API

Import SuperSCC as:

import SuperSCC

Feature selection

SuperSCC.feature_selection.feature_selection(...)

A function to do feature seletion based on filtering, embedding and wrapping method respectively or combing those methods together.

SuperSCC.feature_selection.find_signature_genes(data)

A simple wrapper of feature_selection function for finding highly variable genes for overall clusters or for markers of the corresponding cluster.

SuperSCC.feature_selection.find_markers_ovr(data)

A simple wrapper of fing_signature_genes function to run in one vs rest mode.

Clustering

SuperSCC.clustering.global_consensus_cluster(data)

A function to merge global clusters and find markers for each global cluster.

SuperSCC.clustering.sub_consensus_cluster(...)

A function to merge sub clusters in different global clusters and find markers for corresponding sub cluster.

Label transfer

SuperSCC.label_transfer.model_training(data, ...)

A function to do model training based on selected features and model.

SuperSCC.label_transfer.predict_label(...[, ...])

A function to predict label and score the prediction by using pre-trained model.

Gene module

SuperSCC.gene_module.get_gene_module(data[, ...])

A function to iteratively find gene modules from a collection of gene sets.

SuperSCC.gene_module.compare_gene_modules(...)

Compare two gene modules and analyze their similarities and differences.

SuperSCC.gene_module.analyse_one_gene_module(...)

Analyze a single gene module using the DeepSeek model

Retrival augment generation

SuperSCC.rag.SimpleRAG(file_path, file_type)

A class that encapsulates a complete Retrieval-Augmented Generation (RAG) pipeline, from data loading and processing to answer generation and citation

SuperSCC.rag.SimpleRAG.run_rag(...[, ...])

Executes the entire RAG pipeline from scratch: loading, splitting, encoding, and creating the chain.

SuperSCC.rag.SimpleRAG.get_answer(gene_list)

The main entry point for asking a question.

SuperSCC.rag.SimpleRAG.update_rag_chain([...])

Updates components of the existing RAG chain, such as the LLM or prompt.

SuperSCC.rag.ConnectRAG(host, api_key, ...)

Establishes a connection to the Qdrant vector store using the provided details.

SuperSCC.rag.ConnectRAG.connect_client([...])

Establishes a connection to the Qdrant vector store using the provided details.

SuperSCC.rag.ConnectRAG.run_rag(llm_model, ...)

Initializes the RAG chain using the established connection to the vector store.