add param

NEWS.md

@@ -1,4 +1,4 @@
-# LIANA 0.1.9 (05.12.22)
+# LIANA 0.1.9 (13.12.22)
 
 ## Changes
 - `return_all` parameter was included to `liana_wrap`. `return_all` enables
@@ -17,7 +17,8 @@ This deals with edge cases with non-expression ligand/receptor scores (e.g. z-sc
 the lower score subunit is the one with higher expression proportion. Hence,
 this is intended to make all methods consistent according to which interactions
 are returned, regardless of which subunit has the lower score.
-
+- `aggregate_how` parameter added to `liana_aggregate` to allow the aggregation
+by specificity and magnitude scores.
 
 
 # LIANA 0.1.8 (08.11.22)

R/liana_aggregate.R

@@ -1,6 +1,9 @@
 #' Function to Aggregate CCC Method Results
 #'
 #' @param liana_res LIANA results
+#' @param aggregate_how way to aggregate, by default (NULL) will aggregate
+#'  all passed methods with the approach specified in `liana:::..score_specs`.
+#'  Alternative options are `magnitude` and `specificity`.
 #' @param set_cap Function used to set ranked cap (i.e. the value that is
 #'    assigned to interactions with NA for scores);
 #'    By default, this is set to "max", which is the maximum number of interactions
@@ -49,6 +52,7 @@
 #' # aggregate results from multiple methods
 #' liana_res <- liana_aggregate(liana_res)
 liana_aggregate <- function(liana_res,
+                            aggregate_how=NULL,
                             resource = NULL,
                             set_cap = "max",
                             cap = NULL,
@@ -59,6 +63,21 @@ liana_aggregate <- function(liana_res,
                             join_cols = NULL,
                             ...){
 
+    # define approach to aggregate
+    if(!is.null(aggregate_how)){
+        if(aggregate_how=="magnitude"){
+            score_mode = liana:::.score_housekeep()
+        } else if(aggregate_how=="specificity"){
+            specs <- liana:::.score_specs()
+            specs$sca <- NULL # remove SingleCellSignalR score
+            score_mode = specs
+        } else{
+            stop("Please specify an existing aggregate approach!")
+        }
+    } else{
+        score_mode <- .score_mode()
+    }
+
     if(!is_tibble(liana_res[[1]]) && is.null(resource)){
         stop("Please provide provide a name for the resource ",
              "to be plucked and used to aggregate the method results!")
@@ -88,7 +107,7 @@ liana_aggregate <- function(liana_res,
     liana_mlist <- liana_res %>%
         map2(names(.), function(res, method_name){
 
-            if(is.null(.score_mode()[[method_name]])){
+            if(is.null(score_mode[[method_name]])){
                 liana_message(
                     str_glue(
                         "Unknown method name or missing specifics for: {method_name}"
@@ -100,8 +119,8 @@ liana_aggregate <- function(liana_res,
                               verbose = verbose)
             }
 
-            method_score <- .score_mode()[[method_name]]@method_score
-            desc_order <- .score_mode()[[method_name]]@descending_order
+            method_score <- score_mode[[method_name]]@method_score
+            desc_order <- score_mode[[method_name]]@descending_order
 
             .method = sym(as.character(str_glue("{method_name}.{method_score}")))
             .rank_col = sym(as.character(str_glue("{method_name}.rank")))

tests/testthat/test-liana_aggr.R

@@ -23,7 +23,7 @@ test_that("Test liana aggregate (housekeep)", {
     testthat::expect_warning(
         res2 <- readRDS(file.path(liana_path, "testdata",
                                   "output", "liana_res_plus.RDS")) %>%
-            liana_aggregate(.score_mode = .score_housekeep)
+            liana_aggregate(aggregate_how = "magnitude")
     )
 
 

vignettes/liana_nichenet.Rmd

@@ -46,7 +46,7 @@ library(nichenetr)
 library(Seurat)
 library(ggrepel)
 library(cowplot)
-options(timeout=300) # required to download expression data /w slow connection
+options(timeout=600) # required to download expression data /w slow connection
 ```
 
 Then, we load and prepare the single-cell data, NicheNet's model weights, and the gene set of interest. The latest is composed by genes that are known to participate in the partial epithelial-mesenchymal transition (p-EMT) program, as defined in [NicheNet's vignette](https://github.com/saeyslab/nichenetr/blob/master/vignettes/ligand_activity_geneset.md).