Merge pull request #6 from ccb-hms/main

change param names and add ctb

DESCRIPTION

@@ -2,9 +2,15 @@ Package: scConform
 Type: Package
 Title: Conformal Inference For Cell Type Annotation
 Version: 0.99.1
-Authors@R: person("Corbetta", "Daniela", 
+Authors@R: c(
+                  person("Daniela", "Corbetta", 
                   email = "[email protected]", 
-                  role = c("aut", "cre"))
+                  role = c("aut", "cre")),
+                  person("Tram", "Nguyen", role = "ctb"),
+                  person("Anthony", "Christidis", role = "ctb"), 
+                  person("Nitesh", "Turaga", role = "ctb"),
+                  person("Ludwig", "Geistlinger", role = "ctb")
+           )
 Description: Builds prediction interval for cell type annotation using conformal 
     inference and conformal risk control.
     It provides two main methods. The first one gives prediction intervals with 

NAMESPACE

@@ -1,8 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
+export(getCommonAncestor)
 export(getPredictionSets)
 export(plotResult)
-export(returnCommonAncestor)
 import(Rgraphviz)
 importFrom(BiocParallel,SerialParam)
 importFrom(BiocParallel,bplapply)

R/returnCommonAncestor.R → R/getCommonAncestor.R

@@ -16,12 +16,12 @@
 #' )
 #' # Let's consider this prediction set
 #' pred_set <- c("golden", "labrador", "cocker")
-#' com_anc <- returnCommonAncestor(pred_set, onto)
+#' com_anc <- getCommonAncestor(pred_set, onto)
 #' @importFrom igraph V distances degree
 #' @export
 
 # Function to return the common ancestor instead of the single leaf nodes
-returnCommonAncestor <- function(pred_set, onto) {
+getCommonAncestor <- function(pred_set, onto) {
     com_anc <- Reduce(intersect, lapply(pred_set, function(node) {
         .ancestors(node, onto)
     }))

R/getPredictionSets.R

@@ -25,7 +25,7 @@
 #' @param follow_ontology If \code{TRUE}, then the function returns hierarchical
 #' prediction sets that follow the cell ontology structure. If \code{FALSE}, it
 #' returns classical conformal prediction sets. See details.
-#' @param resample_cal Should the calibration dataset be resampled according to
+#' @param resample Should the calibration dataset be resampled according to
 #' the estimated relative frequencies of cell types in the query data?
 #' @param labels labels of different considered cell types. Necessary if
 #' \code{onto=NULL}, otherwise they are set equal to the leaf nodes of the
@@ -38,7 +38,7 @@
 #' @param pr_name name of the colData variable in the returned
 #' SingleCellExperiment object that will contain the prediction
 #' sets. The default name is \code{pred.set}.
-#' @param simplify_pred if \code{TRUE}, the output will be the common ancestor
+#' @param simplify if \code{TRUE}, the output will be the common ancestor
 #' of the labels inserted into the prediction set. If \code{FALSE} (default),
 #' the output will be the set of the leaf labels.
 #' @param BPPARAM BiocParallel instance for parallel computing. Default is
@@ -136,7 +136,7 @@
 #'     onto = NULL,
 #'     alpha = 0.1,
 #'     follow_ontology = FALSE,
-#'     resample_cal = FALSE,
+#'     resample = FALSE,
 #'     labels = cell_types,
 #'     return_sc = FALSE
 #' )
@@ -151,11 +151,11 @@ getPredictionSets <- function(
         x_query, x_cal, y_cal, onto = NULL, alpha = 0.1,
         lambdas = seq(0.001, 0.999, length.out = 100),
         follow_ontology = TRUE,
-        resample_cal = FALSE,
+        resample = FALSE,
         labels = NULL,
         return_sc = NULL,
         pr_name = "pred.set",
-        simplify_pred = FALSE,
+        simplify = FALSE,
         BPPARAM = SerialParam()) {
     ## Sanity checks
 
@@ -187,8 +187,8 @@ getPredictionSets <- function(
         }
     }
 
-    if (!follow_ontology & simplify_pred) {
-        stop("If follow_ontology=FALSE, please set simplify_pred=FALSE")
+    if (!follow_ontology & simplify) {
+        stop("If follow_ontology=FALSE, please set simplify=FALSE")
     }
 
     ## If labels parameter is NULL, retrieve labels from the ontology
@@ -210,7 +210,7 @@ getPredictionSets <- function(
         p_cal <- x_cal
     }
 
-    if (!resample_cal) {
+    if (!resample) {
         if (follow_ontology) {
             pred_sets <- .getHierarchicalPredSets(
                 p_cal = p_cal, p_test = p_query,
@@ -227,7 +227,7 @@ getPredictionSets <- function(
         }
     }
 
-    if (resample_cal) {
+    if (resample) {
         data <- .resampleTwo(
             p_cal = p_cal, p_test = p_query, y_cal = y_cal,
             labels = labels
@@ -272,11 +272,11 @@ getPredictionSets <- function(
     }
 
     ## Transform prediction with leaf nodes to prediction sets with the
-    ## common ancestor if simplify_pred=TRUE
-    if (simplify_pred) {
+    ## common ancestor if simplify=TRUE
+    if (simplify) {
         pred_sets1 <- vapply(
             pred_sets,
-            function(x) returnCommonAncestor(x, onto),
+            function(x) getCommonAncestor(x, onto),
             character(1)
         )
         ## Check for ramification. If there is a ramification in the ontology

vignettes/scConform.Rmd

@@ -452,7 +452,7 @@ spe_query <- getPredictionSets(
     follow_ontology = TRUE,
     pr_name = "pred_set_hier",
     BPPARAM = MulticoreParam(workers = 2),
-    simplify_pred = FALSE
+    simplify = FALSE
 )
 
 # See the first six prediction sets
@@ -473,14 +473,14 @@ mean(cvg1)
 
 As an alternative, we can choose to return only the common ancestor, instead 
 of the entire set of predicted labels. This can be done directly in the 
-function by setting `simplify_pred=TRUE`.
+function by setting `simplify=TRUE`.
 Alternatively, we can convert the obtained prediction sets invoking
-the function `returnCommonAncestor`.
+the function `getCommonAncestor`.
 
 ```{r common-ancestor}
 spe_query$pred_set_hier_simp <- vapply(
     spe_query$pred_set_hier,
-    function(x) returnCommonAncestor(x, opi2),
+    function(x) getCommonAncestor(x, opi2),
     character(1)
 )
 head(spe_query$pred_set_hier_simp)