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 <cstdlib>78usingnamespa...
Single-link-Clustering Description Given n nodes in a two-dimensional space, we want to use single-link custering method to find k clusters. This is equivalent to finding an MST (Minimum spanning tree) of these nodes and deleting k-1 longest edges. Your job is to output the length of th...
Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive ...
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...
Clustering of both, cell lines and drugs, was performed using the hclust function and resulting dendrograms cut with the cutree function of R statistical environment. Differential drug sensitivity analysis We used DREEP to predict the effects of 2434 drugs on 1541 sequenced cells of the barcoded ...
Imputation, cell type annotation, and clustering tasks are supported under this module. For the multimodal module, multiple modalities for the cell can be accessed. For example, CITE-seq can provide both gene expression and protein data for analysis. Modality prediction, modality matching, and ...
We are now working on submitting it to Bioconductor and will provide the link once online. Quick Start The following code is a quick example of running our simulator. The functionscdesign3()takes in aSinglecellExperimentobject with the cell covariates(such as cell types, pseudotime, or spatial...
For example, SC3 has comparatively high cell clustering concordance compared to other best performing methods (e.g. Monocle3) but was significantly over-estimating the number of cell types (Fig. 2a, b). These results highlight the importance of evaluating the number of cell types estimation ...
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res. 7, 1297 (2018). Article PubMed Central PubMed Google Scholar Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci....
(Supplementary Figs.S1b–e), transcriptomes of 79200 cells were obtained. A total of 16 distinct cell clusters were identified according to unsupervised hierarchical clustering (Fig.3a; Supplementary Figs.S1f,1g). We formed cluster-specific marker genes using differential gene expression analysis to ...