• 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 09:30 to 17:30 (lunch break-1 hr, 40 min of total coffee breaks)
      • 1.5.2 Tuesday – Classes from 09:30 to 17:30
      • 1.5.3 Wednesday – Classes from 09:30 to 17:30
      • 1.5.4 Thursday – Classes from 09:30 to 17:30
      • 1.5.5 Friday – Classes from 09:30 to 17:30
  • 2 scRNA-Seq Experimental Design
  • 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
  • 4 Data Preprocessing
  • 5 Processing scRNAseq Data
    • 5.1 Goal
    • 5.2 Further reading
    • 5.3 FastQC
      • 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 Transcriptome Quantification
  • 7 Introduction R/Bioconductor
    • 7.1 Start Environment
    • 7.2 Installing packages
      • 7.2.1 CRAN
      • 7.2.2 Github
      • 7.2.3 Bioconductor
      • 7.2.4 Source
    • 7.3 Installation instructions:
      • 7.3.1 Classes/Types
      • 7.3.2 Data structures
      • 7.3.3 Detour to S3/S4
    • 7.4 More information
      • 7.4.1 Checking for help for any function!
    • 7.5 Grammer of Graphics (ggplot2)
      • 7.5.1 What is ggplot2?
      • 7.5.2 Principles of ggplot2
    • 7.6 Reference
  • 8 Expression QC and Normalization
  • 9 Data Wrangling scRNAseq
    • 9.1 Goal
    • 9.2 Introduction
      • 9.2.1 Load necessary packages
      • 9.2.2 Read in NSCLC counts matrix.
      • 9.2.3 Let’s examine the sparse counts matrix
      • 9.2.4 How big is the matrix?
      • 9.2.5 How much memory does a sparse matrix take up relative to a dense matrix?
    • 9.3 Filtering low-quality cells
      • 9.3.1 Look at the summary counts for genes and cells
      • 9.3.2 Plot cells ranked by their number of detected genes.
    • 9.4 Beginning with Seurat: http://satijalab.org/seurat/
      • 9.4.1 Creating a seurat object
    • 9.5 Preprocessing step 1 : Filter out low-quality cells
    • 9.6 Examine contents of Seurat object
      • 9.6.1 Preprocessing step 2 : Expression normalization
    • 9.7 Detection of variable genes across the single cells
    • 9.8 Gene set expression across cells
  • 10 Identifying Cell Populations
    • 10.1 Google Slides
  • 11 Feature Selection and Cluster Analysis
    • 11.1 Abstract
    • 11.2 Seurat Tutorial
      • 11.2.1 Preprocessing Steps
      • 11.2.2 Start of Identifying Cell Types
    • 11.3 Feature Selection
      • 11.3.1 Differential Expression Analysis
      • 11.3.2 Dimensionality Reduction
      • 11.3.3 Independent Components Analysis (ICA)
      • 11.3.4 Clustering
      • 11.3.5 Check Clusters
      • 11.3.6 Practice Visualizing/Embedding
  • 12 Batch Effects
  • 13 Correcting Batch Effects
    • 13.1 Load settings and packages
    • 13.2 Preparing the individual Seurat objects for each pancreas dataset without batch correction
    • 13.3 Cluster pancreatic datasets without batch correction
      • 13.3.1 Batch correction: canonical correlation analysis (CCA) using Seurat
      • 13.3.2 Batch correction: integrative non-negative matrix factorization (NMF) using LIGER
    • 13.4 Additional exploration: Regressing out unwanted covariates
    • 13.5 Additional exploration: kBET
    • 13.6 Additional exploration: Seurat 3
    • 13.7 Acknowledgements
  • 14 Functional Analysis
    • 14.1 Google Slides
    • 14.2 Gene sets and signatures
      • 14.2.1 Cell Cycle
    • 14.3 Pathway analysis
    • 14.4 inferCNV / honeybadger
      • 14.4.1 Create the InferCNV Object
      • 14.4.2 Filtering genes
      • 14.4.3 Normalize each cell’s counts for sequencing depth
      • 14.4.4 Perform Anscombe normalization
      • 14.4.5 Log transform the normalized counts:
      • 14.4.6 Apply maximum bounds to the expression data to reduce outlier effects
      • 14.4.7 Initial view, before inferCNV operations:
      • 14.4.8 Perform smoothing across chromosomes
      • 14.4.9 Subtract the reference values from observations, now have log(fold change) values
      • 14.4.10 Invert log values
      • 14.4.11 Removing noise
      • 14.4.12 Remove outlier data points
      • 14.4.13 Find DE genes by comparing the mutant types to normal types, BASIC
      • 14.4.14 Additional Information
  • 15 Pseudotime Cell Trajectories
    • 15.1 Google Slides
    • 15.2 Comparison Abstract
  • 16 Functional Pseudotime Analysis
    • 16.1 Load settings and packages
    • 16.2 First look at the differentiation data from Deng et al.
    • 16.3 Diffusion map pseudotime
    • 16.4 Slingshot map pseudotime
    • 16.5 Find temporally expressed genes
    • 16.6 Comparison of the different trajectory inference methods
    • 16.7 Plots of gene expression over time.
    • 16.8 Acknowledgements
  • 17 Single Cell Multiomic Technologies
  • 18 CITE-seq and scATAC-seq
    • 18.1 Load settings and packages
    • 18.2 Load in the data
    • 18.3 Setup a Seurat object, and cluster cells based on RNA expression
    • 18.4 Add the protein expression levels to the Seurat object
    • 18.5 Visualize protein levels on RNA clusters
    • 18.6 Identify differentially expressed proteins between clusters
    • 18.7 Cluster directly on protein levels
    • 18.8 Additional exploration: another example of multi-modal analysis
    • 18.9 Acknowledgements
  • 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

  • https://hemberg-lab.github.io/scRNA.seq.course/
  • https://github.com/SingleCellTranscriptomics
  • https://r4ds.had.co.nz/