Fixed issue LR-T correlation for n < 5 for all cell types + add verbose

Author: browaeysrobin

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: multinichenetr
 Type: Package
 Title: Cell-Cell Communication analysis for scRNAseq data with complex multi-sample multi-group designs 
-Version: 1.0.0
+Version: 1.0.1
 Author: person("Robin", "Browaeys", email = "robin.browaeys@ugent.be",
   role = c("aut", "cre"))
 Maintainer: Robin Browaeys <robin.browaeys@ugent.be>
@@ -59,4 +59,4 @@ Suggests:
     tidyverse
 Remotes: 
   github::saeyslab/nichenetr
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.3

R/lr_target_correlation.R

@@ -204,7 +204,13 @@ lr_target_prior_cor_inference = function(receivers_oi, abundance_expression_info
   # ligand_target_df = ligand_target_df %>% dplyr::mutate(scaled_prior_score = 0.5*(scaled_ligand + scaled_target))
 
   # Step4: Combine the ligand-target prior information scores and combine with the correlation based ones!
-  cor_prior_df = lr_target_cor %>% dplyr::inner_join(ligand_target_df, by = c("ligand", "target")) %>% dplyr::mutate(id_target = paste(id, target, sep = "_")) %>% dplyr::ungroup() 
+  if(nrow(lr_target_cor) > 0){
+    cor_prior_df = lr_target_cor %>% dplyr::inner_join(ligand_target_df, by = c("ligand", "target")) %>% dplyr::mutate(id_target = paste(id, target, sep = "_")) %>% dplyr::ungroup() 
+  } else {
+    cor_prior_df = NULL
+    print("For no celltypes, sufficient samples (>= 5) were available for a correlation analysis. lr_target_prior_cor, the output of this function, will be NULL. As a result, not all types of downstream visualizations can be created.")
+  }
+  
 
   # combine prior and correlation information
   # cor_prior_df = cor_prior_df %>% mutate(scaled_cor_score = 0.5*(scaled_pearson + scaled_target_pearson ))

R/pipeline.R

@@ -659,6 +659,9 @@ multi_nichenet_analysis_separate = function(sce_receiver,
 
 
   # Add information on prior knowledge and expression correlation between LR and target expression ------------------------------------------------------------------------------------------------------------
+  if(verbose == TRUE){
+    print("Calculate correlation between LR pairs and target genes")
+  }
   lr_target_prior_cor = lr_target_prior_cor_inference(prioritization_tables$group_prioritization_tbl$receiver %>% unique(), abundance_expression_info, celltype_de_receiver, grouping_tbl, prioritization_tables, ligand_target_matrix, logFC_threshold = logFC_threshold, p_val_threshold = p_val_threshold, p_val_adj = p_val_adj, top_n_LR = top_n_LR)
 
 
@@ -1149,6 +1152,9 @@ multi_nichenet_analysis_combined = function(sce,
   }
 
   # Add information on prior knowledge and expression correlation between LR and target expression ------------------------------------------------------------------------------------------------------------
+  if(verbose == TRUE){
+    print("Calculate correlation between LR pairs and target genes")
+  }
   lr_target_prior_cor = lr_target_prior_cor_inference(prioritization_tables$group_prioritization_tbl$receiver %>% unique(), abundance_expression_info, celltype_de, grouping_tbl, prioritization_tables, ligand_target_matrix, logFC_threshold = logFC_threshold, p_val_threshold = p_val_threshold, p_val_adj = p_val_adj, top_n_LR = top_n_LR)
 
   multinichenet_output = list(

README.Rmd

@@ -17,7 +17,7 @@ rmarkdown::render("README.Rmd",output_format = "md_document")
 [![Coverage Status](https://codecov.io/gh/browaeysrobin/multinichenetr/branch/master/graph/badge.svg?token=0X627I4TM7)](https://codecov.io/gh/browaeysrobin/multinichenetr)
 <!-- badges: end -->
 
-**multinichenetr: the R package containing multiple functionalities to computationally study cell-cell communication from single-cell transcriptomics data with complex multi-sample, multi-condition designs.** The goal of this toolbox is to study differences in intercellular communication between groups of samples of interest (eg patients of different disease states). 
+**multinichenetr: the R package for differential cell-cell communication analysis from single-cell transcriptomics data with complex multi-sample, multi-condition designs.** The goal of this toolbox is to study differences in intercellular communication between groups of samples of interest (eg patients of different disease states). 
 
 You can read all about MultiNicheNet in the following preprint: https://www.biorxiv.org/content/10.1101/2023.06.13.544751v1 
 
@@ -93,6 +93,11 @@ When applying MultiNicheNet on datasets with many samples and cell types, it is
 * [Running MultiNicheNet with qsub](vignettes/2-multinichenet_prism_final_MSA.Rmd)
 * [Interpreting MultiNicheNet results locally](vignettes/3-multinichenet_interpretation_MSA_final.Rmd)
 
+## Frequently recurring questions and issues
+
+* Even though it is stated in the vignettes, many reported issues arise because names of celltypes, groups/conditions, and/or samples are not syntactically valid. Before reporting your issue, make sure you satisfy this condition and other conditions described in the vignettes.
+* We strongly recommend having at least 4 samples in each of the groups/conditions you want to compare. With less samples, the benefits of performing a pseudobulk-based DE analysis are less clear and non-multi-sample tools for differential cell-cell communication might be better alternatives. 
+
 ## References
 
 Browaeys, R. et al. MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. (preprint)

README.md

@@ -13,12 +13,11 @@ status](https://github.com/browaeysrobin/multinichenetr/workflows/R-CMD-check-bi
 Status](https://codecov.io/gh/browaeysrobin/multinichenetr/branch/master/graph/badge.svg?token=0X627I4TM7)](https://codecov.io/gh/browaeysrobin/multinichenetr)
 <!-- badges: end -->
 
-**multinichenetr: the R package containing multiple functionalities to
-computationally study cell-cell communication from single-cell
-transcriptomics data with complex multi-sample, multi-condition
-designs.** The goal of this toolbox is to study differences in
-intercellular communication between groups of samples of interest (eg
-patients of different disease states).
+**multinichenetr: the R package for differential cell-cell communication
+analysis from single-cell transcriptomics data with complex
+multi-sample, multi-condition designs.** The goal of this toolbox is to
+study differences in intercellular communication between groups of
+samples of interest (eg patients of different disease states).
 
 You can read all about MultiNicheNet in the following preprint:
 <https://www.biorxiv.org/content/10.1101/2023.06.13.544751v1>
@@ -185,6 +184,19 @@ plots; and 2) interpreting the results and generating visualizations.
 -   [Interpreting MultiNicheNet results
     locally](vignettes/3-multinichenet_interpretation_MSA_final.Rmd)
 
+## Frequently recurring questions and issues
+
+-   Even though it is stated in the vignettes, many reported issues
+    arise because names of celltypes, groups/conditions, and/or samples
+    are not syntactically valid. Before reporting your issue, make sure
+    you satisfy this condition and other conditions described in the
+    vignettes.
+-   We strongly recommend having at least 4 samples in each of the
+    groups/conditions you want to compare. With less samples, the
+    benefits of performing a pseudobulk-based DE analysis are less clear
+    and non-multi-sample tools for differential cell-cell communication
+    might be better alternatives.
+
 ## References
 
 Browaeys, R. et al. MultiNicheNet: a flexible framework for differential