/scRNAseq-MouseSkinEpithelia

Developed scripts for single-cell RNA-seq study in Yejing Ge et al, The aging skin microenvironment dictates stem cell behavior. PNAS (2020). It focuses on comparing Young vs. Aged mouse skin epithelium with single-cell RNA-seq.

Primary LanguageR

The aging skin microenvironment dictates stem cell behavior

This project provides the code developed for the study in Yejing Ge et al, The aging skin microenvironment dictates stem cell behavior. PNAS (2020). It focuses on comparing Young vs. Aged mouse skin epithelium with single-cell RNA-seq.

Requirements

  • R (tested in R version 3.5.2 (2018-12-20) -- "Eggshell Igloo")
  • R librarys: Seurat (v2.3), SingleR (github version)

Data

Raw counts data and processed Seurat object will be released after the final publication.

Reproduce results

1. Data preprocess

1 Seurat_setup.R

Move Cell ranger output foder under data. This script uses Seurat 2 to read the result from the Cell ranger outputs, perform normalization, scaling, dimension reduction, and unsupervised-clustering. The output Seurat object will be saved in the file data/MouseSkin_{date}.Rda

2-3. Identify cell types

2 Identify_Cell_Types_Manually.R

This script uses predefined cell type markers to identify cell types manually.

Besides, an R shiny app is built to identify cell types. scRNAseq-MouseSkinEpithelia

3 SingleR.R

This script uses the SingleR package to identify cell types based on reference datasets.

4-6. Generate Figures and data exploration

4 Major_Figures.R

This script contains source code for generating Fig 1A, 1C, and 1S.

5 Differential_analysis.R, conducting differential analysis between Young and Aged sample.

6 Monocle.R, performing Monocle analysis.