Plotting single cell data
At this stage in the workflow we usually would like to plot aspects of our data in one of the reduced dimension representations. Instead of plotting this in an ordinary fashion, I will demonstrate how schex can provide a better way of plotting this.
Calculate hexagon cell representation
First, I will calculate the hexagon cell representation for each cell for a specified dimension reduction representation. I decide to use nbins=40 which specifies that I divide my x range into 40 bins. Note that this might be a parameter that you want to play around with depending on the number of cells/ nuclei in your dataset. Generally, for more cells/nuclei, nbins should be increased.
Plot number of cells/nuclei in each hexagon cell
First I plot how many cells are in each hexagon cell. This should be relatively even, otherwise change the nbins parameter in the previous calculation.
Plot meta data in hexagon cell representation
Next I colour the hexagon cells by some meta information, such as the majority of cells cluster membership and the median total count in each hexagon cell.
While for plotting the cluster membership the outcome is not too different from the classic plot, it is much easier to observe differences in the total count.
For convenience there is also a function that allows the calculation of label positions for factor variables. These can be overlayed with the package ggrepel.
label_df <- make_hexbin_label(tenx_pbmc3k, col = "cluster")
pp <- plot_hexbin_meta(tenx_pbmc3k, col = "cluster", action = "majority")
pp + ggrepel::geom_label_repel(
data = label_df, aes(x = x, y = y, label = label),
colour = "black", label.size = NA, fill = NA
)
Plot gene expression in hexagon cell representation
Finally, I will visualize the gene expression of the POMGNT1 gene in the hexagon cell representation.
gene_id <- "POMGNT1"
plot_hexbin_feature(tenx_pbmc3k,
type = "logcounts", feature = gene_id,
action = "mean", xlab = "UMAP1", ylab = "UMAP2",
title = paste0("Mean of ", gene_id)
)
Again it is much easier to observe differences in gene expression using the hexagon cell representation than the classic representation.
We can overlay the gene expression data with the clusters for convenience.
plot_hexbin_feature_plus(tenx_pbmc3k,
col = "cluster", type = "logcounts",
feature = "POMGNT1", action = "mean"
)
Understanding schex output as ggplot objects
The schex packages renders ordinary ggplot objects and thus these can be treated and manipulated using the ggplot grammar. For example the non-data components of the plots can be changed using the function theme.
gene_id <- "CD19"
gg <- schex::plot_hexbin_feature(tenx_pbmc3k,
type = "logcounts", feature = gene_id,
action = "mean", xlab = "UMAP1", ylab = "UMAP2",
title = paste0("Mean of ", gene_id)
)
gg + theme_void()
The fact that schex renders ggplot objects can also be used to save these plots. Simply use ggsave in order to save any created plot.
To find the details of the session for reproducibility, use this:
sessionInfo()
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] ggrepel_0.9.5 scran_1.32.0
#> [3] scater_1.32.0 scuttle_1.14.0
#> [5] TENxPBMCData_1.21.0 HDF5Array_1.32.0
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#>
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