Map categorical features

MapLabels colors spots based on the values of a categorical feature.

Usage

MapLabels(object, ...)

# S3 method for default
MapLabels(
  object,
  crop_area = NULL,
  pt_size = 1,
  pt_alpha = 1,
  pt_stroke = 0,
  section_number = NULL,
  label_by = NULL,
  split_labels = FALSE,
  ncol = NULL,
  colors = NULL,
  dims = NULL,
  coords_columns = c("pxl_col_in_fullres", "pxl_row_in_fullres"),
  return_plot_list = FALSE,
  drop_na = FALSE,
  add_scalebar = FALSE,
  scalebar_gg = NULL,
  scalebar_height = 0.05,
  scalebar_position = c(0.8, 0.8),
  ...
)

# S3 method for Seurat
MapLabels(
  object,
  column_name,
  image_use = NULL,
  coords_use = "raw",
  crop_area = NULL,
  pt_size = 1,
  pt_alpha = 1,
  pt_stroke = 0,
  section_number = NULL,
  label_by = NULL,
  split_labels = FALSE,
  ncol = NULL,
  colors = NULL,
  override_plot_dims = FALSE,
  return_plot_list = FALSE,
  drop_na = FALSE,
  add_scalebar = FALSE,
  scalebar_height = 0.05,
  scalebar_gg = scalebar(x = 500, text_height = 5),
  scalebar_position = c(0.8, 0.7),
  ...
)

Arguments

object: An object
...: Arguments passed to other methods
crop_area: A numeric vector of length 4 specifying a rectangular area to crop the plots by. These numbers should be within 0-1. The x-axis is goes from left=0 to right=1 and the y axis is goes from top=0 to bottom=1. The order of the values are specified as follows: crop_area = c(left, top, right, bottom). The crop area will be used on all tissue sections and cannot be set for each section individually. using crop areas of different sizes on different sections can lead to unwanted side effects as the point sizes will remain constant. In this case it is better to generate separate plots for different tissue sections.
pt_size: A numeric value specifying the point size passed to geom_point
pt_alpha: A numeric value between 0 and 1 specifying the point opacity passed to geom_point. A value of 0 will make the points completely transparent and a value of 1 will make the points completely opaque.
pt_stroke: A numeric specifying the point stroke width
section_number: An integer select a tissue section number to subset data by
label_by: A character specifying a column name in object with labels that can be used to provide a title for each subplot. This column should have 1 label per tissue section. This can be useful when you need to provide more detailed information about your tissue sections.
split_labels: A logical specifying if labels should be split.
ncol: An integer value specifying the number of columns in the output patchwork.
colors: A character vector of colors to use for the color scale. The number of colors should match the number of labels present.
dims: A tibble with information about the tissue sections. This information is used to determine the limits of the plot area. If dims is not provided, the limits will be computed directly from the spatial coordinates provided in object.
coords_columns: A character vector of length 2 specifying the names of the columns in object holding the spatial coordinates
return_plot_list: logical specifying whether the plots should be return as a list of ggplot objects. If return_plot_list = FALSE (default), the plots will be arranged into a patchwork
drop_na: A logical specifying if NA values should be dropped
add_scalebar: A logical specifying if a scale bar should be added to the plots
scalebar_gg: A 'ggplot' object generated with scalebar. The appearance of the scale bar is styled by passing parameters to scalebar.
scalebar_height: A numeric value specifying the height of the scale bar relative to the height of the full plot area. Has to be a value between 0 and 1. The title of the scale bar is scaled with the plot and might disappear if the down-scaled text size is too small. Increasing the height of the scale bar can sometimes be useful to increase the text size to make it more visible in small plots.
scalebar_position: A numeric vector of length 2 specifying the position of the scale bar relative to the plot area. Default is to place it in the top right corner.
column_name: A character specifying a meta data column holding the categorical feature vector.
image_use: A character specifying image type to use.
coords_use: A character specifying coordinate type to use.
override_plot_dims: A logical specifying whether the image dimensions should be used to define the plot area. Setting override_plot_dims can be useful in situations where the tissue section only covers a small fraction of the capture area, which will create a lot of white space in the plots. The same effect can be achieved with the crop_area but the crop area is instead determined directly from the data.

Value

A patchwork object or a list of ggplot objects

Details

You can only plot categorical features with MapLabels, for example: spot annotations or clusters. If you want to plot numerical features, use MapFeatures instead. Only 1 categorical feature can be provided.

Author

Ludvig Larsson

Examples


library(semla)

# Load Seurat object
se_mbrain <- readRDS(system.file("extdata/mousebrain", "se_mbrain", package = "semla"))

# Run PCA and data-driven clustering
se_mbrain <- se_mbrain |>
  ScaleData() |>
  RunPCA() |>
  FindNeighbors(reduction = "pca", dims = 1:10) |>
  FindClusters(resolution = 0.2) |>
  FindClusters(resolution = 0.3)
#> Centering and scaling data matrix
#> PC_ 1 
#> Positive:  Nrgn, Olfm1, Cck, Nptxr, Rtn1, Snca, Nov, Tmsb4x, Lamp5, Egr1 
#> 	   Crym, Cpne6, Coro1a, Arc, Hpca, Sst, Nr4a1, Npy, Chgb, Neurod6 
#> 	   Snap25, Myh7, Uchl1, Eef1a2, Cort, Grp, Stmn2, Rprm, Spink8, Mfge8 
#> Negative:  Mbp, Plp1, Apod, Mobp, Ptgds, Mog, Mal, Mag, Cnp, Aldh1a1 
#> 	   Opalin, Tcf7l2, Ddc, Lhx1os, Slc6a3, Ret, Th, Slc18a2, Sncg, Drd2 
#> 	   Chrna6, Slc10a4, Spp1, En1, Dlk1, Calb2, Hbb-bs, Pvalb, Col1a2, Tnnt1 
#> PC_ 2 
#> Positive:  Myoc, Gfap, Col1a2, Fmod, Slc13a4, Hba-a1, Hbb-bt, Slc6a20a, Hba-a2, Hbb-bs 
#> 	   Acta2, Tagln, Mgp, Ogn, Vtn, H2-Aa, Lyz2, Cd74, Myl9, Dcn 
#> 	   Myh11, Ptgds, Cytl1, H2-Eb1, Hmgcs2, Clu, Mfge8, Emp1, Npy, Cnn1 
#> Negative:  Th, Uchl1, Slc18a2, En1, Slc10a4, Slc6a3, Chrna6, Stmn2, Ret, Snap25 
#> 	   Drd2, Dlk1, Ddc, Scg2, Sncg, Eef1a2, Rtn1, Chga, Pcp4, Calb2 
#> 	   Mobp, Mbp, Chgb, Fabp5, Plp1, Pvalb, Mog, Mal, Mag, Snca 
#> PC_ 3 
#> Positive:  Trbc2, Arc, Egr1, Myl4, Nr4a1, Mbp, Mobp, Pvalb, Plp1, Opalin 
#> 	   Mog, Mal, Cnp, Mag, Snap25, Lamp5, Ighm, Tcf7l2, Cplx3, Tgm3 
#> 	   Ighg2c, Pcp4, Hpca, Ly6d, Eef1a2, Tnnt1, Chga, Neurod6, Prph, Ctgf 
#> Negative:  Nnat, Slc18a2, Dlk1, Slc6a3, Slc10a4, En1, Th, Chrna6, Dcn, Sncg 
#> 	   Cpne7, Drd2, Ddc, Ret, Hpcal1, Ecel1, Cpne6, Col1a2, Trh, Calb2 
#> 	   Cd24a, Fmod, Mgp, Snca, Lypd1, Slc13a4, Fibcd1, Crym, Spink8, Slc6a20a 
#> PC_ 4 
#> Positive:  Nr4a1, Arc, Lamp5, Egr1, Myl4, Trbc2, Chrna6, Tagln, En1, Snap25 
#> 	   Th, Slc18a2, Acta2, Col1a2, Myh11, Fmod, Hba-a2, Slc10a4, Slc6a3, Slc13a4 
#> 	   Ret, Tgm3, Hbb-bt, Slc6a20a, Ighm, Hbb-bs, Hba-a1, Vtn, Mgp, Drd2 
#> Negative:  Spink8, Fibcd1, Tmsb4x, Nnat, Lefty1, Crym, Cpne7, Nos1, Dcn, Cpne6 
#> 	   Grp, Homer3, Htr3a, Tac1, Fabp5, C1ql2, Trh, Opalin, Mog, Plp1 
#> 	   Mag, Calb2, Ecel1, Gfap, Cnp, Tcf7l2, Lypd1, Vgll3, Hpcal1, Mal 
#> PC_ 5 
#> Positive:  En1, Chrna6, Th, Slc18a2, Slc10a4, Ddc, Lefty1, Arc, Slc6a3, Neurod6 
#> 	   Grp, Nov, Lamp5, Fibcd1, Nr4a1, Drd2, Spink8, Vip, Myl4, Ret 
#> 	   Egr1, Dlk1, Nrgn, Gfap, Trbc2, Tgm3, Myh7, Tac2, Sncg, Npy 
#> Negative:  Pcp4, Tcf7l2, Snap25, Uchl1, Eef1a2, Chga, Stmn2, Lhx1os, Calb2, Scg2 
#> 	   Fabp5, Bok, Prkcd, Pvalb, Cartpt, Chgb, Tnnt1, Gpx3, Slc20a1, Rtn1 
#> 	   C1ql2, Spp1, Hpcal1, Ptgds, Pitx2, Lypd1, Aldh1a1, Ecel1, Vtn, Nme7 
#> Computing nearest neighbor graph
#> Computing SNN
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 2560
#> Number of edges: 85218
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.9096
#> Number of communities: 6
#> Elapsed time: 0 seconds
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 2560
#> Number of edges: 85218
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8881
#> Number of communities: 8
#> Elapsed time: 0 seconds

# Plot clusters
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 2)


# Plot clusters in split view
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 0.5,
          section_number = 1, split_labels = TRUE, ncol = 4)

          
# \donttest{

# Combine plots with different labels
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2") |
  MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.3")


# Move legend to the right side of the plot
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 2) &
  theme(legend.position = "right")


# Override fill aesthetic to increase point sixe in legend
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 2) &
  guides(fill = guide_legend(override.aes = list(size = 4)))


# Use custom colors
cols <- c("#332288", "#88CCEE", "#117733", "#DDCC77", "#CC6677","#AA4499")
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 2, colors = cols)


# Factor are to used to determine the color order. If you change the
# levels of your label of interest, the labels and colors will change order
se_mbrain$Spatial_snn_res.0.2 <- factor(se_mbrain$Spatial_snn_res.0.2,
                                        levels = sample(paste0(0:7), size = 8))
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 2, colors = cols)


# Control what group label colors by naming the color vector
# this way you can make sure that each group gets a desired color
# regardless of the factor levels
cols <- setNames(cols, nm = paste0(0:5))
cols
#>         0         1         2         3         4         5 
#> "#332288" "#88CCEE" "#117733" "#DDCC77" "#CC6677" "#AA4499" 
MapLabels(se_mbrain, column_name = "Spatial_snn_res.0.2", pt_size = 2, colors = cols)

# }