singlecellvr

Single cell visualization using Virtual Reality (VR)

http://www.singlecellvr.com/

SingleCellVR can be used with our preprocessed datasets found at the link above or by following the steps below to process your own dataset.

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

Usage

$ scvr --help

usage: scvr [-h] -f FILE -t {scanpy,paga,seurat,stream} -a ANNOTATIONS [-g GENES] [-o OUTPUT]

scvr Parameters

required arguments:
  -f FILE, --filename FILE
                        Analysis result file name (default: None)
  -t {scanpy,paga,seurat,stream}, --toolname {scanpy,paga,seurat,stream}
                        Tool used to generate the analysis result (default: None)
  -a ANNOTATIONS, --annotations ANNOTATIONS
                        Annotation file name. It contains the cell annotation key(s) 
                        to visualize in one column (default: None)
                        
optional arguments:

  -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: scvr_report)
  -h, --help            show this help message and exit

Examples:

Scanpy:

To get single cell VR report for Scanpy :

scvr -f ./scanpy_result/scanpy_10xpbmc.h5ad -t scanpy -a annotations.txt -g genes.txt -o scanpy_report

• Input files can be found here
• To generate the scanpy_10xpbmc.h5ad, check out Scanpy analysis. (Make sure set n_components=3 in sc.tl.umap(adata,n_components=3))

PAGA:

To get single cell VR report for PAGA :

scvr -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 -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 -f ./stream_result/stream_nestorowa16.pkl -t stream -a annotations.txt -g genes.txt -o stream_report

• Input files can be found here
• To generate the stream_nestorowa16.pkl, check out STREAM analysis.

Or use STREAM package, e.g.:

import stream as st
st.save_vr_report(adata,
                  ann_list=['label','kmeans','branch_id_alias','S4_pseudotime'],
                  gene_list=['Gata1','Car2','Epx','Mfsd2b','Mpo','Emb','Flt3','Dntt'],
                  file_name='stream_report')