• 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

19 Single Cell Resources

19.1 Comprehensive list of single-cell resources

  • https://github.com/seandavi/awesome-single-cell

19.2 Computational packages for single-cell analysis

  • http://bioconductor.org/packages/devel/workflows/html/simpleSingleCell.html
  • https://satijalab.org/seurat/
  • https://scanpy.readthedocs.io/

19.3 eLife Commentary on the Human Cell Atlas

link - Nature Commentary on the Human Cell Atlas - https://www.nature.com/news/the-human-cell-atlas-from-vision-to-reality-1.22854

19.4 Online courses

  • Analysis of single cell RNA-seq data
  • Single Cell Genomics Day
  • Simple Single Cell
  • Single Cell Transcriptomics
  • R for Data Sciences