Orthologous marker gene tutorial

1. Install R and RStudio

Install R:

Windows:

• Visit R for Windows and download the .exe file.
• Run the installer and follow prompts to complete setup.

MacOS:

• Visit R for macOS, choose the correct version (Apple Silicon or Intel).
• Download the .pkg file and install by following prompts.

Linux (Ubuntu):

• Open terminal, update packages, and install R:

sudo apt update
sudo apt install r-base r-base-dev -y
            

Install RStudio

Download and install RStudio from the RStudio download page for your computer (Windows, MacOS, or Linux).

Launch RStudio

Open RStudio after installation, and start coding in R.

Tutorial video

2. Essential R Code

• Loading data: CSV, TSV, Excel, RDS, RData
• Inspecting data: head(), str(), summary()
• Manipulating data with dplyr: filter(), select(), and mutate() to manage rows, columns, and new variables
• Summarizing data: group_by() and summarise() to calculate grouped stats
• Visualization with ggplot2
• Statistical test
• Exporting PDF file with: pdf() and dev.off()

Tutorial video

3. scRNA-seq data processing

• Convert the count matrix (gene x cell) to a Seurat object
• Filter out the low-quality cells
• Normalize the data
• Find highly variable genes
• Scale the data
• Reduce data dimensions using PCA
• Cluster cells by applying the graph-based method
• Visualize cluster cells using UMAP

Tutorial video

4. Marker genes identification

• Load the libraries

library(Seurat)
library(tidyverse)
            

• Load dataset

ATH_root = readRDS("/Users/tranchau/Documents/GFP_2024/Data/S11_WER_GFP/Seurat_Obj_WER_wlabels.rds")
            

• Find marker genes

ATH_root_marker <- FindAllMarkers(ATH_root, only.pos = TRUE, min.pct = 0.5, logfc.threshold = 0.5) %>% 
                                  group_by(cluster) %>% 
                                  arrange(cluster, desc(avg_log2FC))

head(ATH_root_marker)
table(ATH_root_marker$cluster) #summarize number of genes in each cluster
            

Tutorial video

5. OMG browser tutorial

This OMG browser enables plant biologists to quickly compare cell types across species and identify cell types in new samples.

6. OMG code tutorial

7. Contact me

For more information, please contact me at tnchau@vt.edu.