scRNABatchQC

scRNABatchQC performs quality control and comparative analysis of multiple single-cell RNA sequencing (scRNA-seq) datasets to distinguish technical artifacts from biological variation.

Key Features:

• Multi-Sample Comparison: Performs simultaneous comparison across multiple scRNA-seq sample sets to identify systematic biases and batch effects.
• Comprehensive QC Report Generation: Generates an HTML-based QC report containing metrics and visualizations of technical and biological features across datasets.
• Support for Diverse Data Formats: Accepts gene-cell count matrices, 10x Genomics datasets, SingleCellExperiment objects, and Seurat v3 objects as inputs.
• Detection of Biases and Outliers: Examines consistency across datasets to detect biases and outlier cells that may affect downstream analysis.
• Identification of Systematic Variability Sources: Characterizes sources of variability within single-cell transcriptome data to help separate technical artifacts from biological variation.

Scientific Applications:

• Quality Assurance: Assess scRNA-seq dataset quality before downstream analyses such as clustering or differential expression.
• Batch Effect Mitigation: Identify and characterize batch effects across datasets to inform normalization and correction strategies.
• Outlier Detection: Detect and flag low-quality or anomalous cells to refine datasets prior to analysis.

Methodology:

Leverages technical and biological features inherent in scRNA-seq data, provides visualizations and metrics, and applies robust statistical analyses to disentangle technical noise from biological signal.