After you install Single cell explorer, MOngoDB, and python notebook, and pipeline such as Cell ranger, you have a operation streameline system for your lab.You can run your prefered pipelines to generate counts data first. Or you can even use our example codes to call Cell Ranger pipelines to process FASTQ files (you need to import our library scpipeline to do that). Then you can use our example code (PBMC10k) to run "seurat" like analysis using scanpy packages. At last, you can save he result to database. The result swill be automatically posted on Single cell Explorer website you hosted. Scientists can take an initial analysis to check if the prelimianry analysis (most likey you will use default seetings) satisfy the needs of analysis.
Yes, we demonstrated how to use Jupyter Notebook to communicate with Single Cell Explorer system on data analysis section. You can find more about why Jupyter is the computational notebook of choice for data scientists here . For R users, you can use http API to retrieve data from Single Cell Explorer and run your statistical analysis such as EdgeR or DESeq2. We also published the approach to convert Seurat result into csv files, followed by python scripts to load into database.
The current application is able to host data from different species. In addition to human blood and tissue samples, we added non-standard model organisms such as Drosophila in our software demo website.
Django is a high-level Python Web framework that enables rapid development and clean, pragmatic design.
With tools including Django ORM, Middlewares, Authentication, HTTP libraries, Multi-site support,
i18n, Django Admin, and a template engine, it removes the hassle in Web development, database integration, and content
management. We chose Django to make the development journey easier for the future.
While Shiny is particularly great for fast prototyping and fairly easy to use, it has problems. R stack is weak in retrieving and saving data to the database and in multipage support.
Better concurrent user support requires Shiny Server Pro.
Also, it is hard to add extra functionality not already in the package or Dash.
Cellxgene is an interactive data explorer for checking single-cell transcriptomics datasets that are already well curated.That serves the purpose for Chan Zuckerberg Initiative (Human Cell Atlas). Single Cell Explorer was built for scientists to label, annotate, and share their findings so that experimentla scientists will be able to participate data mining more proactively. For Cellxgene, the time required to load h5ad files for a particular dataset or map to cellxgene makes it difficult for it to serve as a data portal for more datasets with concurrent users. Single Cell Explorer was built for supporting general purpose ( multiple datasets, multiple species, flexibility for intergration).
| Function | Cellxgene | Single Cell Explorer |
|---|---|---|
| Multiple datasets | Not yet, needs to load h5ad each time | Data portal host studies with multiple samples |
| Manual label and Annotattion | No | Yes |
| Gene Search & Visualization | Yes | Yes |
| Dataset registration | No | Data content managment |
| API and Re-analysis | No | Yes |
Please follow our installation tutorial step by step. If you want to use MongoDB to host your dataset, we recommend your learn basic operation skills for MongoDB. A Docker solution does not make the task easy at this moment. If you run MongoDB in a container (e.g. Docker, etc.), you need to set you need to set storage.wiredTiger.engineConfig.cacheSizeGB if it does not have access to all of the RAM available in a system. The conda installation method is not currently available.