Metadata-Version: 2.1
Name: scvr_prep
Version: 1.1.1
Summary: single cell VR preprocess
Home-page: https://github.com/pinellolab/singlecellvr
Author: Huidong Chen
Author-email: huidong.chen@mgh.harvard.edu
License: UNKNOWN
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 :  
        ```bash
        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](https://www.dropbox.com/sh/03zpxs9zv7yusi1/AADKVSU8Il1JcjA7lfHjmRpSa?dl=0) 
        * To generate the `paga3d_paul15.h5ad`, check out [PAGA analysis](https://nbviewer.jupyter.org/github/pinellolab/singlecellvr/blob/master/examples/paga3d_paul15.ipynb?flush_cache=true). *(Make sure set `n_components=3` in `sc.tl.umap(adata,n_components=3)`)*
        
        ### Seurat:  
        To get single cell VR report for Seurat :  
        ```bash
        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](https://www.dropbox.com/sh/tpk4qfm5qsjpffn/AADmKmyDx7rhzKBOpIlAgMEUa?dl=0) 
        * To generate the `seurat3d_10xpbmc.loom`, check out [Seurat analysis](https://nbviewer.jupyter.org/github/pinellolab/singlecellvr/blob/master/examples/seurat3d_10xpbmc.ipynb?flush_cache=true). *(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 : 
        ```bash
        scvr_prep -f ./stream_result/stream_nestorowa16.pkl -t stream -g genes.txt -o stream_report
        ```
        * Input files can be found [here](https://www.dropbox.com/sh/fg84hfdeihielun/AACRcmuAIg9RMU30ChgAZevza?dl=0) 
        * To generate the `stream_nestorowa16.pkl`, check out [STREAM analysis](https://nbviewer.jupyter.org/github/pinellolab/singlecellvr/blob/master/examples/stream_nestorowa16.ipynb?flush_cache=true).
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
