Merge remote-tracking branch 'upstream/upstream_fork'

.github/ISSUE_TEMPLATE/issue_template.md

@@ -3,7 +3,7 @@ name: Bug report or feature request
 about: Describe a bug you've seen or make a case for a new feature
 ---
 
-Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on <https://stackoverflow.com/> or <https://community.rstudio.com/>.
+Please briefly describe your problem and what output you expect.
 
 Please include a minimal reproducible example (AKA a reprex). If you've never heard of a [reprex](http://reprex.tidyverse.org/) before, start by reading <https://www.tidyverse.org/help/#reprex>.
 

.github/workflows/check-bioc.yml

@@ -51,9 +51,9 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - { os: ubuntu-latest, r: '4.1', bioc: '3.13', cont: "bioconductor/bioconductor_docker:RELEASE_3_13", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" }
-          - { os: macOS-latest, r: '4.1', bioc: '3.13'}
-          - { os: windows-latest, r: '4.1', bioc: '3.13'}
+          - { os: ubuntu-latest, r: '4.1', bioc: '3.14', cont: "bioconductor/bioconductor_docker:RELEASE_3_14", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" }
+          - { os: macOS-latest, r: '4.1', bioc: '3.14'}
+          - { os: windows-latest, r: '4.1', bioc: '3.14'}
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       RSPM: ${{ matrix.config.rspm }}

R/statistic-enricher.R

@@ -1,7 +1,7 @@
 #' Enricher Python module used to predict the activity of regulatory proteins
 #'
 #' @description
-#' Calculates regulatory activities using Enricher.
+#' Calculates regulatory activities using Enricher
 #'
 #' @details
 #' This function is a wrapper for the python method [enricher.enrich()].
@@ -14,6 +14,8 @@
 #' @param thresh.filter Float, Prior to normalization remove features that have a standard deviation per feature less
 #' than {thresh_filter}
 #' @param scaler_type Character indicating whether to scale and by what method to scale dataset
+#' @param enr_type Character indicating which enrichment slot to use 
+#' @param enr_weights Character indicating out file handle for enrichment regulon weights 
 #' @return A long format tibble of the enrichment scores for each source
 #'  across the samples. Resulting tibble contains the following columns:
 #'  1. `statistic`: Indicates which method is associated with which score.
@@ -40,24 +42,37 @@ run_enrich <- function(mat,
                       network,
                       .source = .data$source,
                       .target = .data$target,
-                      minsize = 0,
-                       scaler_type = NULL,
+                      .mor = .data$mor,
+                      .likelihood = .data$likelihood,
+                      minsize = 5,
+                      scaler_type = NULL,
+                      enr_type = 'total_enrichment',
+                      enr_weights = 'weights.tsv',
                       ...) {
     # Check for NAs/Infs in mat
     check_nas_infs(mat)
-    use_condaenv('base')
-    enr <- import('priori.priori')
+    use_condaenv('enrich_env')
+    enr <- import('enricher.enrich')
+
     # Before to start ---------------------------------------------------------
     network <- network %>%
-        convert_to_enricher({{ .source }}, {{ .target }})
+        rename_net({{ .source }}, {{ .target }}, {{ .mor }}, {{ .likelihood }})
+    network <- filt_minsize(rownames(mat), network, minsize)
+    network <- network %>% convert_f_defaults( UpGene = source, DownGene = target) %>% dplyr::mutate(Type = 'controls-expresssion-of') %>% select(UpGene,Type, DownGene) 
     pd_network <- enr$pd$DataFrame(network)
     pd_mat <- enr$pd$DataFrame(mat, index=rownames(mat), columns=colnames(mat))
+
     # Analysis ----------------------------------------------------------------
-    enr_scores <- enr$Priori(cohort='decoupler',expr=pd_mat,regulon=pd_network,regulon_size=minsize)
+    enr_scores <- enr$Enrichment(cohort='decoupler',expr=pd_mat,regulon=pd_network,regulon_size=minsize)
     enr_scores$scale(scaler_type=scaler_type)
     enr_scores$assign_weights()
     enr_scores$calculate_enrichment()
-    t(enr_scores$enrichment) %>%
+    weights <- enr_scores[['regulon_weights']]
+    write.table(weights,file=enr_weights,sep='\t',quote=F)
+    scores <- enr_scores[[enr_type]]
+    if (enr_type %in% c('total_enrichment','local_enrichment','delta')){
+        scores <- t(scores)}
+    scores %>%
     as.data.frame() %>%
     rownames_to_column("source") %>%
     pivot_longer(-.data$source, names_to = "condition", values_to = "score") %>%