It then computes a consensus matrix by summarizing the three individual clustering results. 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 ...
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 ...
E Hierarchical clustering of the CD8 + T cell subsets based on dysfunction and cytotoxicity scores. F Composition of the CD8 + T cell subset (excluding the two non-T cell clusters and the single-patient cluster). Full size image Because our initial T/NK clustering did not identify...
Code to support: "pcaReduce: hierarchical clustering of single cell transcriptional profiles" - GitHub - JustinaZ/pcaReduce: Code to support: "pcaReduce: hierarchical clustering of single cell transcriptional profiles"
Thus, we propose Markov hierarchical clustering algorithm (MarkovHC), which reconstructs multi-scale pseudo-energy landscape by exploiting underlying metastability structure in an exponentially perturbed Markov chain . A Markov process describes the random walk of a hypothetically traveling cell in the ...
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...
The genes (rows) and groups (columns) can be furthered clustered using hierarchical clustering. The eighth tab "Differential expression" allows users to perform gene differential expression analysis grouped by categorical cell information (e.g. library / cluster) or using partial cells (group.1 ...
(e.g., linear, bifurcation, tree) using a gamma-Poisson hierarchical model. Importantly, this approach provides ground truth reference information (e.g., cell type annotations, differentially expressed genes per cell type and trajectory, and a latent vector that describes an individual cell’s ...
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...
We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.Similar content being viewed by others RA3 is a reference-guided approach for epigenetic characterization of ...