Liana analysis L6

1. load libraries

2. load seurat object

#Load Seurat Object
L6 <- readRDS("../../0-RDS_Cell_lines/L6_clustered.rds")


DefaultAssay(L6) <- "SCT"

3. Liana analysis using scPubr

Idents(L6) <- "SCT_snn_res.0.3"

liana_output <- liana::liana_wrap(sce = L6,
                                  method = c("natmi", "connectome", "logfc", "sca", "cellphonedb", "CellChat"),
                                  idents_col = NULL,
                                  verbose = FALSE,
                                  assay = "SCT")

Expression from the `SCT` assay will be used
Avis : The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.Avis : `invoke()` is deprecated as of rlang 0.4.0.

Ligand Receptor analysis plot

for (name in names(liana_output)) {
    # Save each component to a CSV file
    write.csv(liana_output[[name]], paste0("liana_L6_", name, "_L6_results.csv"), row.names = FALSE)
}

4. Liana Visualization-p1

# Ligand Receptor analysis plot.
p1 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output)
p1

NA
NA

Liana Visualization-p2

# Arrange the interactions by aggregate_rank
# Ligand Receptor analysis plot with extra interactions.
p2 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   arrange_interactions_by = "aggregate_rank",
                                   top_interactions = 10)
p2

Liana Visualization-p3

# Arrange the interactions by specificity
# Ligand Receptor analysis plot with extra interactions.
p3 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   arrange_interactions_by = "specificity",
                                   top_interactions = 10)
p3

Liana Visualization-p4

# Arrange the interactions by magnitude
# Ligand Receptor analysis plot with extra interactions.
p4 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   arrange_interactions_by = "magnitude",
                                   top_interactions = 10)
p4

Liana Visualization-p5

# Arrange the interactions by both magnitude and specificity.
# Ligand Receptor analysis plot with extra interactions.
p5 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   arrange_interactions_by = "both",
                                   top_interactions = 10)
p5

Liana Visualization-p6

# Arrange the interactions alphabetically.
# Ligand Receptor analysis plot with extra interactions.
p6 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   sort_interactions_alphabetically = TRUE,
                                   top_interactions = 10)
p6

Liana Visualization-top significant interactions plotted-p7

# Ligand Receptor analysis plot with extra interactions.
p7 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   top_interactions = 30)
p7

Liana Visualization-p8-Add or remove missing source-target combinations

# Add missing LR combinations.
P1 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                    add_missing_LR_combinations = TRUE)

# Restrict the results to only the LR combinations that have a value.
P2 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                    add_missing_LR_combinations = FALSE)
P <- P1 /P2

P

# Ligand Receptor analysis plot with grid.
p8 <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   grid.color = "steelblue")
p8

Compute chord diagrams of the interactions-p9

# Chord diagram of the total significant interactions from each cluster and all the rest.
out <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                     top_interactions = 25,
                                     compute_ChordDiagrams = TRUE)
out$chord_total_interactions

out$dotplot

out$chord_ligand_receptor