ascend

ascend provides an R package for analysis of single-cell RNA sequencing (scRNA-seq) data, enabling quality control and filtering, normalization, dimensionality reduction (including PCA and t-SNE), clustering, differential expression analysis, visualization, and integration with other bioinformatics tools for biological interpretation.

Key Features:

• Quality Control and Filtering: Implements cell- and gene-level quality control and filtering to identify and remove low-quality cells or genes based on QC metrics.
• Normalization: Provides normalization methods to correct for technical biases such as differences in sequencing depth across samples.
• Dimensionality Reduction: Includes established and additional dimensionality reduction methods, explicitly supporting PCA and t-SNE for visualizing cellular heterogeneity.
• Clustering: Offers clustering algorithms to group cells by gene expression profiles and identify distinct cell types or states.
• Differential Expression Analysis: Supplies tools to identify differentially expressed genes between defined groups of cells.
• Visualization: Contains functions to plot QC metrics, dimensionality reduction results, clustering outcomes, and differential expression findings.
• Integration with Other Tools: Integrates with other bioinformatics tools and native R functions to incorporate additional analyses or custom scripts.
• Computational Efficiency: Implements fast parallel computation and efficient memory management to accommodate large-scale scRNA-seq datasets.

Scientific Applications:

• Developmental biology: Profiling cell-type composition and inferring lineage relationships from scRNA-seq data.
• Immunology: Characterizing immune cell populations and transcriptional states using scRNA-seq.
• Oncology: Assessing tumor heterogeneity and identifying tumor cell states and marker genes with scRNA-seq.
• Neuroscience: Analyzing neuronal and glial cell-type composition and transcriptional states from scRNA-seq datasets.

Methodology:

Explicit computational methods include cell- and gene-level QC and filtering, normalization to correct sequencing depth and technical variability, dimensionality reduction (PCA, t-SNE), clustering algorithms, differential expression analysis, visualization functions, integration with R tools, and use of parallel computation and memory management; statistical approaches address sparsity, noise, and technical variability in scRNA-seq data.