make_circos_one_group | R Documentation |
make_circos_one_group
Make a circos plot with top prioritized ligand-receptor interactions for one group of interest.
make_circos_one_group(prioritized_tbl_oi, colors_sender, colors_receiver)
prioritized_tbl_oi |
Subset of 'prioritization_tables$group_prioritization_tbl': the ligand-receptor interactions shown in this subset will be visualized: recommended to consider the top n LR interactions of a group of interest, based on the prioritization_score (eg n = 50; see vignettes for examples). |
colors_sender |
Named vector of colors associated to each sender cell type. Vector = color, names = sender names. If sender and receiver cell types are the same, recommended that this vector is the same as 'colors_receiver'. |
colors_receiver |
Named vector of colors associated to each receiver cell type. Vector = color, names = sender names. Vector = color, names = sender names. If sender and receiver cell types are the same, recommended that this vector is the same as 'colors_receiver'. |
a list with a circos plot for one group of interest, and a legend showing the color corresponding to each sender/receiver cell type.
## Not run:
library(dplyr)
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% dplyr::distinct(ligand, receptor)
ligand_target_matrix = readRDS(url("https://zenodo.org/record/3260758/files/ligand_target_matrix.rds"))
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
batches = NA
contrasts_oi = c("'High-Low','Low-High'")
contrast_tbl = tibble(contrast = c("High-Low","Low-High"), group = c("High","Low"))
output = multi_nichenet_analysis(
sce = sce,
celltype_id = celltype_id,
sample_id = sample_id,
group_id = group_id,
batches = batches,
lr_network = lr_network,
ligand_target_matrix = ligand_target_matrix,
contrasts_oi = contrasts_oi,
contrast_tbl = contrast_tbl
)
## End(Not run)
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