Minor docfixes, mostly related to the absence of first.labels.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: SingleR
 Title: Reference-Based Single-Cell RNA-Seq Annotation
-Version: 2.5.1
-Date: 2024-01-05
+Version: 2.5.2
+Date: 2024-02-13
 Authors@R: c(person("Dvir", "Aran", email="[email protected]", role=c("aut", "cph")),
     person("Aaron", "Lun", email="[email protected]", role=c("ctb", "cre")),
     person("Daniel", "Bunis", role="ctb"),
@@ -57,6 +57,6 @@ biocViews:
 SystemRequirements: C++17
 VignetteBuilder: knitr
 Encoding: UTF-8
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 URL: https://github.com/LTLA/SingleR
 BugReports: https://support.bioconductor.org/

R/combineCommonResults.R

@@ -13,7 +13,7 @@
 #' \item \code{references}, an integer vector specifying the reference from which the combined label was derived.
 #' \item \code{orig.results}, a DataFrame containing \code{results}.
 #' }
-#' It may also contain \code{first.labels} and \code{pruned.labels} if these were also present in \code{results}.
+#' It may also contain \code{pruned.labels} if these were also present in \code{results}.
 #'
 #' The \code{\link{metadata}} contains \code{common.genes},
 #' a character vector of the common genes that were used across all references in \code{results};

R/pruneScores.R

@@ -19,7 +19,7 @@
 #' resulting in assignment of an incorrect label to that cell.
 #' The \code{pruneScores} function aims to mitigate this effect by removing poor-quality assignments with \dQuote{low} scores.
 #'
-#' We compute a \dQuote{delta} value for each cell, defined as the difference between the score for the assigned label and the and median score across all labels.
+#' We compute a \dQuote{delta} value for each cell, defined as the difference between the score for the assigned label and the median score across all labels.
 #' If the delta is small, this indicates that the cell matches all labels with the same confidence such that the assigned label is not particularly meaningful.
 #' The aim is to discard low delta values caused by (i) ambiguous assignments with closely related reference labels and (ii) incorrect assignments that match poorly to all reference labels.
 #' 

vignettes/SingleR.Rmd

@@ -74,7 +74,7 @@ pred.hesc <- SingleR(test = hESCs, ref = hpca.se, assay.type.test=1,
 ```
 
 Each row of the output `DataFrame` contains prediction results for a single cell.
-Labels are shown before fine-tuning (`first.labels`), after fine-tuning (`labels`) and after pruning (`pruned.labels`), along with the associated scores.
+Labels are shown before (`labels`) and after pruning (`pruned.labels`), along with the associated scores.
 
 ```{r}
 pred.hesc