scRNA-Seq

scRNA-Seq applies transfer learning to improve clustering of single-cell RNA sequencing (scRNA-seq) data for analysis of cellular heterogeneity.

Key Features:

• Transfer Learning Integration: Implements transfer learning by transferring models or learned patterns from well-characterized source datasets to improve clustering performance on target scRNA-seq datasets.
• Python-Based Framework: Delivered as a Python package for integration into bioinformatics workflows and computational pipelines.
• Artificial Data Generation: Includes methods for generating synthetic single-cell RNA-seq datasets for testing and validation of clustering approaches.
• Clustering Algorithms: Provides clustering algorithms tailored to high-dimensional scRNA-seq data to identify cell populations.
• Knowledge Transfer Mechanism: Employs systematic mechanisms to transfer reference knowledge to target datasets to enhance interpretability and reliability of clustering results.

Scientific Applications:

• Cell-to-Cell Variability Analysis: Improves identification of cell populations and assessment of cell-to-cell variability to study cellular heterogeneity.
• Transcriptome Profiling: Supports transcriptome analysis workflows and is compatible with protocols such as Smart-seq2 for full-length cDNA sequencing.

Methodology:

Applies transfer learning-based clustering algorithms with iterative training on source datasets and application of trained models to target datasets, and uses synthetic data generation for validation.