final_pou4_revision_code.md

Final Pou4 Revision Code

This is supplemental code for additional visualizations and analyses.

Note: It is compatible with Seurat v.4.0.4.

Revision Strategy:

Version 2 Seurat objects “oe_epi_suerat” and “wt_epi_cns9_seurat” were generated from running “final_pou4_analysis_pipeline.Rmd”. They have Amazon S3 object URLs. They are loaded into a new environment using these URLs, updated to version 4 Seurat objects and manipulated in this script.

library(Seurat)
library(edgeR)
library(dplyr)
library(Matrix)
library(ggplot2)

Update Seurat objects from v2 to v3.

First load objects.

oe_epi_seurat_url <- "https://relevant-pou4-data.s3.us-east-2.amazonaws.com/pou4_revision_code_relevant_objects/oe_epi_seurat.rds"
download.file(oe_epi_seurat_url, destfile = "/tmp/oe_epi_seurat.rds")
oe_epi_seurat <- readRDS("/tmp/oe_epi_seurat.rds")

wt_epi_cns9_seurat_url <- "https://relevant-pou4-data.s3.us-east-2.amazonaws.com/pou4_revision_code_relevant_objects/wt_epi_cns9_seurat.rds"
download.file(wt_epi_cns9_seurat_url, "/tmp/wt_epi_cns9_seurat.rds")
wt_epi_cns9_seurat <- readRDS("/tmp/wt_epi_cns9_seurat.rds")

Revision 1: Figure 2c.

Perform UMAP on OE Epi Seurat and visualize subclusters.

UMAPPlot(updated_oe_epi_seurat, reduction = "umap")

Revision 2: Figures S16 and S18.

Visualize various quality control metrics in OE Epi and WT Epi and CNS seurats.

oe_vln_plot <- VlnPlot(updated_oe_epi_seurat, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
oe_scatter_plot <- FeatureScatter(updated_oe_epi_seurat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")

wt_vln_plot <- VlnPlot(updated_wt_epi_cns9_seurat, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
wt_scatter_plot <- FeatureScatter(updated_wt_epi_cns9_seurat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")

# For OE Epi Clusters.
oe_vln_plot

oe_scatter_plot

# For WT Epi and CNS Clusters.
wt_vln_plot

wt_scatter_plot

Revision 3: Figure S17.

Make Log10 Cell Density of UMIs (a) and Number of Genes (b) in OE Epi and WT Epi and CNS Cells

# Do for OE Epi.
oe_metadata <- updated_oe_epi_seurat@meta.data
oe_metadata$cells <- rownames(oe_metadata)
oe_metadata$sample <- "OE Epi"
oe_metadata$res.0.5 <- NULL

# Rename columns.
oe_metadata <- oe_metadata %>%
        dplyr::rename(nUMI = nCount_RNA,
                      nGene = nFeature_RNA)

# Do for WT.
wt_metadata <- updated_wt_epi_cns9_seurat@meta.data
wt_metadata$cells <- rownames(wt_metadata)
wt_metadata$sample <- "WT Epi and CNS"
wt_metadata$res.0.6 <- NULL

# Rename columns.
wt_metadata <- wt_metadata %>%
        dplyr::rename(nUMI = nCount_RNA,
                      nGene = nFeature_RNA)

# Combine OE Epi and WT Epi and CNS metadata.
all_meta_data <- rbind(oe_metadata, wt_metadata)

Visualize.

all_meta_data %>% 
    ggplot(aes(color=sample, x=nUMI, fill= sample)) +
    geom_density(alpha = 0.2) + 
    scale_x_log10() + 
    theme_classic() +
    ylab("log10 Cell Density") +
    geom_vline(xintercept = 1000) +
    ggtitle("Log10 Cell Density of UMIs in OE Epi and WT Epi and CNS Cells") 

all_meta_data %>% 
    ggplot(aes(color=sample, x=nGene, fill= sample)) +
    geom_density(alpha = 0.2) + 
    scale_x_log10() + 
    theme_classic() +
    ylab("log10 Cell Density") +
    ggtitle("Log10 Cell Density of Number of Genes in OE Epi and WT Epi and CNS Cells")