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
3
Expression QC and Normalization
3.1
Google Slides