Introduction to Single-cell Sequencing Data Analysis

Hwang Lab

Changjin Hong, Sunho Park, and Tae Hyun Hwang

Cleveland Clinic Foundation

January 16, 2020

This tutorial is an introductory single cell sequencing data analysis. We will cover both theory and experiment with public data set. Bring your laptop and there are no prerequisites.

Index

1. Machine learning ideas in Suerat Package
   1. Machine learning theory under scRNA analysis
2. Introduction of single cell sequencing
   1. 10x_chromium
3. Cellranger
   1. bcl to FASTQ
   2. FATQ to BAM
4. Experiment Setup
   1. Rstudio and R libraries
5. Break (15 min)
6. Single sample analysis
   1. Normalization
   2. Clustering
   3. Cluster Analysis
   4. Cell Identification
7. Break (15 min)
8. Comparative analysis
   1. integration
   2. conserved markers
   3. differential gene-expression analysis

Common Linux Commands:

General syntax: <command> -<option> <input1>

• pwd - Print Working Directory
• ls - List items in directory
• cd - Change directory
• mkdir - Make Directory
• export - Set environment variable
• echo - Print contents of variable
• tar - Compress or Extract files

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Brought to you by the Dr. Hwang lab in Department of Quantitative Health Science, LRI, Cleveland Clinic

Clone github codes:

1. Click "Clone or download" and click "Download Zip".
2. Save the zip file into your home directory and unzip.