accept df

Author: stemangiola

NAMESPACE

@@ -87,6 +87,7 @@ importFrom(GenomicRanges,makeGRangesListFromDataFrame)
 importFrom(Matrix,colSums)
 importFrom(S4Vectors,metadata)
 importFrom(SummarizedExperiment,SummarizedExperiment)
+importFrom(SummarizedExperiment,as.data.frame)
 importFrom(SummarizedExperiment,assays)
 importFrom(SummarizedExperiment,colData)
 importFrom(SummarizedExperiment,rowData)

R/functions_SE.R

@@ -1446,6 +1446,33 @@ univariable_differential_tissue_composition_SE = function(
 		unnest(surv_test, keep_empty = TRUE)
 }
 
+.resolve_complete_confounders_of_non_interest_df <- function(df, ...){
+  
+  combination_of_factors_of_NON_interest =
+    # Factors
+    df |> 
+    as_tibble(rownames = ".sample") |> 
+    select(...) |>
+    suppressWarnings() |>
+    colnames() |>
+    
+    # Combinations
+    combn(2) |>
+    t() |>
+    as_tibble() |>
+    set_names(c("factor_1", "factor_2"))
+  
+  for(i in combination_of_factors_of_NON_interest |> nrow() |> seq_len()){
+    df =
+      df |>
+      resolve_complete_confounders_of_non_interest_pair_df(
+        !!as.symbol(combination_of_factors_of_NON_interest[i,]$factor_1),
+        !!as.symbol(combination_of_factors_of_NON_interest[i,]$factor_2)
+      )
+  }
+  
+  df
+}
 
 #' Resolve Complete Confounders of Non-Interest
 #'
@@ -1475,13 +1502,13 @@ univariable_differential_tissue_composition_SE = function(
 #' # se is a SummarizedExperiment object
 #' resolve_complete_confounders_of_non_interest(se, .factor_1 = factor1, .factor_2 = factor2)
 #' @noRd
-resolve_complete_confounders_of_non_interest_pair_SE <- function(se, .factor_1, .factor_2){
+resolve_complete_confounders_of_non_interest_pair_df <- function(df, .factor_1, .factor_2){
 
   .factor_1 <- enquo(.factor_1)
   .factor_2 <- enquo(.factor_2)
 
   cd =
-    colData(se) |>
+    df |>
     as_tibble() |>
     rowid_to_column() |>
     distinct(rowid, !!.factor_1, !!.factor_2) |>
@@ -1516,15 +1543,12 @@ resolve_complete_confounders_of_non_interest_pair_SE <- function(se, .factor_1,
     cd = cd |>
       mutate(!!.factor_2 := if_else(n1 + n2 < 3, dummy_factor_2, !!.factor_2))
   }
-
-  colData(se)[,c(quo_name(.factor_1), quo_name(.factor_2))] =
+  
+  df[,c(quo_name(.factor_1), quo_name(.factor_2))] =
     cd |>
     unnest(se_data) |>
     arrange(rowid) |>
-    select(!!.factor_1, !!.factor_2) |>
-    DataFrame()
-
-  se
+    select(!!.factor_1, !!.factor_2) 
 
+  df
 }
-

R/methods.R

@@ -4914,24 +4914,70 @@ as_matrix <- function(tbl,
 
 #' Resolve Complete Confounders of Non-Interest
 #'
-#' This generic function processes a SummarizedExperiment object to handle confounders
-#' that are not of interest in the analysis. It dynamically handles combinations
-#' of provided factors, adjusting the data by nesting and summarizing over these factors.
+#' This function identifies and resolves complete confounders among specified factors of non-interest within a `SummarizedExperiment` object. 
+#' Complete confounders occur when the levels of one factor are entirely predictable based on the levels of another factor. 
+#' Such relationships can interfere with downstream analyses by introducing redundancy or collinearity.
 #'
+#' The function systematically examines pairs of specified factors and determines whether they are completely confounded. 
+#' If a pair of factors is found to be confounded, one of the factors is adjusted or removed to resolve the issue. 
+#' The adjusted `SummarizedExperiment` object is returned, preserving all assays and metadata except the resolved factors.
 #'
-#' @param se A SummarizedExperiment object that contains the data to be processed.
-#' @param ... Arbitrary number of factor variables represented as symbols or quosures
-#'            to be considered for resolving confounders. These factors are processed
-#'            in combinations of two.
+#' @param se A `SummarizedExperiment` object. This object contains assay data, row data (e.g., gene annotations), and column data (e.g., sample annotations).
+#' @param ... Factors of non-interest (column names from `colData(se)`) to examine for complete confounders.
 #'
-#' @rdname resolve_complete_confounders_of_non_interest-methods
+#' @details 
+#' Complete confounders of non-interest can create dependencies between variables that may bias statistical models or violate their assumptions. 
+#' This function systematically addresses this by:
+#' 1. Identifying pairs of factors in the specified columns that are fully confounded.
+#' 2. Resolving confounding by adjusting or removing one of the factors from the `colData` slot.
 #'
-#' @return A modified SummarizedExperiment object with confounders resolved.
+#' The resolution strategy depends on the analysis context and can be modified in the helper function 
+#' `resolve_complete_confounders_of_non_interest_pair_SE()`. By default, the function removes one of the confounded factors.
+#'
+#' @return 
+#' A `SummarizedExperiment` object with resolved confounders. The object retains its structure, including assays and metadata, 
+#' but the column data (`colData`) is updated to reflect the resolved factors.
 #'
 #' @examples
-#' # Not run:
-#' # se is a SummarizedExperiment object
-#' # resolve_complete_confounders_of_non_interest(se, factor1, factor2, factor3)
+#' # Load necessary libraries
+#' library(SummarizedExperiment)
+#' library(dplyr)
+#'
+#' # Sample annotations
+#' sample_annotations <- data.frame(
+#'   sample_id = paste0("Sample", seq(1, 9)),
+#'   factor_of_interest = c(rep("treated", 4), rep("untreated", 5)),
+#'   A = c("a1", "a2", "a1", "a2", "a1", "a2", "a1", "a2", "a3"),
+#'   B = c("b1", "b1", "b2", "b1", "b1", "b1", "b2", "b1", "b3"),
+#'   C = c("c1", "c1", "c1", "c1", "c1", "c1", "c1", "c1", "c3"),
+#'   stringsAsFactors = FALSE
+#' )
+#'
+#' # Simulated assay data
+#' assay_data <- matrix(rnorm(100 * 9), nrow = 100, ncol = 9)
+#'
+#' # Row data (e.g., gene annotations)
+#' row_data <- data.frame(gene_id = paste0("Gene", seq_len(100)))
+#'
+#' # Create SummarizedExperiment object
+#' se <- SummarizedExperiment(
+#'   assays = list(counts = assay_data),
+#'   rowData = row_data,
+#'   colData = DataFrame(sample_annotations)
+#' )
+#'
+#' # Apply the function to resolve confounders
+#' se_resolved <- resolve_complete_confounders_of_non_interest(se, A, B, C)
+#'
+#' # View the updated column data
+#' colData(se_resolved)
+#'
+#' @seealso 
+#' \code{\link[SummarizedExperiment]{SummarizedExperiment}} for creating and handling `SummarizedExperiment` objects.
+#'
+#' @importFrom dplyr select
+#' @importFrom rlang set_names
+#' @importFrom tibble as_tibble
 #' @export
 setGeneric("resolve_complete_confounders_of_non_interest", function(se, ...) {
   standardGeneric("resolve_complete_confounders_of_non_interest")

R/methods_SE.R

@@ -2816,33 +2816,17 @@ setMethod("describe_transcript", "RangedSummarizedExperiment", .describe_transcr
 #' @importFrom dplyr select
 #' @importFrom rlang set_names
 #' @importFrom tibble as_tibble
+#' @importFrom SummarizedExperiment as.data.frame
 .resolve_complete_confounders_of_non_interest <- function(se, ...){
 
-  combination_of_factors_of_NON_interest =
-    # Factors
-    se[1,1, drop=FALSE] |>
-    colData() |> 
-    as_tibble(rownames = ".sample") |> 
-    select(...) |>
-    suppressWarnings() |>
-    colnames() |>
-
-    # Combinations
-    combn(2) |>
-    t() |>
-    as_tibble() |>
-    set_names(c("factor_1", "factor_2"))
-
-  for(i in combination_of_factors_of_NON_interest |> nrow() |> seq_len()){
-    se =
-      se |>
-      resolve_complete_confounders_of_non_interest_pair_SE(
-        !!as.symbol(combination_of_factors_of_NON_interest[i,]$factor_1),
-        !!as.symbol(combination_of_factors_of_NON_interest[i,]$factor_2)
-      )
-  }
-
+  colData(se) = 
+    colData(se) |> 
+    as.data.frame() |> 
+    .resolve_complete_confounders_of_non_interest_df(...) |> 
+    DataFrame()
+  
   se
+    
 }
 
 #' resolve_complete_confounders_of_non_interest