$Rscript scripts/seurat_sc_cluster.r -h
usage: scripts/seurat_sc_cluster.r [-h] -i filepath [-d dim]
[--resolution resolution [resolution ...]]
[--umap.method umap.method]
[--tsne.method tsne.method]
[--high.variable.genes high.variable.genes]
[--vars.to.regress vars.to.regress [vars.to.regress ...]]
[--sctransform]
[--integrate.method integrate.method]
[--integration.reduction integration.reduction]
[--k.anchor k.anchor] [-b batch.id]
[--downsample downsample]
[--pt.size pt.size] [-H height] [-W width]
[-o path] [-p prefix]
Seurat single cell analysis : https://nbisweden.github.io/workshop-
scRNAseq/labs/compiled/seurat/seurat_07_spatial.html
optional arguments:
-h, --help show this help message and exit
-i filepath, --rds filepath
input single Seurat obj in rds file [default NULL]
-d dim, --dim dim set pca dim number to find cluster [default 20]
--resolution resolution [resolution ...]
set resolution parameters [default 0.5]
--umap.method umap.method
UMAP implementation to run. Can be uwot:Runs umap via
the uwot R package; uwot-learn:Runs umap via the uwot
R package and return the learned umap model;umap-
learn:Run the Seurat wrapper of the python umap-learn
package [default uwot]
--tsne.method tsne.method
Select the method to use to compute the tSNE.
Available methods are:Rtsne: Use the Rtsne package
Barnes-Hut implementation of tSNE (default) ;FIt-SNE:
Use the FFT-accelerated Interpolation-based t-SNE.
Based on Kluger Lab code found here:
https://github.com/KlugerLab/FIt-SNE [default Rtsne]
--high.variable.genes high.variable.genes
highly variable gene number through the
FindVariableFeatures function (identify variable
features based on the variance stabilization
transformation (“vst”)) [default 3000]
--vars.to.regress vars.to.regress [vars.to.regress ...]
Variables to regress out in ScaleData function
(previously latent.vars in RegressOut). For example,
nUMI, or percent.mito. [default None]
--sctransform If set, use SCTransform normalization [optional,
default: False]
--integrate.method integrate.method
set integrated method , harmony or seuratIntegration
[default None]
--integration.reduction integration.reduction
Dimensional reduction to perform when finding anchors
for seuratIntegration method. Can be one of:
cca,rpca,rlsi [default cca]
--k.anchor k.anchor You can increase the strength of alignment by
increasing the k.anchor parameter, which is set to 5
by default. Increasing this parameter to 20 will
assist in aligning these populations [default 5]
-b batch.id, --batch.id batch.id
set batch column name in meta data for integrated
[default None]
--downsample downsample
subset cells numbers for analysis [default None]
--pt.size pt.size the point size [default 1]
-H height, --height height
the height of pic inches [default 7]
-W width, --width width
the width of pic inches [default 6]
-o path, --outdir path
output file directory [default
/share/nas5/huangls/test/10X.sc.test/pbmc]
-p prefix, --prefix prefix
out file name prefix [default demo]
