Description Usage Arguments Details Value
Perform k=means clustering on both genes and single cells
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object |
Seurat object |
genes.use |
Genes to use for clustering |
k.genes |
K value to use for clustering genes |
k.cells |
K value to use for clustering cells (default is NULL, cells are not clustered) |
k.seed |
Random seed |
do.plot |
Draw heatmap of clustered genes/cells (default is TRUE) |
data.cut |
Clip all z-scores to have an absolute value below this. Reduces the effect of huge outliers in the data. |
k.cols |
Color palette for heatmap |
pc.row.order |
Order gene clusters based on the average PC score within a cluster. Can be useful if you want to visualize clusters, for example, based on their average score for PC1. |
pc.col.order |
Order cell clusters based on the average PC score within a cluster |
rev.pc.order |
Use the reverse PC ordering for gene and cell clusters (since the sign of a PC is arbitrary) |
use.imputed |
Cluster imputed values (default is FALSE) |
set.ident |
If clustering cells (so k.cells>0), set the cell identity class to its K-means cluster (default is TRUE) |
do.constrained |
FALSE by default. If TRUE, use the constrained K-means function implemented in the tclust package. |
... |
Additional parameters passed to kmeans (or tkmeans) |
K-means and heatmap are calculated on object@scale.data
Seurat object where the k-means results for genes is stored in object@kmeans.obj[[1]], and the k-means results for cells is stored in object@kmeans.col[[1]]. The cluster for each cell is stored in object@data.info[,"kmeans.ident"] and also object@ident (if set.ident=TRUE)
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