# Bar Graph of Expression Data from Seurat Object

I am looking for a way to present some single-cell RNA sequencing data on expression of a certain gene. My dataset has 2 variables, cell type and condition. I'm looking to create a grouped aligned scatter plot bar graph of the expression of the gene, where the y-axis is expression levels, while X axis is the different cell types, with each cell type having 2 bars (1 for healthy, 1 for diseased). My dataset has 3 healthy and 3 diseased samples, but all of the data is integrated into a Seurat object. To first create an aligned scatter plot bar graph, what I did was generate a DotPlot for the expression of gene X in each sample, split by cell-type. Then I realized that DotPlot() prints out a data table with values including avg.exp, pct.exp and avg.scaled.exp. I used the avg.exp metric to plot the data, but I was posting this question to see if there are other ways I can access and present expression data from a Seurat object. Is the way I've presented accurate? I've posted a sample bar graph of what I'm looking for, and the main question is how I can gather the expression levels across cell types AND diseased or healthy condition.

Thanks!

In single-cell data a bargraph with error bar is not usually shown since the gene expression does not have a normal distribution due to drop-outs. A violin-plot is commonly used. In your case, first set the active.ident to cell types

seuratObj <- SetIdent(seuratObj , value = "Cell.Types")


Then call the violin plot

VlnPlot(seuratObj , features = "geneX", split.by = "condition")


If you still specifically need bargraph, you can get the output of the violin plot as a dataframe and then draw the barplot.

You can add + geom_boxplot() after the command to turn the violin plot to a box plot.

As for the barplot, you can do it with these:

# the object is your seurat object
data.df <- object@meta.data[, c("celltypes", "condition")]
data.df["value"] <- object$RNA@data["geneX", ] data.df <- data.df %>% mutate(group = paste(celltypes, condition, sep=":")) # remove cells that do not express the gene data.df <- data.df %>% filter(value > 0) data.df.2 <- data.df %>% group_by(group) %>% summarise(sd=sd(value),value=mean(value)) X <- str_split(data.df.2$$'group', ":", 2, simplify=TRUE) data.df.2$$'celltypes' <- X[, 1] data.df.2$'condition' <- X[, 2]

ggplot(data.df.2, aes(fill=condition, y=value, x=celltypes)) +
geom_bar(color="black", position="dodge", stat="identity") +
geom_errorbar(
aes(ymin=value, ymax=value+sd), width=.2,
position=position_dodge(.9)
)


Dropout is very usual in single-cell data. If you really want to show the expression with barplot, I would suggest removing the zero values or using denoised data for better visualisation.