size rapidly grows as the dimension increases. To avoid scalability issues, we propose sSketch, a scalable Sketching Technique for PCA that employs several optimization ideas, such as mean propagation, efficient sparse matrix operations, and effective job consolidation to minimize intermediate data. Using sSketch, we also provide two other scalable methods for deriving singular value and 2-norm of reconstruction error, both of which are used for data analysis purpose. We provide our implementation on popular Spark framework for distributed platform. We compare our method against state-of-the-art library functions available for distributed settings, namely MLlib-PCA and Mahout-PCA with real big datasets. Our experiments show that our method outperforms both of them by a wide margin. To encourage reproducibility, the source code of sSketch is made publicly available at \hrefhttps://github.com/DataMiningResearch/sSketch https://github.com/DataMiningResearch/sSketch.