In this chapter, a multi-facet hierarchical classi-fication technique based on the single-link clustering is proposed. 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...
coefficient (red/blue colorbar) of pairwise gene expression profiles (rows and columns) significantly (FDR < 5%) associated positively (purple) or negatively (green), with c-Fos (additive model), p65 (additive model), or c-Fos*p65 (interaction model), ordered by hierarchical clustering....
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 ...
or artifactual sub-populations. When cells are slightly over-clustered, non-relevant subdivisions have been introduced; however, these subclusters can still be merged to recover appropriate cell types. Once severe over-clustering occurs, however, some clusters may be shattered, meaning they are segreg...
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 (...
All hierarchical clustering performed used Pearson correlation as the distance function. All k-medoids clustering shown in this work is performed using 10 replicates with the distance function Pearson correlation. For k-mediods clustering throughout this work, the gap statistic is used to determine the...
Agglomerative hierarchical clustering is a “bottom up” approach: each observation starts in its own cluster, and pairs of clusters are iteratively merged based on inter-cluster distances. Ward’s method [16] was used as linkage criterion. We included in the comparison four variants of hierarchica...
Hierarchical clustering of modules is displayed as topological overlap matrix (TOM) plots and similarity between gene modules are displayed as adjacency plots. The hub genes for each module were identified as module eigengene based connectivity kME > 0.8 and P < 0.05. Gene enrichment ...
Yuchao Jiang, Nancy R. Zhang, and Mingyao Li. "SCALE: modeling allele-specific gene expression by single-cell RNA sequencing." Genome Biology 18.1 (2017): 74.link Common questions How to adjust for possible heterogeneity within the cell population?
Here we extend a previous method, significance of hierarchical clustering, to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to ...