scvr-prep 1.1.1

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 set n_components=3 in sc.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 set n.components = 3 in pbmc <- 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.