ANALYSIS OF SINGLE CELL RNA-SEQ DATA
1
Introduction
1.1
COURSE OVERVIEW
1.2
TARGETED AUDIENCE & ASSUMED BACKGROUND
1.3
COURSE FORMAT
1.4
Getting Started
1.5
SESSION CONTENT
1.5.1
Monday – Classes from 08:00 to 16:00 (lunch break-1 hr, 40 min of total coffee breaks)
1.5.2
Tuesday – Classes from 08:00 to 16:00
1.5.3
Wednesday – Classes from 08:00 to 16:00
1.5.4
Thursday – Classes from 08:00 to 16:00
1.5.5
Friday – Classes from 08:00 to 16:00
2
Introduction
2.1
Slides
3
Understanding Sequencing Raw Data
3.1
Class Environment
3.1.1
Getting into AWS Instance
3.2
Shell and Unix commands
3.2.1
Common Linux Commands
3.3
File formats
3.3.1
View FASTQ Files
3.3.2
View BAM Files
3.4
Public data repositories
3.4.1
Cellranger/10x
3.4.2
GEO
3.4.3
Single Cell Portal
3.5
Docker Commands
4
Transcriptome Quantification
4.1
Google Slides
5
Processing scRNAseq Data
5.1
Goal
5.2
Further reading
5.3
Download data
5.3.1
Fastq file format
5.4
Align the reads
5.4.1
STAR align
5.4.2
Bam file format
5.5
Visualization
6
Introduction R/Bioconductor
6.1
Start Environment
6.1.1
Local Command
6.1.2
AWS Command
6.2
Installing packages
6.2.1
CRAN
6.2.2
Github
6.2.3
Bioconductor
6.2.4
Source
6.3
Installation instructions:
6.3.1
Classes/Types
6.3.2
Data structures
6.3.3
Detour to S3/S4
6.4
More information
6.4.1
Checking for help for any function!
6.5
Grammer of Graphics (ggplot2)
6.5.1
What is ggplot2?
6.5.2
Principles of ggplot2
6.6
Reference
7
Quality Control
7.1
Slides
8
Data Wrangling scRNAseq
8.1
Goal
8.2
Introduction
8.2.1
Load necessary packages
8.2.2
Read in NSCLC counts matrix.
8.2.3
Let’s examine the sparse counts matrix
8.2.4
How big is the matrix?
8.2.5
How much memory does a sparse matrix take up relative to a dense matrix?
8.3
Filtering low-quality cells
8.3.1
Look at the summary counts for genes and cells
8.3.2
Plot cells ranked by their number of detected genes.
8.4
Beginning with Seurat:
http://satijalab.org/seurat/
8.4.1
Creating a seurat object
8.5
Preprocessing step 1 : Filter out low-quality cells
8.6
Examine contents of Seurat object
8.6.1
Preprocessing step 2 : Expression normalization
8.7
Detection of variable genes across the single cells
8.8
Gene set expression across cells
9
Identifying Cell Populations
9.1
Slides
10
Feature Selection and Cluster Analysis
10.1
Abstract
10.2
Seurat Tutorial Redo
10.2.1
Preprocessing Steps
10.2.2
Start of Identifying Cell Types
10.2.3
Run non-linear dimensional reduction (UMAP/tSNE)
10.3
Feature Selection
10.3.1
Differential Expression Analysis
10.3.2
Check Clusters
10.3.3
View Entire Object Structure
10.4
Probabilistic (LDA) Clustering
10.4.1
Example LDA in Bulk
10.4.2
PBMC LDA
10.4.3
Practice Visualizing/Embedding
10.5
Other Options For Analysis
11
Batch Correction Lecture
11.1
Slides
12
Batch Correction Lab
12.1
Load settings and packages
12.2
Read in pancreas expression matrices
12.3
Preparing the individual Seurat objects for each pancreas dataset without batch correction
12.4
Cluster pancreatic datasets without batch correction
12.4.1
Batch correction: canonical correlation analysis (CCA) + mutual nearest neighbors (MNN) using Seurat v3
12.4.2
Batch correction: integrative non-negative matrix factorization (NMF) using LIGER
12.5
Additional exploration: Regressing out unwanted covariates
12.6
Additional exploration: kBET
12.7
Additional exploration: Seurat 3
12.8
Acknowledgements
13
Trajectory Inference
13.1
Slides
14
Trajectory Analysis
14.1
Load settings and packages
14.2
First look at the differentiation data from Deng et al.
14.3
Principle Components Analysis
14.4
Diffusion map pseudotime
14.5
Slingshot map pseudotime
14.6
Find temporally expressed genes
14.7
Comparison of the different trajectory inference methods
14.8
Plots of gene expression over time.
14.9
Acknowledgements
15
Mutli-omic Analysis
15.1
Slides
16
CITE-Seq
16.1
Load settings and packages
16.2
Load in the data
16.3
Setup a Seurat object, and cluster cells based on RNA expression
16.4
Add the protein expression levels to the Seurat object
16.5
Visualize protein levels on RNA clusters
16.6
Identify differentially expressed proteins between clusters
16.7
Cluster directly on protein levels
16.8
Additional exploration: another example of multi-modal analysis
16.9
Acknowledgements
17
DIY Project
17.1
Slides
18
DIY Lab
18.1
DIY Lab
19
Single Cell Resources
19.1
Comprehensive list of single-cell resources
19.2
Computational packages for single-cell analysis
19.3
eLife Commentary on the Human Cell Atlas
19.4
Online courses
References
Published with bookdown
ANALYSIS OF SINGLE CELL RNA-SEQ DATA
15
Mutli-omic Analysis
15.1
Slides