First, the single-link clustering is formally presented. Then, the concepts un-derlying the generic hierarchical classification technique are given. Next, analysis domains modeling a given facet of a dataset are
Finally, the consensus matrix is clustered using hierarchical clustering to produce final clustering results18. However, these traditional single-cell clustering methods are not ready to take the advantage of multi-omics data to improve clustering performance and are thus not applicable to multimodal ...
Hierarchical clustering of epithelial cell communities was performed on the basis of classic marker genes, including luminal lineage (expressingEPCAM,KRT8,KRT18) and myoepithelial lineage (expressingKRT5,KRT14,KRT17). In addition, we integrated four single-cell datasets of normal human mammary epithelial...
Hierarchical clustering of the TCGA AMLs by these signatures revealed seven clusters of tumors with distinct malignant cell-type compositions (Figures 5F and S5C). Several clusters included tumors with high abundances of specific cell types, such as GMP-like (clusters A and B), progenitor-like (...
a Hierarchical clustering of the correlation between each transformed subpopulation and a database of cell type-specific expression profiles with high variability across the data set. We find three cell type clusters referred to as Neural/ESC, Immune, and Mesenchymal/MSC which divide the tumor cell...
use scipy hierarchical linkage rather than fastcluster Feb 28, 2025 View all files README MIT license Consensus NMF (cNMF) cNMF is a pipeline for inferring gene expression programs from scRNA-Seq. It takes a count matrix (N cells X G genes) as input and produces a (K x G) matrix of ...
subpopulations.DThe most differentially expressed gene within eachCD8 + T cell cluster.EHierarchical clustering of theCD8 + T cell subsets based on dysfunction and cytotoxicity scores.FComposition of theCD8 + T cell subset (excluding the two non-T cell clusters and the single-...
(IPC), a transitional state between qNSCs and mature neurons. However, they note an additional cluster of 23 cells branching off of this lineage, potentially representing an alternative terminal cell type. As in the original paper, we used the hierarchical clustering labels and the first two ...
We evaluated how well clustering could recover cell types (using only cell types with at least 10 cells) by clustering each dataset based on the integrated reduced dimensional space using hierarchical graph-based clustering56 into between 2 and 50 clusters. For each clustering solution, we calculate...
In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Forest Fi