GRN-guided simulator for single-cell RNA-seq data using causal GANs. Long Description (required)
GRouNdGAN is a reference-based causal implicit generative model that simulates single-cell RNA-seq data while imposing a user-defined causal gene regulatory network as regulatory ground truth. single-cell RNA-seq; gene regulatory network; GAN; causal model; synthetic transcriptomics; Python https://emad-combine-lab.github.io/GRouNdGAN/
Step 1: Use the attribute tree to add new attributes or remove pre-selected attributes to describe the simulator.
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Summary of Proposed Changes Step 2: Review list of proposed attribute addition(s) and subtraction(s).
Can't Find the Attribute You Are Looking For? If you would like to propose an attribute that you cannot find in the tree above, or if you would like to add a clarification to one or more attributes for this simulator (e.g. a specific file format for attribute /Output/File Format/Other), please list them in the Additional Comment box of the Submit tab .
Summary of Proposed Changes Current Citations/Applications