Plot mean expression of a gene vs other parameters and highlight specific genes using ggrepel

gistfile1.txt

## Use scater to investigate and annotate gene and cluster properties

```{r}
library(scater)
## Create new SCE object from the seurat expression object
fData <- new("AnnotatedDataFrame", data = [email protected])
seurat_scater_sce <- newSCESet(exprsData = as.matrix(expression_seurat@data), phenoData = fData,
                            lowerDetectionLimit=0.01,
                            logExprsOffset=1)
seurat_scater_sce <- calculateQCMetrics(seurat_scater_sce)
```

```{r}
library(ggrepel)
## Put the feature Data into variable called gene_info
gene_info <- fData(seurat_scater_sce)

## Define a group of genes of interest
genes_of_interest <- c("")


## Write a table that contains some useful stats for these genes
genes_of_interest_data <- subset(gene_info,rownames(gene_info) %in% genes_of_interest)
write.table(params_goi,file="Feature_params.genes_of_interest.txt",
              row.names=T,
              col.names=T,
              quote=F,
              sep="\t")

## Plot the position of genes of interest in relation to all other genes in the distribution of mean gene expression vs pct dropout
mean_exprs_vs_drpt <- ggplot(gene_info,aes(log10(mean_exprs),pct_dropout)) +
  geom_point(fill="white",col="grey",size=1.5) +
  geom_point(data= genes_of_interest_data,
             color="red",
             size=1.5) +
   geom_text_repel(
    data = genes_of_interest_data,
    aes(label = rownames(genes_of_interest_data)),
    size = 5,
    box.padding = unit(0.35, "lines"),
    point.padding = unit(0.3, "lines")
  ) +
  xlab("log10 of mean expression") +
  ylab("% Total expression")

ggsave(mean_exprs_vs_drpt,file="mean_exprs_vs_drpt.svg")

## Plot the position of genes of interest in relation to all other genes in the distribution of mean gene expression vs pct_total_expression
mean_exprs_vs_pcttotal <- ggplot(gene_info,aes(log10(mean_exprs),pct_total_exprs)) +
  geom_point(fill="white",
             col="grey",
             pch=21,
             alpha=0.8,
             size=1.5) +
  geom_point(data= genes_of_interest_data,
             fill="red",
             col="black",
             pch=21,
             size=1.5) +
   geom_text_repel(
    data = genes_of_interest_data,
    aes(label = rownames(genes_of_interest_data)),
    size = 5,
    box.padding = unit(0.35, "lines"),
    point.padding = unit(0.3, "lines")
  ) +
  xlab("log10 of mean expression") +
  ylab("% Total expression")

ggsave(mean_exprs_vs_pcttotal,file="mean_exprs_vs_pcttotal.svg")

detach("package:scater", unload=TRUE)
detach("package:ggrepel", unload=TRUE)
```