Single-link Clustering http://soj.sysu.edu.cn/show_problem.php?pid=1000&cid=1750 题目说是单链聚类,其实就是最小生成树,输出第k-1大的边; 我用的是kruskal算法: 1 #include <iostream> 2 #include <cstdio> 3 #include <cstring> 4 #include <cmath> 5 #include <algorithm> 6 #include <...
In joint clustering, all datasets may not be equally well clustered, or some datasets may dominate the integration due to for example a difference in the number of cells. To assess if there is a performance imbalance between datasets, we evaluated the clustering performance in each dataset after...
We also found that ADT could complement RNA-based clustering. For example, the simultaneous expression of T cell markers (CD3 and CD4) was unexpectedly observed in two subclusters of B cells (B2 and B3) expressing canonical B cell makers (CD19, CD21 and CD22; Fig. 2g). As this ...
A number of data-driven metrics have been proposed to address the challenges of over- and under-clustering, some of which estimate the statistical robustness of clusters. A notable example is theSC3package, which provides an estimate of cluster number in a dataset and also includes a measure of...
After clustering, user can interactively visualize and analyze the data with modulevisualize scATAC-pro -s visualize -i output/downstream_analysis/PEAK_CALLER/CELL_CALLER/VisCello_obj -c configure_user.txt Note that the visualization can also be done through R/Rstudio: ...
Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data
Clustering analysis was performed at a resolution parameter of 0.4 and 0.2 for TM-1 and Jin668, respectively. The t-SNE [84] and UMAP [85] methods were used to visualize the cell clusters. Identification of all markers for each cluster were performed using function FindAllMarkers with a ...
scGNN (single cell graph neural networks) for single cell clustering and imputation using graph neural networks - juexinwang/scGNN
Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised clustering algorithms
We next analyzed the mesenchymal compartment of the PDAC TME. Mesenchymal cell reclustering revealed seven distinct clusters (Fig.3A). The two main clusters consisted of myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs) (Fig.3A, B)29. We did not find a strong antigen-presenting CAF...