SeuratClusteringOfAllCells¶
Cluster all cells, including T cells/non-T cells and B cells/non-Bcells using Seurat.
This process will perform clustering on all cells using
Seurat
package.
The clusters will then be used to select T/B cells by
TOrBCellSelection
process.
Note
If all your cells are all T/B cells (TOrBCellSelection
is not set in configuration), you should not use this process.
Instead, you should use SeuratClustering
process
for unsupervised clustering, or SeuratMap2Ref
process
for supervised clustering.
Input¶
srtobj
: The seurat object loaded by SeuratPreparing
Output¶
outfile
: Default:{{in.srtobj | stem}}.qs
.
The seurat object with cluster information atseurat_clusters
.
Environment Variables¶
ncores
(type=int;order=-100
): Default:1
.
Number of cores to use.
Used infuture::plan(strategy = "multicore", workers = <ncores>)
to parallelize some Seurat procedures.
See also: https://satijalab.org/seurat/articles/future_vignette.htmlRunUMAP
(ns
): Arguments forRunUMAP()
.
object
is specified internally, and-
in the key will be replaced with.
.
dims=N
will be expanded todims=1:N
; The maximal value ofN
will be the minimum ofN
and the number of columns - 1 for each sample.dims
(type=int
): The number of PCs to usereduction
: The reduction to use for UMAP.
If not provided,sobj@misc$integrated_new_reduction
will be used.<more>
: See https://satijalab.org/seurat/reference/runumap
RunPCA
(ns
): Arguments forRunPCA()
.FindNeighbors
(ns
): Arguments forFindNeighbors()
.
object
is specified internally, and-
in the key will be replaced with.
.reduction
: The reduction to use.
If not provided,sobj@misc$integrated_new_reduction
will be used.<more>
: See https://satijalab.org/seurat/reference/findneighbors
FindClusters
(ns
): Arguments forFindClusters()
.
object
is specified internally, and-
in the key will be replaced with.
.
The cluster labels will be saved inseurat_clusters
and prefixed with "c".
The first cluster will be "c1", instead of "c0".resolution
(type=auto
): Default:0.8
.
The resolution of the clustering. You can have multiple resolutions as a list or as a string separated by comma.
Ranges are also supported, for example:0.1:0.5:0.1
will generate0.1, 0.2, 0.3, 0.4, 0.5
. The step can be omitted, defaulting to 0.1.
The results will be saved inseurat_clusters_<resolution>
.
The final resolution will be used to define the clusters atseurat_clusters
.<more>
: See https://satijalab.org/seurat/reference/findclusters
cache
(type=auto
): Default:/tmp
.
Where to cache the information at different steps.
IfTrue
, the seurat object will be cached in the job output directory, which will be not cleaned up when job is rerunning.
Set toFalse
to not cache the results.