prep to push + remove few bad q interacts

DESCRIPTION

@@ -75,7 +75,6 @@ Suggests:
     markdown,
     testthat (>= 3.0.0),
     sessioninfo,
-    logger,
     Seurat,
     entropy,
     circlize,

NEWS.md

@@ -14,6 +14,9 @@ being done on both .expr and .sum columns. These are now seperated into `columns
 are the ones for which I account for complexes, and `add_cols`, the ones that
 are additional - no need to account for complexes (e.g. also `global_mean`).
 
+## Minor Changes
+- I now refer to [SCPubr](https://enblacar.github.io/SCpubr-book/index.html) in liana's tutorial.
+
 
 ## Minor changes
 

R/liana_specs.R

@@ -258,4 +258,3 @@ setClass("ScoreSpecifics",
             )
     )
 }
-

R/natmi_pipe.R

@@ -102,17 +102,15 @@ call_natmi <- function(
     .csv_path = file.path(.input_path, expr_file)
 
     if(!dir.exists(file.path(.input_path))){
-        log_success(str_glue("Input path created: {.input_path}"))
+        print(str_glue("Input path created: {.input_path}"))
         dir.create(file.path(.input_path), recursive = TRUE)
     }
 
-        log_success(str_glue("Output path created: {.output_path}"))
+        print(str_glue("Output path created: {.output_path}"))
         dir.create(file.path(.output_path), recursive = TRUE)
 
-        print(.csv_path)
-
     if(.overwrite_data || !file.exists(.csv_path)){
-        log_info(str_glue("Writing EM to {.csv_path}"))
+        print(str_glue("Writing EM to {.csv_path}"))
         if(assay.type=="counts"){
             write.csv(GetAssayData(object = sce,
                                    assay = "RNA",
@@ -129,13 +127,13 @@ call_natmi <- function(
         }
     }
 
-    log_info(str_glue("Writing Annotations to {.input_path}/{meta_file}"))
+    print(str_glue("Writing Annotations to {.input_path}/{meta_file}"))
     write.csv(Idents(sce) %>%
                   enframe(name="barcode", value="annotation"),
               file = file.path(.input_path, meta_file),
               row.names = FALSE)
 
-    log_info(str_glue("Saving resource to {.natmi_path}/lrdbs/{reso_name}"))
+    print(str_glue("Saving resource to {.natmi_path}/lrdbs/{reso_name}"))
     # Deal with Default (i.e. NULL)
     if(is.null(op_resource)){
         reso_name <- "lrc2p"

vignettes/liana_tutorial.Rmd

@@ -159,8 +159,6 @@ and receptor are uniquely expressed in a given pair of cells.
 Expression magnitude, represented by [SingleCellExperiment's LRScore](https://academic.oup.com/nar/article/48/10/e55/5810485), is on the other hand, 
 meant to represent a non-negative regularized score, comparable between datasets.
 
-For more advanced visualization options, we refer the user to [SCPubr](https://enblacar.github.io/SCpubr-book/ligand-receptor-analysis.html).
-
 
 ## Frequency Heatmap
 We will now plot the frequencies of interactions for each pair of *potentially* communicating cell types.
@@ -210,6 +208,9 @@ p <- chord_freq(liana_trunc,
 ```
 
 
+For more advanced visualization options, we kindly refer the user to [SCPubr](https://enblacar.github.io/SCpubr-book/ligand-receptor-analysis.html).
+
+
 ## Run any method of choice.
 
 We will now run only the CellPhoneDB's permutation-based algorithm.