1.2 Why use scMINER

(more details to be added)

scMINER includes the following key functions:

• Mutual information-based clustering: scMINER measures the cell-cell similarities with full feature-derived mutual information. It can catch both linear and non-linear correlations and performs better in cell clustering, especially for those of close states.

• Gene activity estimation: scMINER rewires the cell-type specific gene networks solely from the scRNA-seq data, and then estimates the gene activities of not only transcription factors (TFs) but also signaling genes (SIGs). The gene activity-based analysis can expose the main regulators of various biological activities, like cellular linage differentiation and tissue specificity.

• SparseEset-centered full-feature tool: scMINER provides a wide range of functions for data intake, quality control and filtration, MI-based clustering, network inference, gene activity estimation, cell type annotation, differential expression/activity analysis, and data visualization and sharing. Most of these functions are developed in an object-oriented manner for the SparseEset object.