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Integration with Jupyter Notebook

Please follow the instructions to install Jupyter Notebook. You can use walkthrough examples to start.

For more information about Notebook, you can visit jupyter for more information about Python Notebook, and scanpy for single cell analysis using Python.

Walkthrough Examples

1) Run cell ranger (advanced user) from python notebook to generate counts data: Run cell ranger.

2) Analyze using counts matrix (10X Genomic V3 Use), and load results into single cell explorer database (MongoDB): PBMC10k example for Python Notebook user.

The example demonstrated -- the analysis of 10X RNAseq using scanpy module -- Need of parameter optimization for leiden algorhism to find right cell clusters -- UMAP or t-SNE can be used by experimental scientists to annotate cells from their perspectives or goals

3) R user: Import counts table and tsne/umap coordinate from Seurat object or loom files: Import Data.

The example demonstrated -- extract results from Seurat object to single cell explorer -- Loom files support

4) Retrieve annoated cells using API and identify differentially expressed genes: DEG analysis (using API).

The example demonstrated -- use http API from application url to retrieve cell types and normalized expression matrix from single cell explorer database -- find differentially expressed genes