SeuratClusteringOfAllCells

SeuratClusteringOfAllCells¶

Cluster all cells, including T cells and non-T cells using Seurat

This process will perform clustering on all cells using Seurat package.
The clusters will then be used to select T cells by TCellSelection process.

Note

If all your cells are all T cells (TCellSelection 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¶

rdsfile: Default: {{in.srtobj | stem}}.RDS.
The seurat object with cluster information at seurat_clusters If SCTransform was used, the default Assay will be reset to RNA.

Environment Variables¶

ncores (type=int;order=-100): Default: 1.
Number of cores to use.
Used in future::plan(strategy = "multicore", workers = <ncores>) to parallelize some Seurat procedures.
See also: https://satijalab.org/seurat/articles/future_vignette.html
ScaleData (ns): Arguments for ScaleData().
If you want to re-scale the data by regressing to some variables, Seurat::ScaleData will be called. If nothing is specified, Seurat::ScaleData will not be called.
- vars-to-regress: The variables to regress on.
- <more>: See https://satijalab.org/seurat/reference/scaledata
SCTransform (ns): Arguments for SCTransform().
If you want to re-scale the data by regressing to some variables, Seurat::SCTransform will be called. If nothing is specified, Seurat::SCTransform will not be called.
- vars-to-regress: The variables to regress on.
- <more>: See https://satijalab.org/seurat/reference/sctransform
RunUMAP (ns): Arguments for RunUMAP().
object is specified internally, and - in the key will be replaced with ..
dims=N will be expanded to dims=1:N; The maximal value of N will be the minimum of N and the number of columns - 1 for each sample.
- dims (type=int): Default: 30.
  The number of PCs to use
- reduction: The reduction to use for UMAP.
  If not provided, sobj@misc$integrated_new_reduction will be used.
- <more>: See https://satijalab.org/seurat/reference/runumap
FindNeighbors (ns): Arguments for FindNeighbors().
object is specified internally, and - in the key will be replaced with ..
- reduction: The reduction to use.
  If not provided, sobj@misc$integrated_new_reduction will be used.
- <more>: See https://satijalab.org/seurat/reference/findneighbors
FindClusters (ns): Arguments for FindClusters().
object is specified internally, and - in the key will be replaced with ..
The cluster labels will be saved in seurat_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 generate 0.1, 0.2, 0.3, 0.4, 0.5. The step can be omitted, defaulting to 0.1.
  The results will be saved in seurat_clusters_<resolution>.
  The final resolution will be used to define the clusters at seurat_clusters.
- <more>: See https://satijalab.org/seurat/reference/findclusters
cache (type=auto): Default: /tmp.
Whether to cache the information at different steps.
If True, the seurat object will be cached in the job output directory, which will be not cleaned up when job is rerunning.
The cached seurat object will be saved as <signature>.<kind>.RDS file, where <signature> is the signature determined by the input and envs of the process.
See https://github.com/satijalab/seurat/issues/7849, https://github.com/satijalab/seurat/issues/5358 and https://github.com/satijalab/seurat/issues/6748 for more details also about reproducibility issues.
To not use the cached seurat object, you can either set cache to False or delete the cached file at <signature>.RDS in the cache directory.