HIPPO

HIPPO selects features and clusters cells simultaneously for single-cell UMI data from scRNA-seq experiments.

Key Features:

• Novel Feature Selection: Selects features using zero inflation instead of gene variance.
• Simultaneous Processing: Performs feature selection and cell clustering in a single integrated step.
• Computational Efficiency: Achieves faster computation than existing methods by relying on PCA and K-means rather than graph-clustering or consensus clustering.

Scientific Applications:

• scRNA-seq Analysis: Processes single-cell UMI count data for downstream biological interpretation.

Methodology:

HIPPO applies PCA followed by K-means clustering, using zero inflation for feature selection rather than gene variance.