CausalDesign-GPT: Optimize Perturb-seq for Causal Discovery
Your causal-inference planner for designing perturb-seq experiments to maximize information gain about gene regulatory networks, inspired by Caroline Uhler.
Access CausalDesign-GPTWhat is CausalDesign-GPT?
CausalDesign-GPT is a sophisticated AI planner dedicated to optimizing perturb-seq experiments (single-cell CRISPR perturbation + profiling). It empowers researchers to design experiments that maximize information gain about gene regulatory networks, focusing on robust causal inference to distinguish direct versus indirect genetic effects and understand complex biological systems.
Design Experiments for Maximum Causal Insight
Causal Inference Guidance
Explains how specific perturbation designs enable causal inference, clarifying why multiple perturbations are needed to distinguish direct vs. indirect effects.
Optimal Guide Set Design
Suggests minimal guide sets that still yield strong causal insights, helping to make experiments more efficient and cost-effective.
Interpret Causal Graphs (DAGs)
Helps users understand directed acyclic graph (DAG) outputs from analysis, clarifying cause-effect relationships between genes.
Simulate & Optimize Designs
Simulates perturb-seq outcomes, computes information gain or identifiability metrics, and recommends guide adjustments to improve causal resolution.
Clear Design Rationale
Provides Markdown rationale for designs, tables comparing options, lists of guides with expected benefits, and JSON/edge lists for causal graphs.
Secure & Ethical Planning
Masks proprietary identifiers, cautions on re-identification risks with human data, and ensures only necessary, anonymized information is shared.
Ready to Uncover Causal Gene Networks?
Leverage CausalDesign-GPT to design perturb-seq experiments that provide maximal causal insights into gene regulatory networks.
Plan Your Causal Experiment