Single-Cell Transcriptomics#
Tutorials for the complete single-cell workflow: alignment, preprocessing, annotation, trajectory analysis, cell-structure analysis, velocity, and multi-omics.
- Alignment
- Preprocessing
- Preprocessing the data of scRNA-seq with omicverse[CPU-GPU-mixed]
- Preprocessing the data of scRNA-seq with omicverse[GPU]
- Preprocessing the data of scRNA-seq [Rust / out-of-memory]
- Removing ambient / contamination RNA from droplet scRNA-seq
- Clustering space
- GeneModule Identified
- Lazy analysis of scRNA-seq
- Batch correction
- Annotation
- MetaCell
- MetaCell
- Recommended workflow: SEACells end-to-end + downstream sanity
- Multi-sample metacells with batch correction
- MetaCell zoo
- SEACells — kernel archetypal analysis
- MetaQ — VQ-VAE codebook metacells
- SuperCell — kNN graph + walktrap community detection
- k-means — the trivial baseline that’s often hard to beat
- random — the honest lower-bound baseline
- GeoSketch — density-aware sketching as a metacell baseline
- Side-by-side comparison of all metacell backends
- Trajectory
- Trajectory inference
- Trajectory Inference with Slingshot
- Unified terminal-state & fate-probability inference
- Trajectory inference — backend zoo
- Timing-associated genes analysis with TimeFateKernel
- Identify the driver regulators of cell fate decisions
- Data loading and processing
- Training CEFCON model
- Downstream analysis
- In-silico Perturbation
- Cell-Cell Communication
- Cell Structure
- Copy-Number Variation
- Metabolism
- Enrichment
- Velocity
- Multi-omics
- Single-EV proteomics