scry

scry provides implementations of count-based feature selection and dimension reduction algorithms for analyzing high-dimensional biological datasets with small counts.

Key Features:

• Count-based feature selection: Implements feature selection methods specifically developed for small count data rather than standard linear-Gaussian approaches.
• Dimension reduction: Provides dimension reduction algorithms optimized for count-based data distributions.

Scientific Applications:

• Single-cell RNA-seq analysis: Facilitates unsupervised analysis of single-cell RNA sequencing data with small counts.
• High-dimensional count data analysis: Supports unsupervised analysis of any high-dimensional biological dataset with small count values.

Methodology:

The package implements algorithms that avoid standard linear-Gaussian methods such as principal component analysis, which are not well suited for small count data.