Merge pull request #1049 from jiajic/suite_dev

!feat: remove re-export of combineSpatialCellFeatureInfo()

NAMESPACE

@@ -97,7 +97,6 @@ export(combineICG)
 export(combineInteractionChangedFeats)
 export(combineInteractionChangedGenes)
 export(combineMetadata)
-export(combineSpatialCellFeatureInfo)
 export(combineSpatialCellMetadataInfo)
 export(combineToMultiPolygon)
 export(compareCellAbundance)
@@ -575,7 +574,6 @@ importFrom(GiottoClass,combineCellData)
 importFrom(GiottoClass,combineFeatureData)
 importFrom(GiottoClass,combineFeatureOverlapData)
 importFrom(GiottoClass,combineMetadata)
-importFrom(GiottoClass,combineSpatialCellFeatureInfo)
 importFrom(GiottoClass,combineSpatialCellMetadataInfo)
 importFrom(GiottoClass,combineToMultiPolygon)
 importFrom(GiottoClass,convertGiottoLargeImageToMG)

R/normalize.R

@@ -25,21 +25,21 @@
 #' A. The standard method follows the standard protocol which can be adjusted
 #' using the provided parameters and follows the following order: \cr
 #' \itemize{
-#'   \item{1. Data normalization for total library size and scaling by a custom 
+#'   \item{1. Data normalization for total library size and scaling by a custom
 #'   scale-factor.}
 #'   \item{2. Log transformation of data.}
 #'   \item{3. Z-scoring of data by genes and/or cells.}
 #' }
-#' B. The normalization method as provided by the osmFISH paper is also 
+#' B. The normalization method as provided by the osmFISH paper is also
 #' implemented: \cr
 #' \itemize{
-#'   \item{1. First normalize genes, for each gene divide the counts by the 
+#'   \item{1. First normalize genes, for each gene divide the counts by the
 #'   total gene count and multiply by the total number of genes.}
-#'   \item{2. Next normalize cells, for each cell divide the normalized gene 
-#'   counts by the total counts per cell and multiply by the total number of 
+#'   \item{2. Next normalize cells, for each cell divide the normalized gene
+#'   counts by the total counts per cell and multiply by the total number of
 #'   cells.}
 #' }
-#' C. The normalization method as provided by Lause/Kobak et al is also 
+#' C. The normalization method as provided by Lause/Kobak et al is also
 #' implemented: \cr
 #' \itemize{
 #'   \item{1. First calculate expected values based on Pearson correlations.}
@@ -48,9 +48,9 @@
 #' D. Quantile normalization across features
 #' \itemize{
 #'   \item{1. Rank feature expression}
-#'   \item{2. Define a common distribution by sorting expression values per 
+#'   \item{2. Define a common distribution by sorting expression values per
 #'   feature then finding the mean across all features per index}
-#'   \item{3. Apply common distribution to expression information by using 
+#'   \item{3. Apply common distribution to expression information by using
 #'   the ranks from step 1 as indices}
 #' }
 #' By default the latter two results will be saved in the Giotto slot for
@@ -218,7 +218,7 @@ normalizeGiotto <- function(
     } else if (methods::is(mymatrix, "Matrix")) {
         mymatrix@x <- log(mymatrix@x + offset) / log(base)
     } else if (methods::is(mymatrix, "dbMatrix")) {
-        mymatrix[] <- dplyr::mutate(mymatrix[], x = x + offset) 
+        mymatrix[] <- dplyr::mutate(mymatrix[], x = x + offset)
         # workaround for lack of @x slot
         mymatrix <- log(mymatrix) / log(base)
     } else {

R/suite_reexports.R

@@ -167,8 +167,6 @@ GiottoClass::combineFeatureOverlapData
 #' @export
 GiottoClass::combineMetadata
 #' @export
-GiottoClass::combineSpatialCellFeatureInfo
-#' @export
 GiottoClass::combineSpatialCellMetadataInfo
 #' @export
 GiottoClass::combineToMultiPolygon