Description Usage Arguments Value Examples
Embeds cells in two dimensions using umap based on
a celda model. For celda_C sce
objects, PCA on the normalized counts
is used to reduce the number of features before applying UMAP. For celda_CG
sce
object, UMAP is run on module probabilities to reduce the number
of features instead of using PCA. Module probabilities are squareroot
transformed before applying UMAP.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  celdaUmap(sce, ...)
## S4 method for signature 'SingleCellExperiment'
celdaUmap(
sce,
useAssay = "counts",
altExpName = "featureSubset",
maxCells = NULL,
minClusterSize = 100,
modules = NULL,
seed = 12345,
nNeighbors = 30,
minDist = 0.75,
spread = 1,
pca = TRUE,
initialDims = 50,
normalize = "proportion",
scaleFactor = NULL,
transformationFun = sqrt,
cores = 1,
...
)

sce 
A SingleCellExperiment object returned by celda_C, celda_G, or celda_CG. 
... 
Additional parameters to pass to umap. 
useAssay 
A string specifying which assay slot to use. Default "counts". 
altExpName 
The name for the altExp slot to use. Default "featureSubset". 
maxCells 
Integer. Maximum number of cells to plot. Cells will be
randomly subsampled if 
minClusterSize 
Integer. Do not subsample cell clusters below this threshold. Default 100. 
modules 
Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL. 
seed 
Integer. Passed to with_seed. For reproducibility, a default value of 12345 is used. If NULL, no calls to with_seed are made. 
nNeighbors 
The size of local neighborhood used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. Default 30. See umap for more information. 
minDist 
The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. Default 0.75. See umap for more information. 
spread 
The effective scale of embedded points. In combination with

pca 
Logical. Whether to perform
dimensionality reduction with PCA before UMAP. Only works for celda_C

initialDims 
Integer. Number of dimensions from PCA to use as
input in UMAP. Default 50. Only works for celda_C 
normalize 
Character. Passed to normalizeCounts in normalization step. Divides counts by the library sizes for each cell. One of 'proportion', 'cpm', 'median', or 'mean'. 'proportion' uses the total counts for each cell as the library size. 'cpm' divides the library size of each cell by one million to produce counts per million. 'median' divides the library size of each cell by the median library size across all cells. 'mean' divides the library size of each cell by the mean library size across all cells. 
scaleFactor 
Numeric. Sets the scale factor for celllevel
normalization. This scale factor is multiplied to each cell after the
library size of each cell had been adjusted in 
transformationFun 
Function. Applys a transformation such as 'sqrt',
'log', 'log2', 'log10', or 'log1p'. If 
cores 
Number of threads to use. Default 1. 
sce
with UMAP coordinates
(columns "celda_UMAP1" & "celda_UMAP2") added to
reducedDim(sce, "celda_UMAP")
.
1 2  data(sceCeldaCG)
umapRes < celdaUmap(sceCeldaCG)

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