single cell VR preprocess
Project description
SingleCellVR Preprocess:
Prepare your data for the visualization on Single Cell VR website https://singlecellvr.com/
Installation
Install and update using pip:
pip install scvr-prep
Usage
$ scvr_prep --help
Usage: scvr_prep [-h] -f FILE -t {paga,seurat,stream} [-a ANNOTATIONS] [-g GENES] [-o OUTPUT]
scvr_prep Parameters
required arguments:
-f FILE, --filename FILE
Analysis result file name (default: None)
-t {paga,seurat,stream}, --toolname {paga,seurat,stream}
Tool used to generate the analysis result (default: None)
optional arguments:
-a ANNOTATIONS, --annotations ANNOTATIONS
Annotation file name. It contains the cell
annotation(s) used to color cells (default: None)
-g GENES, --genes GENES
Gene list file name. It contains the genes to
visualize in one column (default: None)
-o OUTPUT, --output OUTPUT
Output folder name (default: vr_report)
-h, --help show this help message and exit
Examples:
PAGA:
To get single cell VR report for PAGA :
scvr_prep -f ./paga_result/paga3d_paul15.h5ad -t paga -a annotations.txt -g genes.txt -o paga_report
- Input files can be found here
- To generate the
paga3d_paul15.h5ad
, check out PAGA analysis. (Make sure setn_components=3
insc.tl.umap(adata,n_components=3)
)
Seurat:
To get single cell VR report for Seurat :
scvr_prep -f ./seurat_result/seurat3d_10xpbmc.loom -t seurat -a annotations.txt -g genes.txt -o seurat_report
- Input files can be found here
- To generate the
seurat3d_10xpbmc.loom
, check out Seurat analysis. (Make sure setn.components = 3
inpbmc <- RunUMAP(pbmc, dims = 1:10, n.components = 3)
)
STREAM:
To get single cell VR report for STREAM :
scvr_prep -f ./stream_result/stream_nestorowa16.pkl -t stream -g genes.txt -o stream_report
- Input files can be found here
- To generate the
stream_nestorowa16.pkl
, check out STREAM analysis.
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