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...
Cell calling, clustering and differential expression were performed using PIPseeker v1.0.0 (Fluent Biosciences) in ‘reanalyze’ mode using –force-cells 65000. The top differentially expressed genes from the PIPseeker graph-based clustering result were used to determine cell types by comparing to ...
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...
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from stati
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: ...
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
scGNN (single cell graph neural networks) for single cell clustering and imputation using graph neural networks - juexinwang/scGNN
We note that cell population annotations were externally determined through cell surface protein measurements and not from unsupervised clustering on the expression data. To obtain predicted trajectories from integrated data, we performed trajectory inference using two approaches that were shown to outperform...
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