Runs a typical Seurat workflow on a Seurat object (up to dimensionality reduction and clustering).
FindClustersRecall.RdGiven a Seurat object, returns a new Seurat that has been normalized, had variable features identified, scaled, had principal components computed, hadclusters identified, and had tSNE and UMAP embeddings determined.
Usage
FindClustersRecall(
seurat_obj,
resolution_start = 0.8,
reduction_percentage = 0.2,
num_clusters_start = 20,
dims = 1:10,
algorithm = "louvain",
null_method = "ZIP",
assay = "RNA",
cores = 1,
shared_memory_max = 8000 * 1024^2,
verbose = TRUE
)Arguments
- seurat_obj
The Seurat object that will be analyzed.
- resolution_start
The starting resolution to be used for the clustering algorithm (Louvain and Leiden algorithms).
- reduction_percentage
The amount that the starting parameter will be reduced by after each iteration (between 0 and 1).
- num_clusters_start
The starting number of clusters to be used for the clustering algorithm (K-means and Hierarchical clustering algorithms).
- dims
The dimensions to use as input features (i.e. 1:10).
- algorithm
The clustering algorithm to be used.
- null_method
The generating distribution for the synthetic null variables (ZIP, NB, ZIP-copula, NB-copula)
- assay
The assay to generate artificial variables from.
- cores
The number of cores to compute marker genes in parallel.
The maximum size for shared global variables. Increased this variable if you see the following error: The total size of the X globals that need to be exported for the future expression ('FUN()') is X GiB. This exceeds the maximum allowed size of 500.00 MiB (option 'future.globals.maxSize'). The X largest globals are ...
- verbose
Whether or not to show all logging.