Selected article for: "cell number and reduce cell number"
Author: Lulin Zhou; Zubiao Niu; Xiaoyi Jiang; Zhengrong Zhang; You Zheng; Zhongyi Wang; Yichao Zhu; Lihua Gao; Xiaoning Wang; Qiang Sun
Title: Systemic analysis of tissue cells potentially vulnerable to SARS-CoV-2 infection by the protein-proofed single-cell RNA profiling of ACE2, TMPRSS2 and Furin proteases
  • Document date: 2020_4_10
    • ID: 3btc31kj_12
    Snippet: In order to reduce the number of dimensions representing each cell, the "Run PCA" functions in Seurat was performed to calculate principal components (PCs). And the number of PCs used in downstream analysis and visualization was determined based on the "Jack Straw" procedure and elbow of a scree plot. Nonlinear dimensionality reduction algorithms including uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbour.....
    Document: In order to reduce the number of dimensions representing each cell, the "Run PCA" functions in Seurat was performed to calculate principal components (PCs). And the number of PCs used in downstream analysis and visualization was determined based on the "Jack Straw" procedure and elbow of a scree plot. Nonlinear dimensionality reduction algorithms including uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbour embedding (t-SNE) were also used to unsupervised clustering of single cells. Specifically, we made use of the UMAP and t-SNE to place cells with similar local neighbourhoods based on the statistically significant PCs to visualize the datasets.

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