change expression filtering (>= instead of >)

R/expression_processing.R

@@ -607,9 +607,9 @@ get_frac_exprs = function(sce, sample_id, celltype_id, group_id, batches = NA, m
     print(paste0("Genes expressed in at least ",n_min, " samples will considered as expressed in the cell type: ",celltype_oi)) 
   }
 
-  frq_df = frq_df %>% dplyr::inner_join(grouping_df) %>% dplyr::mutate(expressed_sample = fraction_sample > fraction_cutoff)
+  frq_df = frq_df %>% dplyr::inner_join(grouping_df) %>% dplyr::mutate(expressed_sample = fraction_sample >= fraction_cutoff)
 
-  expressed_df = frq_df %>% inner_join(n_smallest_group_tbl) %>% inner_join(abundance_data) %>% dplyr::group_by(gene, celltype) %>% dplyr::summarise(n_expressed = sum(expressed_sample)) %>% dplyr::mutate(expressed = n_expressed > n_min) %>% distinct(celltype, gene, expressed)
+  expressed_df = frq_df %>% inner_join(n_smallest_group_tbl) %>% inner_join(abundance_data) %>% dplyr::group_by(gene, celltype) %>% dplyr::summarise(n_expressed = sum(expressed_sample)) %>% dplyr::mutate(expressed = n_expressed >= n_min) %>% distinct(celltype, gene, expressed)
   for(i in seq(length(unique(expressed_df$celltype)))){
     celltype_oi = unique(expressed_df$celltype)[i]
     n_genes = expressed_df %>% filter(celltype == celltype_oi) %>% filter(expressed == TRUE) %>% pull(gene) %>% unique() %>% length()

vignettes/basic_analysis_steps_MISC.Rmd

@@ -506,6 +506,20 @@ combined_plot
 
 __Note__ Use `make_DEgene_dotplot_pseudobulk_batch` if you want to indicate the batch of each sample to the plot
 
+What if there is a specific ligand you are interested in?
+
+```{r}
+group_oi = "M"
+receiver_oi = "L_T_TIM3._CD38._HLADR."
+ligands_oi = c("IFNG","IL15")
+prioritized_tbl_ligands_oi = get_top_n_lr_pairs(multinichenet_output$prioritization_tables, 10000, groups_oi = group_oi, receivers_oi = receiver_oi) %>% filter(ligand %in% ligands_oi) # ligands should still be in the output tables of course
+```
+
+```{r, fig.width=20, fig.height=10}
+combined_plot = make_ligand_activity_target_plot(group_oi, receiver_oi, prioritized_tbl_ligands_oi, multinichenet_output$prioritization_tables, multinichenet_output$ligand_activities_targets_DEgenes, contrast_tbl, multinichenet_output$grouping_tbl, multinichenet_output$celltype_info, ligand_target_matrix, plot_legend = FALSE)
+combined_plot
+```
+
 ## Visualization of expression-correlated target genes of ligand-receptor pairs
 
 Before, we had calculated the correlation in expression between ligand-receptor pairs and DE genes. Now we will filter out correlated ligand-receptor --> target links that both show high expression correlation (spearman or activity > 0.50 in this example) and have some prior knowledge to support their link.