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
2
Batch Correction
2.1
Load datasets
2.2
Preprocess
2.3
Batch correction
2.4
Clustering
2.5
Visualization
3
Cell type annotation
3.1
Manual annotation
3.2
Automatic annotation
3.2.1
Run SingleR
3.2.2
Annotate with SingleR result
3.2.3
Comparision with manual annotation result
4
Slingshot: cell lineage and pseudotime inference
4.1
Lineage plot
4.2
Pseudotime plot
4.3
Expression trends in different cell trajectories
4.4
Heatmap
5
Cell-cell communication
5.1
Input
5.2
CellChat
5.3
visulization
6
SVP
6.1
runSGSA
6.2
runLISA
6.3
runLOCALBV
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
2025-03-20
Preface
Hello World.