update sample ordering and filtering nans and infs

NAMESPACE

@@ -56,6 +56,7 @@ export(tomic_sort_status)
 export(tomic_to)
 export(tomic_to_matrix)
 export(triple_to_tidy)
+export(update_sample_factors)
 export(update_tidy_omic)
 export(update_tomic)
 import(ggplot2)

R/dim_reduction.R

@@ -133,6 +133,8 @@ add_pcs <- function(
 #'     then drop features}
 #'   \item{impute}{Impute missing values}
 #' }
+#' @param missing_value_types Types of values to treat as missing. One or more
+#'   of "NA", "NaN" and "Inf"
 #' @inheritParams create_tidy_omic
 #'
 #' @returns A \code{tomic} object where missing values have been accounted
@@ -143,18 +145,24 @@ add_pcs <- function(
 #'
 #' @export
 remove_missing_values <- function(
-    tomic,
-    value_var = NULL,
-    missing_val_method = "drop_samples",
-    verbose = TRUE
-    ) {
+  tomic,
+  value_var = NULL,
+  missing_val_method = "drop_samples",
+  missing_value_types = c("NA", "NaN", "Inf"),
+  verbose = FALSE
+  ) {
+
   checkmate::assertClass(tomic, "tomic")
   checkmate::assertChoice(
     missing_val_method,
     c("drop_features", "drop_samples")
   )
+  checkmate::assertCharacter(missing_value_types, min.len = 1)
   checkmate::assertLogical(verbose, len = 1)
 
+  VALID_MISSING_VALUE_TYPES <- c("NA", "NaN", "Inf")
+  purrr::walk(missing_value_types, checkmate::assertChoice, VALID_MISSING_VALUE_TYPES)
+
   triple_omic <- tomic_to(tomic, "triple_omic")
 
   design <- tomic$design
@@ -167,7 +175,8 @@ remove_missing_values <- function(
   # find missing values of value_var
   found_missing_values <- find_triple_omic_missing_values(
     triple_omic,
-    value_var
+    value_var,
+    missing_value_types
   )
   observed_measurements <- found_missing_values$observed_measurements
   missing_values <- found_missing_values$missing_values
@@ -248,11 +257,11 @@ remove_missing_values <- function(
 #'
 #' @export
 impute_missing_values <- function(
-    tomic,
-    impute_var_name = "imputed",
-    value_var = NULL,
-    ...
-    ){
+  tomic,
+  impute_var_name = "imputed",
+  value_var = NULL,
+  ...
+  ){
 
   if (!("impute" %in% rownames(utils::installed.packages()))) {
     stop("Install \"impute\" using remotes::install_bioc(\"impute\") to use this function")
@@ -296,7 +305,7 @@ impute_missing_values <- function(
   omic_matrix <- triple_omic$measurements %>%
     reshape2::acast(formula = cast_formula, value.var = value_var)
 
-  # imput data
+  # impute
   imputed_measurements <- impute::impute.knn(
     omic_matrix,
     ...
@@ -397,15 +406,39 @@ value_var_handler <- function(value_var = NULL, design) {
   return(value_var)
 }
 
-find_triple_omic_missing_values <- function(triple_omic, value_var) {
+find_triple_omic_missing_values <- function(
+  triple_omic,
+  value_var,
+  missing_values_types = c("NA", "NaN", "Inf")
+  ) {
+
   all_expected_obs <- tidyr::expand_grid(
     triple_omic$features[triple_omic$design$feature_pk],
     triple_omic$samples[triple_omic$design$sample_pk]
   )
 
-  observed_measurements <- triple_omic$measurements %>%
-    # drop missing values
-    dplyr::filter_at(value_var, function(x) !is.na(x))
+  observed_measurements <- triple_omic$measurements
+
+  if ("NA" %in% missing_values_types) {
+    observed_measurements <- observed_measurements %>%
+      dplyr::filter(
+        !is.na(!!rlang::sym(value_var))
+      )
+  }
+
+  if ("NaN" %in% missing_values_types) {
+    observed_measurements <- observed_measurements %>%
+      dplyr::filter(
+        !is.nan(!!rlang::sym(value_var))
+      )
+  }
+
+  if ("Inf" %in% missing_values_types) {
+    observed_measurements <- observed_measurements %>%
+      dplyr::filter(
+        is.finite(!!rlang::sym(value_var))
+      )
+  }
 
   missing_values <- all_expected_obs %>%
     dplyr::anti_join(

R/mutates.R

@@ -495,3 +495,95 @@ tomic_sort_status <- function(tomic) {
 
   return(status)
 }
+
+#' Update Sample Factors
+#'
+#' Update sample metadata to order categorical variables based on a
+#' specified factor order.
+#'
+#' @inheritParams tomic_to
+#' @param factor_levels a character vector specifying the ordering of factor levels.
+#'
+#' @returns a tomic object with updated sample metadata
+#'
+#' @examples
+#' update_sample_factors(
+#'   brauer_2008_tidy, list(nutrient = c("G", "N", "P", "S", "L", "U"))
+#' )
+#'
+#' @export
+update_sample_factors <- function (tomic, factor_levels) {
+
+  checkmate::assertClass(tomic, "tomic")
+  checkmate::assertNamed(factor_levels)
+  checkmate::assertList(factor_levels)
+
+  samples <- romic::get_tomic_table(tomic, "samples")
+  purrr::walk(names(factor_levels), checkmate::assertChoice, colnames(samples))
+
+  # update all categorical variables with specified factor orders
+  for (fct in names(factor_levels)) {
+    samples[[fct]] <- set_factor_levels(samples[[fct]], factor_levels[[fct]], fct)
+  }
+
+  out <- romic::update_tomic(tomic, samples)
+
+  return(out)
+}
+
+set_factor_levels <- function(samples_vec, fct_levels, fct_label = "?") {
+
+  # validate factor orders
+  if (!("character" %in% class(fct_levels))) {
+    cli::cli_abort(
+      "The factor levels for {fct_label} were {.val {class(fct_levels)}}.
+      This should be a character vector."
+    )
+  }
+
+  duplicated_levels <- unique(fct_levels[duplicated(fct_levels)])
+  if (length(duplicated_levels) > 0) {
+    cli::cli_abort(
+      "{length(duplicated_levels)} factor levels {?was/were} duplicated in the `factor_levels` specification for
+    {.val {fct_label}}: {duplicated_levels}"
+    )
+  }
+
+  if ("character" %in% class(samples_vec)) {
+
+    extra_sample_vars <- setdiff(samples_vec, fct_levels)
+    if (length(extra_sample_vars)) {
+      cli::cli_alert_warning(
+        "{.val {extra_sample_vars}} {?was/were} present in the sample metadata's {.field {fct_label}} field but did not have a corresponding factor level in the {.arg factor_levels} list. They will be added to the end of the specified factor levels"
+      )
+
+      fct_levels <- c(fct_levels, extra_sample_vars)
+    }
+
+    missing_sample_vars <- setdiff(fct_levels, samples_vec)
+    if (length(missing_sample_vars)) {
+      cli::cli_alert_warning(
+        "{.val {missing_sample_vars}} {?was/were} present in {.arg factor_levels} for {.field {fct_label}} but did not have a corresponding entry in the sample metadata."
+      )
+    }
+
+    if (any(is.na(samples_vec))) {
+      cli::cli_alert_warning(
+        "The {.field {fct_label}} field in the sample metadata contains {sum(is.na(samples_vec))} NA values. These entries will be replaced with an {.val unspecified} level.")
+
+      samples_vec[is.na(samples_vec)] <- "unspecified"
+      fct_levels <- c(fct_levels, "unspecified")
+    }
+
+    samples_fct_vec <- factor(
+      samples_vec,
+      levels = fct_levels
+    )
+  } else {
+    cli::cli_abort(
+      "The factor levels for fct were {.val {class(fct)}} and cannot be converted
+    to factors using the specified factor orders.")
+  }
+
+  return(samples_fct_vec)
+}