Indeed, clustering of the 3,669 cells passing quality control threshold uncovered three populations (Extended Data Fig. 1i). Two of them correspond to the GM12878 cells (n = 2,236) and the WTC-11 cells (n
Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for...
Finally, in the third phase, high-quality communities are obtained by combining basic partitions using k means based consensus clustering. All the phases are implemented using Hadoop framework. As a result, the algorithm handles the large size network efficiently and discovers high-quality communities...
We incorporated SCIMAP into MCMICRO to perform unsupervised clustering (Leiden clustering26, Phenograph27, KMeans) for identification of cell types, and also spatial clustering to identify recurrent cellular neighborhoods28. The SCIMAP module outputs CSV files containing cluster annotations, as well as...
For those algorithms that involve the k-means Nystro¨m approximation (i.e., our proposed methods, NOAM, and NSOLAM), we compute 1600 landmark points using the k-means clustering algorithm, which is implemented in C lan- guage. We select a Gaussian kernel function to be used with the ...
Subramani et al. [39] introduced a fuzzy relaxation-based modified fuzzy c-means clustering algorithm for brain MRI segmentation. This approach incorporates an exposure-based sub-image fuzzy brightness adaptation algorithm to enhance brain tissues, followed by the clustering algorithm to segment white ...
Embedding and clustering of single-cell Droplet Hi-C dataset on cultured cells We used Higashi to infer low-dimensional cell embeddings for all our Droplet Hi-C datasets without imputations, including a human cell line mix (HeLa–GM12878–K562; Extended Data Fig. 1b), GM12878–WTC-11 (Exte...
(IVF), and clustering is used only to explore a subset of the database. These solutions are fast when low search accuracy is required, but their performance degrades very quickly when further accuracy is needed. These indices also become hard to maintain in a dynamic setting, where th...
resource allocation, node clustering, and cache target planning are the four sub-issues of this paradigm. It’s separated into three sorts, and then various resources are transferred to the node depending on how well it performs. The work is stored late after the nodes with comparable functions...
49k Accesses 41 Citations 87 Altmetric Metrics details Abstract Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by...