Preface
1
Basic: preprocessing and clustering
1.1
Read 10X data
1.2
QC
1.3
Variable features
1.4
Dimensional reduction
1.5
Clustering
1.6
UMAP
1.7
Find Markers
1.7.1
Marker gene information
1.8
Cell cluster annotation
1.9
Automatic Annotation (SingleR)
2
Batch Correction
2.1
Load datasets
2.2
Preprocess
2.3
Batch correction
2.4
Clustering
2.5
Visualization
2.6
Pseudobulk Differential Expression
3
Coarse-graining of large single-cell data into metacells using SuperCell
3.1
RunSuperCell
3.2
Estimate SuperCell purity
4
Cell type annotation
4.1
Manual annotation
4.2
Automatic annotation
4.2.1
Run SingleR
4.2.2
Annotate with SingleR result
4.2.3
Comparision with manual annotation result
5
Trajectory inference and RNA velocity
5.1
Slingshot: cell lineage and pseudotime inference
5.1.1
Lineage plot
5.1.2
Pseudotime plot
5.1.3
Expression trends in different cell trajectories
5.1.4
Heatmap
5.2
RNA velocity
5.2.1
Example data
5.2.2
Run velocity analysis
5.2.3
Visualize velocity-based trajectory
6
Milo: differential abundance
6.1
Requirements
6.2
Two-group DA (GLM)
6.3
Multi-contrast testing
6.4
GLMM (random intercept)
6.5
Refit after dropping separated neighbourhoods
7
Functional Enrichment Analysis
7.1
Load data and find markers
7.2
GO Enrichment
7.3
KEGG Enrichment
8
Interactive Data Exploration
8.1
Launch Explorer
8.2
Customization
9
Cell-cell communication
9.1
Input
9.2
CellChat
9.3
visulization
10
SVP
10.1
runSGSA
10.2
runLISA
10.3
runLOCALBV
11
Interoperability
11.1
Seurat and SCE Conversion
11.2
AnnData (h5ad)
References
sclet: A Lightweight Toolkit for Single-Cell Data Analysis
sclet: A Lightweight Toolkit for Single-Cell Data Analysis
Guangchuang Yu
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University
guangchuangyu@gmail.com
2026-02-12
Preface
Hello World.