Throughout the manuscript we use diffusion maps, a non-linear dimensionality reduction technique37. We calculate a cell-to-cell distance matrix using 1-Pearson correlation and use the diffuse function of the diffusionMap R package with default parameters to obtain the first 50 DMCs. To determine ...
Dimensionality reductionPCAUMAPMass cytometry is a new high-throughput technology that is becoming a cornerstone in immunology and cell biology research. With technological advancement, the number of cellular characteristics cytometry can simultaneously quantify grows, making analysis increasingly computationally ...
Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the res...
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant analysis. This provides a completely new approach to ...
9 to project single-gene deletion strains into two dimensions and subsequently used Louvain clustering25 on strains in 2D UMAP space using default parameters (except, reduceDimension: reduction_method = UMAP, metric = cosine, n_neighbors = 10, min_dist = 0.05; clusterCells: ...
Among various techniques, spectral clustering [5] is one of the most flexible methods that doesn't make assumptions on cluster shape... Y Pei,TV Tjahja 被引量: 0发表: 0年 A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from ...
analysis showed that current computational tools work well for cell-type characterization for small or moderate dataset sizes, but may not scale to large dataset sizes and/or vary in performance across different datasets11. Apart from performing dimensionality reduction, the growing number of published...
Dimensionality reduction is a way to overcome these problems. Principal component analysis (PCA) and singular value decomposition (SVD) are popular techniques... S Tsuge,M Shishibori,S Kuroiwa,... - IEEE 被引量: 92发表: 2001年 Design Optimization Problem Reformulation Using Singular Value Decompos...
Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering. BMC bioinformatics, 14(1), 222.S. Ji, "Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and cluster- ing," BMC bioinformatics,...
A Clustering Algorithm Based on Semi-supervised Dimensionality Reduction Semi-supervised clustering algorithms use a small amount of supervision information in the form of labeled data or pairwise constraints to improve clusteri... FM Zhu,DQ Zhang - 《Journal of Guangxi Normal University》 被引量: ...