Clustering on Network of Samples
A slightly faster way of calculating column correlation matrix
Create and preprocess a Seurat object
Find threshold of cluster detectability
Find threshold of cluster detectability in trees of clusters
Rescale the weights in an edge matrix to match a given perplexity.
Check that the count data contain only integer counts
Conos R6 class
Convert Conos object to Pagoda2 object
Set edge matrix edgeMat with certain values on sample
Estimate entropy of edge weights per cell according to the specified f...
Filter genes by requiring minimum average expression within at least o...
Increase resolution for a specific set of clusters
Compare two cell types across the entire panel
Compare two cell types across the entire panel
Access cell names from sample
Access clustering from sample
Extract specified clustering from list of conos clusterings
Evaluate consistency of cluster relationships Using the clustering we ...
Access count matrix from sample
Access embedding from sample
Access gene expression from sample
Access genes from sample
Deprecated; Get markers for global clusters
Establish rough neighbor matching between samples given their projecti...
Get top overdispersed genes across samples
Access overdispersed genes from sample
Access PCA from sample
Do differential expression for each cell type in a conos object betwee...
Evaluate how many clusters are global
Access raw count matrix from sample
Retrieve sample names per cell
Performs a greedy top-down selective cut to optmize modularity
Constructrs a two-step clustering, first running multilevel.communitie...
Constructs a two-step clustering, first running multilevel.communities...
Get a vector with the levels of a factor named with their own name. Us...
Get a vector of the names of an object named by the names themselves. ...
Utility function to generate a pagoda2 app from a conos object
Plots barplots per sample of composition of each pagoda2 application b...
Generate boxplot per cluster of the proportion of cells in each cellty...
Plot fraction of variance explained by the successive reduced space co...
Plot a heatmap of differential genes
Plot panel of specified embeddings
Plot panel of specified embeddings, extracting them from pagoda2 objec...
Project a distance matrix into a lower-dimensional space.
Estimate labeling distribution for each vertex, based on provided labe...
Perform CCA (using PMA package or otherwise) on two samples
Perform cpca on two samples
Use space of combined sample-specific PCAs as a space
Get raw matrices with common genes
Objects exported from other packages
Save Conos object on disk to read it from ScanPy
Save differential expression as table in *csv format
Save differential expression results as JSON
Scan joint graph modularity for a range of k (or k.self) values Builds...
Calculate the default number of batches for a given number of vertices...
Determine number of detectable clusters given a reference walktrap and...
RNA velocity analysis on samples integrated with conos Create a list o...
Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.