The paper proposes a simple and faster version of the kernel k-means clustering method, called single pass kernel k -means clustering method. The proposed method works as follows. First, a random sample (mathcal
After the clustering process has been performed, a single representative variable can be extracted from each group. Suppose that the available x data are disposed in a matrix X of dimensions (N × K) such that the kth variable xk is associated to the kth column vector xk∈RN. This vector...
A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. To address this difficu...
However, existing clustering methods are often presented in a single-layer formulation (i.e., shallow formulation). As a result, the mapping between the obtained low-level representation and the original input data may contain rather complex hierarchical information. To overcome the drawbacks of low...
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is...
invention relates to a method for reducing the number of results generated by the alignment of a query protein or nucleotide sequence against a target protein or nucleotide sequence by an alignment algorithm, the method comprising the step of combining two or more alignment results into a single ...
CASCC: A co-expression assisted single-cell RNA-seq data clustering method CASCC, a clustering method designed to improve biological accuracy using gene co-expression features identified using an unsupervised adaptive attractor algorithm. The users can refer to our published manuscript, available at th...
However, instead of following some of the single clustering algorithm procedures in building a consensus function, we are using the generated members as initial clusters of the dataset and the final clustering is generated in three stages, as shown in Fig. 2. The first stage is to transfer the...
CCI (Consensus Clustering Imputation) is a novel imputation method inspired by consensus clustering techniques. It aims to address the issue of dropouts in single-cell RNA sequencing (scRNA-seq) and other high-dimensional data. CCI borrows the information from similar cells by ensemble clustering to...
Traditional clustering methods often use a single attribute to divide the original group without requiring a combination of the above two attributes. However, these two attributes play different roles in the clustering process, insofar as opinion similarity is used to measure the level of difference ...