CellNet is a powerful network-biology computational platform designed to assess the fidelity of cellular engineering and tissue differentiation. It works by reconstructing cell-type-specific gene regulatory networks (GRNs) using massive public RNA-Seq data from human and mouse tissues, allowing you to benchmark your engineered cells against real targets. 🚀 Getting Started Options
Depending on your technical expertise and data size, you can run CellNet through three main interfaces:
CellNet Web App: The easiest path for beginners. It lets you upload a raw expression matrix and a sample annotation table directly via a browser. Computing is handled externally on a high-performance cluster.
Cloud-Based (Amazon Web Services): Best for processing large datasets without local hardware. You can launch a pre-configured CellNet Amazon Machine Image (AMI) on high-memory instances like c3.4xlarge or c3.8xlarge.
Local R Package: Ideal for bioinformatics power users. You can install the code repository directly from GitHub via the devtools package in R. 💡 Tips and Tricks for Setup
Streamline Cloud Deployment: If you use the cloud version, deploy the template to instantly launch an EC2 instance pre-packaged with the web server. Skip the intermediate configuration pages to save setup time.
Speed Up Web App Uploads: The most time-consuming phase of the web app workflow is file transfer. To accelerate this, compress your sequencing files into a single archive before uploading, or point the application directly to an Amazon S3 folder path.
Automate Result Retrieval: Because complex network analyses take anywhere from several minutes to an hour, always input a valid email address on the CellNet Cloud Homepage. The platform will automatically email you when the computation completes. 🛠️ Data Preparation Best Practices
To ensure CellNet outputs accurate biological insights, your input files must follow strict formatting standards:
Verify Your Species: Double-check that your input matrix aligns with the supported species profiles. You must explicitly specify whether your transcriptomic data is originating from Human or Mouse.
Build a Clean Metadata Table: Create a comma-separated (.csv) spreadsheet detailing sample annotations. Format it precisely according to the CellNet Tutorial Guidelines to avoid parsing errors.
Download Local Transcriptome Indices: If you run CellNet locally via R, execute the cn_setup(local=TRUE) function first. Use the built-in fetchIndexHandler to grab the required pre-built Salmon transcriptome index files for your target organism. 📊 Understanding the Outputs
Once the platform completes the pipeline, you will receive several core figures and tables that diagnose your engineered cells:
Classification Scores: A data table scoring how closely your cells resemble the targeted 16 primary mouse or human cell/tissue types.
GRN Establishment Scores: Metrics tracking whether the core gene regulatory networks specific to your target cell type are fully activated.
Network Influence Scores: A vital predictive metric pointing out exactly which transcription factors are aberrantly regulated, offering direct engineering hypotheses to improve your derivation protocols. CahanLab/CellNet_Cloud: Documentation for … – GitHub
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