find_differentiated_clusters | R Documentation |
Find Differentiated Clusters
find_differentiated_clusters(object, ...) ## Default S3 method: find_differentiated_clusters( object, differential_function = differential_ChromSCape, by = "IDcluster", logFC.th = log2(1.5), qval.th = 0.01, min_frac_cell_assigned = 0.1, limit = 5, FP_linear_model = NULL, cluster_of_origin = "Alpha", min_cluster_size = 30, verbose = TRUE, ... ) ## S3 method for class 'Seurat' find_differentiated_clusters( object, differential_function = differential_edgeR_pseudobulk_LRT, by = "IDcluster", logFC.th = log2(1.5), qval.th = 0.01, limit = 5, cluster_of_origin = "Alpha", min_frac_cell_assigned = 0.1, min_cluster_size = 30, verbose = TRUE, ... )
object |
A Seurat object containing scRNA dataset with 'IDcluster' column. |
... |
Additional parameters passed to the differential_function. See
|
differential_function |
A function that take in entry a SingleCellExperiment object and parameters passed in ... and returns a data.frame containing the significantly differential features for each cluster. See differential_edgeR_pseudobulk_LRT for the default function. |
by |
A character specifying the name of the metadata column referencing the clusters. |
logFC.th |
A numeric specifying the log2 fold change of activation above/below which a feature is considered as significantly differential passed to the differential_function. |
qval.th |
A numeric specifying the adjusted p-value below which a feature is considered as significantly differential passed to the differential_function. |
min_frac_cell_assigned |
A numeric between 0 and 1 specifying the minimum percentage of the total cells in the SingleCellExperiment object that needs to be assigned. If a lower proportion is assigned, all cells are assigned to the cluster of origin. |
limit |
An integer specifying the minimum number of features required for a subcluster to be called 'true' subcluster. |
FP_linear_model |
Optional. A linear model (see |
cluster_of_origin |
A character specifying the name of the cluster of origin that will be concatenated before the name of true subclusters. |
min_cluster_size |
An integer specifying the minimum number of cells in a cluster to consider it as a 'true' subcluster. |
verbose |
A logical specifying wether to print. |
Find significantly differential features between the given set
of clusters (within the 'IDcluster' column of the SingleCellExperiment).
For each cluster, if enough differences are found, mark the cluster as a
'true' subcluster and gives it the alias 'cluster_of_origin_cluster'.
The function will use by default ChromSCape::differential_activation()
function to define differential features.
A list containing :
"diffmat_n" - A data.frame containing the number of differential regions foun per cluster and the new assignations of the subclusters.
"res" - A data.frame containing the differential analysis.
"passing_min_pct_cell_assigned" - A boolean indicating if enough cells were assigned
differential_ChromSCape()
if(requireNamespace("Seurat", quietly=TRUE)){ # Find differentiated clusters in Seurat object using # edgeR_pseudobulk_LRT function data("Seu") DA = find_differentiated_clusters( Seu, differential_function = differential_edgeR_pseudobulk_LRT, logFC.th = log2(1.5), qval.th = 0.01, by = "seurat_clusters", limit = 5, cluster_of_origin = "Alpha", min_frac_cell_assigned = 0.1, verbose = TRUE ) # Summary of differential genes per cluster head(DA$diffmat_n) # Differential analysis head(DA$res) # Did the clustering pass the minimum percent of cell assigned threshold ? print(DA$passing_min_pct_cell_assigned) # Find differentiated clusters in Seurat object using Seurat function data("Seu") DA = find_differentiated_clusters( Seu, differential_function = differential_Seurat, logFC.th = log2(1.5), qval.th = 0.01, by = "seurat_clusters", limit = 5, cluster_of_origin = "Alpha", min_frac_cell_assigned = 0.1, verbose = TRUE ) # Find differentiated clusters in Seurat object using Seurat function, # passing additional arguments to differential_Seurat and thus # Seurat::FindAllMarkers funtion. data("Seu") DA = find_differentiated_clusters( Seu, differential_function = differential_Seurat, logFC.th = log2(1.5), qval.th = 0.01, by = "seurat_clusters", limit = 5, cluster_of_origin = "Alpha", min_frac_cell_assigned = 0.1, verbose = TRUE, test.use = "roc" # additional argument ) } # Find differentiated clusters in SingleCellExperiment object using # differential_ChromSCape function. if(requireNamespace("ChromSCape", quietly=TRUE)){ data("scExp") scExp = ChromSCape::find_clusters_louvain_scExp(scExp, resolution = 0.1) DA = find_differentiated_clusters( scExp, differential_function = differential_ChromSCape, logFC.th = log2(5), qval.th = 0.01, by = "IDcluster", limit = 5, cluster_of_origin = "Alpha", min_frac_cell_assigned = 0.1, verbose = TRUE, ) }
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