Plots dotplot to compare ligand-receptor rankings from spatial and expression information
plotRankSpatvsExpr(
gobject,
combCC,
expr_rnk_column = "LR_expr_rnk",
spat_rnk_column = "LR_spat_rnk",
dot_color_gradient = NULL,
midpoint = deprecated(),
gradient_midpoint = 10,
gradient_style = c("divergent", "sequential"),
size_range = c(0.01, 1.5),
xlims = NULL,
ylims = NULL,
selected_ranks = c(1, 10, 20),
show_plot = NULL,
return_plot = NULL,
save_plot = NULL,
save_param = list(),
default_save_name = "plotRankSpatvsExpr"
)Arguments
- gobject
giotto object
- combCC
combined communication scores from
combCCcom- expr_rnk_column
column with expression rank information to use
- spat_rnk_column
column with spatial rank information to use
- dot_color_gradient
character. continuous colors to use. palette to use or vector of colors to use (minimum of 2).
- midpoint
deprecated
- gradient_midpoint
numeric. default = 10. midpoint of colors
- gradient_style
either 'divergent' (midpoint is used in color scaling) or 'sequential' (scaled based on data range)
- size_range
size ranges of dotplot
- xlims
x-limits, numerical vector of 2
- ylims
y-limits, numerical vector of 2
- selected_ranks
numerical vector, will be used to print out the percentage of top spatial ranks are recovered
- show_plot
logical. show plot
- return_plot
logical. return ggplot object
- save_plot
logical. save the plot
- save_param
list of saving parameters, see
showSaveParameters- default_save_name
default save name for saving, don't change, change save_name in save_param
Value
ggplot
Examples
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> cell_spatInfo_spatVector.shp
#> cell
#>
#> 3.2 read Giotto spatial centroid information
#> cell
#>
#> 3.3 read Giotto spatial overlap information
#> No overlaps were found, overlap loading will be
#> skipped
#>
#> 4. read Giotto image information
#> a giotto python environment was found
#> Using python path:
#> "/Users/yuanlab/Library/r-miniconda/envs/giotto_env/bin/pythonw"
exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus",
feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
random_iter = 10)
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10 <- Inf returned
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10
#> simulations: 1 2 3 4 5 6 7 8 9 10 <- Inf returned
#> Warning: no adjusted p.values that are not zero; returning Inf
#> Warning: no adjusted p.values that are not zero; returning Inf
combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
plotRankSpatvsExpr(gobject = g, combCC = combCC)
#> for top 1 expression ranks, you recover 17.24 % of the highest spatial rankfor top 10 expression ranks, you recover 62.07 % of the highest spatial rankfor top 20 expression ranks, you recover 89.66 % of the highest spatial rank
