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 <...
A single-cell clustering algorithm should be computationally efficient. As the number of cells sequenced continues to grow, single-cell RNA-sequencing (scRNA-seq) datasets can have more than a million cells, and clustering once on such a large dataset can take days16. Therefore, it is important...
The structural heterogeneity was determined by analyzing the 3D density maps reconstructed from the centroids of clusters in latent space generated by the simple Kmeans clustering algorithm45. The latent space of different methods was also visualized in 2D using UMAP21. The structural heterogeneity was...
It is rare that a single set of parameters in any clustering algorithm will resolve all putative cell types equally well, especially given the multi-scale organization of most biological systems. Thus, we highlight that an important aspect of chooseR is its ability to identify which clusters are...
which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. ...
for example, adult hematopoiesis violates assumptions of current RNA velocity models17, precluding us from applying CellRank to this well-studied system and prompting the question of whether the algorithm could be developed further to reconstruct differentiation dynamics using another aspect of these data...
score of all clustering output on encoded datasets and select thekthat gives the highest average score. Since scCCESS can be used with any clustering algorithm that allows user-specifiedkvalues, we coupled scCCESS with a basick-means clustering algorithm and SIMLR [25], a single-cell specific ...
Potential doublets were identified using the DoubletFinder algorithm (version 2.0.2) [83] with three input parameters: the number of expected real doublets (nExp) (cell numbers/100000), the number of artificial doublets (pN) (pN = 0.25), and the neighborhood size (pK) (optimal pK ...
shared nearest neighbor (SNN) modularity optimization based clustering algorithm that first calculates k-nearest neighbors and constructs the SNN graph, then optimizes the modularity function to determine clusters; this method is referred to asSeurat_SNN. ...
K-means is done on either the log-transformed expression matrix or the 2-by-mcorrelation t-SNE matrix. The algorithm is implemented by thekmeansfunction in R [25]. Hierarchical clustering Hierarchical clustering (Hclust) is done on either the log-transformed expression matrix or the 2-by-mco...