Introduction to Optional Agents

Thank you for reading this far! The basic CASSIA workflow is sufficient for most situations. However, to handle some special cases, we now introduce several advanced agents.

🤔 Uncertainty Quantification Agent (UQ Agent)

Large language models can make mistakes; if we assume the model has an 80% chance of answering correctly each time, then by asking the model the same question five times and taking the most frequent answer, the probability of getting the correct answer theoretically increases to 94.2%!

The UQ agent mainly provides this functionality, making CASSIA's answers more accurate and reliable.

🚀 Annotation Boost Agent

This agent has appeared in the previous workflow, but you can also choose to apply it to clusters of your choice. The Annotation Boost Agent can read CASSIA's annotation reports, then continuously generate hypotheses and retrieve gene expression information from clusters to verify these hypotheses, ultimately optimizing the annotation results. In our tests, this agent performed exceptionally well and is one of CASSIA's core innovations.

⚖️ Comparecelltype Agent

We know that the basic CASSIA workflow outputs three possible final annotations, ranked from most to least likely. Sometimes, we encounter ambiguous annotations. In such cases, the Comparecelltype Agent can help identify the most probable annotation. This agent is actually a combination of several agents, each scoring the possible annotation results. The final annotation is chosen based on these scores.

🔬 Subclustering Agent

A single round of clustering is often not enough. Sometimes, we are particularly interested in certain clusters that need to be extracted for further analysis. The Subclustering Agent can compare multiple clusters simultaneously, providing more accurate and detailed annotations.

✨ Annotation Boost Agent +

This agent is an upgraded version of the Annotation Boost Agent, capable of performing personalized analyses for specific clusters. It is still under continuous development, so please stay tuned...

📚 RAG Agent (Retrieval-Augmented Generation Agent)

This agent is one of CASSIA's core innovations. Based on the basic information provided by the user, it automatically retrieves marker databases, cell ontology trees, and uses a PCA-based approach on cell types to generate a series of background information to assist the basic CASSIA workflow. When very detailed or novel annotations are needed, this agent significantly improves CASSIA's performance. Due to library dependencies, the RAG Agent is currently only available in Python. We will release an R version as soon as possible.