findMonocleClusters.Rd
Assign barcode spots to clusters according to different clustering algorithms.
findMonocleClusters( object, preprocess_method = c("PCA", "LSI"), reduction_method = c("UMAP", "tSNE", "PCA", "LSI"), cluster_method = c("leiden", "louvain"), k = 20, num_iter = 5, prefix = "Cluster ", verbose = TRUE )
object | A valid spata-object. |
---|---|
preprocess_method | Monocle3 - description: A string specifying the initial dimension method to use, currently either PCA or LSI. For LSI (latent semantic indexing), it converts the (sparse) expression matrix into tf-idf matrix and then performs SVD to decompose the gene expression / cells into certain modules / topics. Default is "PCA". |
reduction_method | Monocle3 - description: A character string specifying the algorithm to use for dimensionality reduction. Currently "UMAP", "tSNE", "PCA" and "LSI" are supported. |
cluster_method | Monocle3 - description: String indicating the clustering method to use. Options are "louvain" or "leiden". Default is "leiden". Resolution parameter is ignored if set to "louvain". |
k | Monocle3 - description: Integer number of nearest neighbors to use when creating the k nearest neighbor graph for Louvain/Leiden clustering. k is related to the resolution of the clustering result, a bigger k will result in lower resolution and vice versa. Default is 20. |
num_iter | Monocle3 - description: Integer number of iterations used for Louvain/Leiden clustering. The clustering result giving the largest modularity score will be used as the final clustering result. Default is 1. Note that if num_iter is greater than 1, the random_seed argument will be ignored for the louvain method. |
prefix | Character value. Clustering algorithms often return only numbers as names for the clusters they generate. If you want to these numbers to have a certain prefix (like 'Cluster', the default) you can specify it with this argument. |
A tidy spata-data.frame
This functions is a wrapper around all monocle3-cluster algorithms which take several options for dimensional reduction upon which the subsequent clustering bases. It iterates over all specified methods and returns a tidy data.frame in which each row represents one barcode-spot uniquely identified by the variable barcodes and in which every other variable about the cluster belonging the specified combination of methods returned. E.g.:
A call to `findMonocleClusters()` with
preprocess_method
set to 'PCA'
reduction_method
set to c('UMAP', 'PCA')
'leiden'
, k
set to 5
will return a data.frame of the following variables:
barcodes
mncl_cluster_UMAP_leiden_k5
mncl_cluster_PCA_leiden_k5
Due to the barcodes-variable it can be easily joined to your-spata object via `addFeature()`. and thus be made available for all spata-functions.