Single-Cell Bioinformatics: From Technology to Analysis Methodologies
Preferance
1
Why Single-Cell Analysis?
2
Single-Cell RNA-seq Technologies
2.1
Plate-Based SMART-seq
2.2
MARS-seq
2.3
Droplet Microfluidics (DROP-seq)
2.4
10X Genomics Chromium
2.5
Nanowell Platforms
2.6
Sample Indexing (Illumina)
2.7
Single-cell Combinatorial Indexing: sciRNA-seq
3
Single-cell RNA-seq Data Processing and Quality Control
3.1
Cell Ranger Pipeline
3.1.1
FASTQ File Format
3.1.2
Sequencing Quality : FASTQC tool
3.1.3
Read trimming
3.2
Demultiplexing
3.3
STAR: Ultrafast Universal RNA-seq Aligner
3.4
Transcript Quantification: KALLISTO
3.5
The Single-Cell Data
3.6
Data normalization
3.7
Dealing with Batch Effects
3.8
Data cleanup (filtering)
3.9
Empty Droplets
3.10
Doublet detection and removal:
demuxlet
3.11
Doublet detection and removal:
DoubletFinder
3.12
Doublet detection and removal:
Scrublet
3.13
Issues of the Single-Cell Data
3.14
Data imputation / Data Smoothing
3.15
MAGIC (Markov Affinity-based Graph Imputation of Cells)
4
Methods Used in Single-Cell
5
Cell Trajectory Inference
6
Differential Regulation and Pathway Analysis in Single-Cell
7
Single-Cell Epigenomics
8
Single-Cell ATAC-seq
9
Single-Cell Data Integration
9.1
Horizontal Integration
9.2
Vertical Integration
9.2.1
MOFA / MOFA+
9.2.2
LIGER (Linked Inference of Genomic Experimental Relationships)
9.3
Weighted Nearest Neighbors (WNN)
9.4
Bridge Integration
9.5
Deep Learning Approaches: MultiVI
10
Single-Cell Multiomics
11
Single-Cell Spatial Transcriptomics
12
Single-Cell DNA sequencing
References
Published with bookdown
Single-Cell Bioinformatics: From Technology to Analysis Methodologies
Chapter 5
Cell Trajectory Inference