Research Preview: AI-Driven Single-Cell Analysis

One of the core pillars of 01-Bio-Research is the analysis of high-dimensional single-cell RNA sequencing (scRNA-seq) data. Today, we are sharing a preview of our automated analysis pipeline.

The Challenge

Traditional scRNA-seq analysis involves multiple manual steps: quality control, dimensionality reduction, clustering, and annotation. This manual process is time-consuming and prone to bias.

Our Approach: The Automated Pipeline

We have developed a Python-based pipeline leveraging Scanpy and custom AI models to streamline this workflow.

Key Components

1. Preprocessing: Automatic filtering of low-quality cells and normalization using pooled size factors.
2. Integration: Batch effect correction using Harmony/BBKNN to merge datasets from different donors.
3. Diagnosis: An AI module (shawn_bot.bio) that predicts cell types based on marker gene expression profiles.

# Pseudo-code example of our pipeline
import scanpy as sc
from shawn_bot.bio import AutoAnnotator

def analyze_sample(adata):
    # Preprocessing
    sc.pp.normalize_total(adata)
    sc.pp.log1p(adata)
    
    # Dimension Reduction
    sc.tl.pca(adata)
    sc.pp.neighbors(adata)
    sc.tl.umap(adata)
    
    # AI Annotation
    annotator = AutoAnnotator(model='immune_v2')
    adata.obs['predicted_cell_type'] = annotator.predict(adata)
    
    return adata

Future Work

We are currently validating this pipeline against manually annotated datasets. Initial results show a 95% concordance rate in major cell type identification.

Full detailed reports and the source code will be available in the Research section soon.

References

• Scanpy Documentation
• Harmony (Korsunsky et al., 2019)
• BBKNN Documentation
• UMAP Paper (McInnes et al., 2018)