alona

alona performs end-to-end analysis of single-cell RNA sequencing (scRNA-seq) data to enable quality control, normalization and batch correction, graph-based clustering, marker-gene-based cell-type annotation, and differential gene expression analysis.

Key Features:

• Flexible Analysis Pipeline: Integrates multiple single-cell analysis algorithms and exposes a Python module framework for constructing custom workflows.
• Quality Filtering: Implements filters to retain high-quality cells and features for downstream analysis.
• Normalization and Batch Correction: Applies normalization and batch-correction methods to reduce technical variability across samples.
• Clustering and Cell Type Annotation: Uses a graph-based clustering strategy and annotates clusters using an extensive collection of marker genes or user-defined markers.
• Differential Gene Expression Analysis: Identifies genes with condition- or cluster-specific expression differences.
• Data Input: Accepts compressed gene expression matrices as input.

Scientific Applications:

• Cell Type Discovery: Identification of novel cell types and cellular heterogeneity from scRNA-seq datasets.
• Cell Atlas Construction: Generation of tissue- and organ-specific cellular atlases based on clustered and annotated single-cell profiles.
• Gene Expression Dynamics: Investigation of single-cell gene expression changes across biological conditions and experimental groups.

Methodology:

Computational steps explicitly include quality filtering, normalization and batch correction, graph-based clustering for cell identification, cluster annotation using predefined or user-specified marker genes, and differential gene expression analysis, implemented via a Python module and accepting compressed gene expression matrices.