Tutorials

This tutorial goes though how to use scSemiProfiler to preprocess and semi-profile a toy dataset with 12 COIVD-19 samples from patients of 6 different severity levels, and how to perform common single-cell level downstream analyses.

• Example
  • Step 1 Initial Setup
  • Step 1.5 Acquiring Single-cell Data for Representatives
  • Step 2 Single-cell Processing & Feature Augmentation
  • Step 3 Single-cell Inference
  • Performance evaluation
    • Reconstruction
    • Target sample inference
    • Check training loss curve
  • Comprehensive evaluation using more downstream tasks
    • Assemble semi-profiled cohort
    • Read the real-profiled single-cell data to compare to
    • Compare the UMAP visualization
    • Compare cell type composition
    • Compare gene set activation pattern
    • Compare top cell type signature genes
    • Use RRHO plot to compare markers
    • Compare GO enrichment analysis similarity
    • Compare partition-based graph abstraction (PAGA) graph similarity
    • Using CellChat to perform cell-cell interaction analysis
    • Export single-cell data for analysis
  • End of comprehensive semi-profiling performance evaluation
  • Optional: New Representative Selection and Run the Next Round
  • Round 2 semi-profiling
    • select a new batch of representatives using active learning:
    • obtain single-cell data for new representatives:
    • run single-cell inference again with more representatives:
  • stop criteria
  • new representative selection using active learning
  • Round 3 semi-profiling
  • Round 4 semi-profiling
  • Round 5 semi-profiling
  • Error curve
    • compute errors of each round
    • visualize the errors