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aded VisiumHD support #154

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fae46e0
aded VisiumHD support
czackl Feb 14, 2025
3fec774
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 18, 2025
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Roxygenize
pre-commit-ci[bot] Mar 5, 2025
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87 changes: 55 additions & 32 deletions R/visualization.R
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Original file line number Diff line number Diff line change
Expand Up @@ -648,7 +648,8 @@ make_baseplot <- function(spe, df, to_plot, palette = "Mako", transform_scale =
limits = NULL, smooth = FALSE, smoothing_factor = 1.5,
title_size = 30, title = NULL, font_size = 15, legend_size = 20, density = TRUE,
save = FALSE, path = NULL, png_width = 1500, png_height = 750, show_legend = TRUE,
nDigits = NULL, pseudocount = 1, shift_positive = TRUE) {
nDigits = NULL, pseudocount = 1, shift_positive = TRUE,
spot_shape = "hexagon") {
if (is.null(spe)) {
stop("Parameter 'spe' is null or missing, but is required")
}
Expand Down Expand Up @@ -751,14 +752,17 @@ make_baseplot <- function(spe, df, to_plot, palette = "Mako", transform_scale =
legend_title <- title
}

# preparing the dataframe with sf, inserting points
sf_points <- sf::st_as_sf(df, coords = c("pxl_col_in_fullres", "pxl_row_in_fullres"))
# Branch based on the spot_shape parameter:
if (spot_shape == "hexagon") {
# Prepare an sf object with spot centers
sf_points <- sf::st_as_sf(df, coords = c("pxl_col_in_fullres", "pxl_row_in_fullres"))

# generate hexagons
new_geom <- get_polygon_geometry(df, spot_distance, offset_rotation = offset_rotation)
# Generate hexagon polygons using the existing function
new_geom <- get_polygon_geometry(df, spot_distance, offset_rotation = offset_rotation)

# now overwrite the points with hex polygons
sf_poly <- sf::st_set_geometry(sf_points, new_geom)
# Overwrite the points with hexagon geometries
sf_poly <- sf::st_set_geometry(sf_points, new_geom)
}

# Determine the palette type based on the palette name
if (is.null(palette_type)) {
Expand Down Expand Up @@ -788,19 +792,34 @@ make_baseplot <- function(spe, df, to_plot, palette = "Mako", transform_scale =
# p <- p + annotation_raster(img, xmin = min(df$pxl_col_in_fullres), xmax = max(df$pxl_col_in_fullres), ymin = max(df$pxl_row_in_fullres), ymax = min(df$pxl_row_in_fullres))
}

# add hexagons
p <- p +
geom_sf(aes_string(fill = to_plot), lwd = 0, color = NA, data = sf_poly) +
# coord_sf(xlim = c(0, width), ylim = c(0, -height)) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = title_size, hjust = 0.5),
legend.text = element_text(size = font_size),
legend.key.size = unit(legend_size, "points")
) +
# add SPOTS
if (spot_shape == "hexagon") {
p <- p + geom_sf(aes_string(fill = to_plot), lwd = 0, color = NA, data = sf_poly)
} else if (spot_shape == "square") {
p <- p + geom_tile(aes_string(x = "pxl_col_in_fullres", y = "pxl_row_in_fullres", fill = to_plot),
data = df, width = spot_distance, height = spot_distance
)
} else if (spot_shape == "circle") {
# For circles, we use geom_point with shape 21.
# Here we approximate the circle size using spot_distance. The conversion factor may be adjusted.
circle_size <- spot_distance / 5 # Adjust this constant as needed.
p <- p + geom_point(aes_string(x = "pxl_col_in_fullres", y = "pxl_row_in_fullres", fill = to_plot),
data = df, shape = 21, size = circle_size, stroke = 0
)
} else {
stop("Invalid spot_shape. Allowed values are 'hexagon', 'square', or 'circle'.")
}


p <- p + theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = title_size, hjust = 0.5),
legend.text = element_text(size = font_size),
legend.key.size = unit(legend_size, "points")
) +
ggplot2::labs(title = legend_title, fill = element_blank())

# zoom if requested
Expand Down Expand Up @@ -883,13 +902,20 @@ make_baseplot <- function(spe, df, to_plot, palette = "Mako", transform_scale =
}

# create density plot if requested
suppressMessages(
suppressMessages({
if (density && palette_type != "discrete") {
data <- data.frame(values = sf_poly[[to_plot]], id = rep(to_plot, nrow(sf_poly)))
density <- ggplot2::ggplot(data, mapping = ggplot2::aes_string(x = "values", y = "id")) + # fill... see ggridges docs
# ggplot2::geom_density() +
ggridges::geom_density_ridges() + # _gradient()
# ggplot2::scale_fill_viridis_c() +
# Determine which vector of values to use:
# For hexagons, use the values from sf_poly; for square/circle, use df.
values_data <- if (spot_shape == "hexagon" && exists("sf_poly")) {
sf_poly[[to_plot]]
} else {
df[[to_plot]]
}

data <- data.frame(values = values_data, id = rep(to_plot, length(values_data)))

density_plot <- ggplot2::ggplot(data, mapping = ggplot2::aes_string(x = "values", y = "id")) +
ggridges::geom_density_ridges() +
ggplot2::scale_y_discrete(expand = c(0, 0)) +
ggplot2::geom_vline(
ggplot2::aes(xintercept = mean(unlist(data["values"]))),
Expand All @@ -898,7 +924,6 @@ make_baseplot <- function(spe, df, to_plot, palette = "Mako", transform_scale =
size = 1
) +
ggplot2::theme_classic() +
# ggplot2::ylim(c(0, 1000)) +
ggplot2::theme(
legend.position = "none",
axis.text.y = ggplot2::element_blank(),
Expand All @@ -907,15 +932,13 @@ make_baseplot <- function(spe, df, to_plot, palette = "Mako", transform_scale =
axis.title = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(size = 14)
)
# ggplot2::ylim(0, max(data["values"]))

# cowplot::plot_grid(spatial, density)
plot <- ggpubr::ggarrange(p, density, ncol = 2, widths = c(2, 1)) # add functions to pkg.R
# plot <- grid::grid.draw(plot) # add functions to pkg.R
plot <- ggpubr::ggarrange(p, density_plot, ncol = 2, widths = c(2, 1))
} else {
plot <- p
}
)
})


if (save) {
# check if provided path works
Expand Down
3 changes: 2 additions & 1 deletion man/make_baseplot.Rd
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15 changes: 15 additions & 0 deletions vignettes/spacedeconv_data.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,21 @@ Spatial data available as a Seurat Object can be converted into a SpatialExperim
spe <- seurat_to_spatialexperiment(seurat_object)
```

# VisiumHD Data

For high-resolution VisiumHD data, the `VisiumIO` package provides functions to load and convert your data into a `SpatialExperiment` object. For additional information please check the VisiumIO [Documentation](https://github.com/waldronlab/VisiumIO).

Below is an example of how to load VisiumHD data:

```{r visiumHDData, eval=FALSE}
library(VisiumIO)
tenxData <- TENxVisiumHD(
spacerangerOut = "path/to/VisiumHD/data",
bin_size = "016"
)
spe <- import(tenxData)
```

# Normalization
SpaceDeconv offers an additional function for convenient normalization of SpatialExperiments. The normalization is saved in a new assay, so make sure the correct data is used during deconvolution by providing the desired assay with the parameters `assay_sc` and `assay_sp`. As normalization method `cpm` and `logcpm` are available.

Expand Down