This function uses the output of sig_genes_extract_all()
as well as the
logcounts from the layer-level (group-level) data to visualize the expression
of a given gene and display the modeling results for the given gene.
layer_boxplot(
i = 1,
sig_genes = sig_genes_extract(),
short_title = TRUE,
sce_layer = fetch_data(type = "sce_layer"),
col_bkg_box = "grey80",
col_bkg_point = "grey40",
col_low_box = "violet",
col_low_point = "darkviolet",
col_high_box = "skyblue",
col_high_point = "dodgerblue4",
cex = 2,
group_var = "layer_guess_reordered_short",
assayname = "logcounts"
)
A integer(1)
indicating which row of sig_genes
do you want to
plot.
The output of sig_genes_extract_all()
.
A logical(1)
indicating whether to print a short title
or not.
Defaults to the output of
fetch_data(type = 'sce_layer')
. This is a
SingleCellExperiment
object with the spot-level Visium data compressed via pseudo-bulking to the
layer-level (group-level) resolution. See fetch_data()
for more details.
Box background color for layers not used when visualizing
the pairwise
model results.
Similar to col_bkg_box
but for the points.
Box background color for layer(s) with the expected
lower expression based on the actual test for row i
of sig_genes
.
Similar to col_low_box
but for the points.
Similar to col_low_box
but for the expected layer(s)
with higher expression.
Similar to col_high_box
but for the points.
Controls the size of the text, points and axis legends.
A character(1)
specifying a colData(sce_layer)
column
name to use for the x-axis.
A character(1)
specifying the default assay to use from
assays(sce_layer)
.
This function creates a boxplot of the layer-level data
(group-level) separated by layer and colored based on the model type from row
i
of sig_genes
.
Adapted from https://github.com/LieberInstitute/HumanPilot/blob/master/Analysis/Layer_Guesses/layer_specificity.R
Other Layer modeling functions:
sig_genes_extract()
,
sig_genes_extract_all()
## Obtain the necessary data
if (!exists("modeling_results")) {
modeling_results <- fetch_data(type = "modeling_results")
}
#> 2024-12-13 19:41:39.883031 loading file /github/home/.cache/R/BiocFileCache/5c656d46b9_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1
if (!exists("sce_layer")) sce_layer <- fetch_data(type = "sce_layer")
#> 2024-12-13 19:41:41.079669 loading file /github/home/.cache/R/BiocFileCache/5c643a903ea_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1
## Top 2 genes from the enrichment model
sig_genes <- sig_genes_extract_all(
n = 2,
modeling_results = modeling_results,
sce_layer = sce_layer
)
## Example default boxplot
set.seed(20200206)
layer_boxplot(sig_genes = sig_genes, sce_layer = sce_layer)
## Now show the long title version
set.seed(20200206)
layer_boxplot(
sig_genes = sig_genes,
short_title = FALSE,
sce_layer = sce_layer
)
set.seed(20200206)
layer_boxplot(
i = which(sig_genes$model_type == "anova")[1],
sig_genes = sig_genes,
sce_layer = sce_layer
)
set.seed(20200206)
layer_boxplot(
i = which(sig_genes$model_type == "pairwise")[1],
sig_genes = sig_genes,
sce_layer = sce_layer
)
## Viridis colors displayed in the shiny app
library("viridisLite")
set.seed(20200206)
layer_boxplot(
sig_genes = sig_genes,
sce_layer = sce_layer,
col_low_box = viridis(4)[2],
col_low_point = viridis(4)[1],
col_high_box = viridis(4)[3],
col_high_point = viridis(4)[4]
)
## Paper colors displayed in the shiny app
set.seed(20200206)
layer_boxplot(
sig_genes = sig_genes,
sce_layer = sce_layer,
col_low_box = "palegreen3",
col_low_point = "springgreen2",
col_high_box = "darkorange2",
col_high_point = "orange1"
)
## Blue/red colors displayed in the shiny app
set.seed(20200206)
layer_boxplot(
i = which(sig_genes$model_type == "pairwise")[1],
sig_genes = sig_genes,
sce_layer = sce_layer,
col_bkg_box = "grey90",
col_bkg_point = "grey60",
col_low_box = "skyblue2",
col_low_point = "royalblue3",
col_high_box = "tomato2",
col_high_point = "firebrick4",
cex = 3
)