A tutorial of scMINER
1
Introduction
1.1
A few concepts
SparseEset
Mutual Information
Gene Activity
1.2
Why use scMINER
1.3
Citation
1.4
Support
2
Get started
2.1
Installation
Install scMINER R package
Install MICA and SJARACNe
2.2
Demo data
2.3
Create project space
3
Generate gene expresion matrix
3.1
From data directory by 10x Genomics
3.2
From text-table file
3.3
From HDF5 file by 10x Genomics
3.4
From H5AD file
4
Create SparseEset object
4.1
Solely from the gene expression matrix
4.2
Using self-customized meta data
4.3
From multiple samples
5
Data filtration
5.1
QC report
5.2
Filter the sparse eset object
5.2.1
Data filtration with auto mode
5.2.2
Data filtration with manual mode
6
Data normalization
7
MI-based clustering analysis
7.1
Introduction to MICA
7.2
Generate MICA input
7.3
Run MICA
7.4
Integrate MICA outputs into SparseEset object
7.5
Visualize the MICA output
7.5.1
Color-coded by cluster labels
7.5.2
Color-coded by true label of cell types
7.5.3
Color-coded by nUMI, for QC purpose
7.5.4
Color-coded by nFeature, for QC purpose
8
Cell type annotation
8.1
Supervised cell type annotation
8.1.1
Annotate using signature scores
8.1.2
Annotate using individual marker genes
8.2
Unsupervised cell type annotation
8.3
Add cell type annotations to SparseExpressionSet object
9
Network inference
9.1
Generate SJARACNe input files
9.2
Run SJARACNe
9.3
Assess the quality of networks
9.3.1
Introduction to the network file by SJARACNe
9.3.2
Generate network QC report
10
Actvity-based analysis
10.1
Calculate the activities
10.1.1
Calculate activities per group
10.1.2
Calculate activities in batch
10.1.3
Save activity eset object
10.2
Differential activity analysis
11
Session information
Published with bookdown
scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data
1.3
Citation
Please consider citing
this paper
if you find scMINER useful in your research.