• 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 Introduction
      • 1.5.2 Transcriptome Quantification
      • 1.5.3 Expression QC and Normalization
      • 1.5.4 Data Wrangling scRNAseq
      • 1.5.5 Identifying Cell Populations
      • 1.5.6 Feature Selection and Cluster Analysis
  • 2 Transcriptome Quantification
    • 2.1 Google Slides
  • 3 Expression QC and Normalization
    • 3.1 Google Slides
  • 4 Data Wrangling scRNAseq
    • 4.1 Goal
    • 4.2 Introduction
      • 4.2.1 Load necessary packages
      • 4.2.2 Read in NSCLC counts matrix.
      • 4.2.3 Let’s examine the sparse counts matrix
      • 4.2.4 How big is the matrix?
      • 4.2.5 How much memory does a sparse matrix take up relative to a dense matrix?
    • 4.3 Filtering low-quality cells
      • 4.3.1 Look at the summary counts for genes and cells
      • 4.3.2 Plot cells ranked by their number of detected genes.
    • 4.4 Beginning with Seurat:
      • 4.4.1 Creating a seurat object
    • 4.5 Preprocessing step 1 : Filter out low-quality cells
    • 4.6 Examine contents of Seurat object
      • 4.6.1 Preprocessing step 2 : Expression normalization
    • 4.7 Detection of variable genes across the single cells
    • 4.8 Gene set expression across cells
  • 5 Identifying Cell Populations
    • 5.1 Google Slides
  • 6 Feature Selection and Cluster Analysis
    • 6.1 Abstract
    • 6.2 Seurat Tutorial Redo
      • 6.2.1 Preprocessing Steps
      • 6.2.2 Start of Identifying Cell Types
      • 6.2.3 Run non-linear dimensional reduction (UMAP/tSNE)
    • 6.3 Feature Selection
      • 6.3.1 Differential Expression Analysis
      • 6.3.2 Check Clusters
      • 6.3.3 View Entire Object Structure
      • 6.3.4 Practice Visualizing/Embedding
    • 6.4 Other Options For Analysis
  • 7 Single Cell Resources
    • 7.1 Comprehensive list of single-cell resources
    • 7.2 Computational packages for single-cell analysis
    • 7.3 eLife Commentary on the Human Cell Atlas
    • 7.4 Online courses
  • References
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ANALYSIS OF SINGLE CELL RNA-SEQ DATA

7 Single Cell Resources

7.1 Comprehensive list of single-cell resources

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

7.2 Computational packages for single-cell analysis

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

7.3 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

7.4 Online courses

  • https://scrnaseq-course.cog.sanger.ac.uk/website/index.html
  • https://broadinstitute.github.io/2019_scWorkshop/
  • https://github.com/SingleCellTranscriptomics
  • https://r4ds.had.co.nz/