Welcome to MICTI’s documentation!

Recent advances in single-cell gene expression profiling technology have revolutionized the understanding of molecular processes underlying developmental cell and tissue differentiation, enabling the discovery of novel cell types and molecular markers that characterize developmental trajectories. Common approaches for identifying marker genes are based on pairwise statistical testing for differential gene expression between cell types in heterogeneous cell populations, which is challenging due to unequal sample sizes and variance between groups resulting in little statistical power and inflated type I errors.

We developed an alternative feature extraction method, Marker gene Identification for Cell Type Identity (MICTI), that encodes the cell-type specific expression information to each gene in every single cell. This approach identifies features (genes) that are cell-type specific for a given cell-type in heterogeneous cell population.

Contents:

• Installation
• User’s Guide
  • Creat MICTI Object
  • Data visualisation
  • Clustering cells
  • Cell-type marker genes
  • significant cluster markers
  • Gene-list enrichment analysis
• Tutorials
  • Import MICTI module
  • Import data
  • Creating MICTI object for known cell-types
  • Lower dimensional data visualization
  • Creating MICTI object for clustering cells into pre-defined k clusters