Spatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficientl
Top: Three-dimensional (3D) volume of an entire porcine oocyte (Imaris, Bitplane). Bottom: isosurface rendering (Imaris, Bitplane) of the cell membrane and chromosomes. h, Line graph showing mean distance of chromosomes from the cell membrane. Scale bar, 10 µm unless otherwise specified....
In fact, clustering algorithms become one of your most important tools when dealing with a lot of high-dimensional data like global gene expression data. It allows you to group data points automatically and systematically. Imagine you have measured how a drug affects 3 different cell types, for ...
dimensional TRACLUS technique described inLee, J.-G., J. Han, and K.-Y. Whang, 2007: Trajectory clustering: a partition-and-group framework. Proceedings of the 2007 ACM SIGMOD international conference on Management of data, SIGMOD ’07, New York, NY, USA, Association for Computing Machinery...
Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually exist in different low-dimensional subspaces hidden in the original space. A family of Gaussian mixtu...
This paper studies the problem of clustering high dimensional data. The paper proposes an algorithm called the CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the Maximum Likelihood Principle, CoFD attempts to optimize its parameter settings to...
Clustering has been used extensively as a vital tool of data mining. Data gathering has been deliberated widely, but mostly all identified usual clustering... G Sugendran,D Dhanabagyam 被引量: 0发表: 0年 Department of Computing Science University of Alberta HIGH-DIMENSIONAL DATA MINING: SUBSPACE...
Compressive Clustering of High-Dimensional Data Ruta, A., Porikli, F.: Compressive clustering of high-dimensional data. In: 11th International Conference on Machine Learning and Applications (ICMLA), ... A Ruta,F Porikli - International Conference on Machine Learning & Applications 被引量: 8发表...
Finding clusters of data objects in high dimensional space is challenging, especially considering that such data can be sparse and highly skewed. This paper focuses on using concept lattice to solve high dimensional sparse data clustering problem. Concept Lattice Theory is an effective tool for data...
Data from: Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Deisseroth Lab http://clarityresourcecenter.org/ (2018). Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Zenodo https://doi.org/...