From 8078963eaca44d1ea4428273fc455788496deace Mon Sep 17 00:00:00 2001
From: George Chen <72078254+jiajic@users.noreply.github.com>
Date: Wed, 22 May 2024 17:38:26 -0400
Subject: [PATCH] chore: document `detectSpatialCorFeats()` and
 `detectSpatialCorFeatsMatrix()` together

---
 NAMESPACE                          |   1 -
 R/spatial_genes.R                  | 268 ++++++++++++++---------------
 man/detectSpatialCorFeats.Rd       |  71 ++++++--
 man/detectSpatialCorFeatsMatrix.Rd |  73 --------
 man/detectSpatialCorGenes.Rd       |  74 --------
 5 files changed, 183 insertions(+), 304 deletions(-)
 delete mode 100644 man/detectSpatialCorFeatsMatrix.Rd
 delete mode 100644 man/detectSpatialCorGenes.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 02aa123e7..bbe6fefac 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -151,7 +151,6 @@ export(crossSectionPlot)
 export(crossSectionPlot3D)
 export(detectSpatialCorFeats)
 export(detectSpatialCorFeatsMatrix)
-export(detectSpatialCorGenes)
 export(detectSpatialPatterns)
 export(dimCellPlot)
 export(dimCellPlot2D)
diff --git a/R/spatial_genes.R b/R/spatial_genes.R
index 7b97c9aa7..bcfadc3c7 100644
--- a/R/spatial_genes.R
+++ b/R/spatial_genes.R
@@ -3228,16 +3228,25 @@ do_spatial_grid_averaging <- function(expression_matrix,
 
 
 
-#' @title detectSpatialCorFeatsMatrix
-#' @name detectSpatialCorFeatsMatrix
-#' @description Detect genes that are spatially correlated
+#' @title Detect spatially correlated features
+#' @name detectSpatialCorFeats
+#' @description Detect features that are spatially correlated. Functions for
+#' starting from either a gobject (`detectSpatialCorFeats()`) or individual
+#' pieces of data (`detectSpatialCorFeatsMatrix()`) are provided.
+#' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
+#' @param expression_values gene expression values to use
 #' @param expression_matrix provided expression matrix
-#' @param method method to use for spatial averaging
+#' @param spat_loc_name name for spatial locations
+#' @param spatial_locs provided spatial locations
+#' @param spatial_network_name name of spatial network to use
 #' @param spatial_network provided spatial network
+#' @param spatial_grid_name name of spatial grid to use
 #' @param spatial_grid provided spatial grid
-#' @param spatial_locs provided spatial locations
+#' @param method method to use for spatial averaging
 #' @param subset_feats subset of features to use
-#' @param network_smoothing smoothing factor beteen 0 and 1
+#' @param network_smoothing smoothing factor between 0 and 1
 #' (has automatic default, see details)
 #' @param min_cells_per_grid minimum number of cells to consider a grid
 #' @param cor_method correlation method
@@ -3257,25 +3266,54 @@ do_spatial_grid_averaging <- function(expression_matrix,
 #'   Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
 #'   contribution from its own expression.
 #'
-#' The `spatCorObject` can be further explored with `showSpatialCorGenes()`
+#' The `spatCorObject` can be further explored with `showSpatialCorFeats()`
 #' @seealso \code{\link{showSpatialCorFeats}}
 #' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' detectSpatialCorFeatsMatrix(expression_matrix = getExpression(
-#' g, output = "matrix"), method = "network",
-#' spatial_network = getSpatialNetwork(g, output = "networkDT"))
+#' # Perform with data in a gobject
+#' detectSpatialCorFeats(g, method = "network")
+#'
+#' # This analysis can also be performed with data outside of the gobject
+#' detectSpatialCorFeatsMatrix(
+#'     expression_matrix = getExpression(
+#'     g, output = "matrix"),
+#'     method = "network",
+#'     spatial_network = getSpatialNetwork(g, output = "networkDT")
+#' )
+#'
+NULL
+
+
+
+#' @rdname detectSpatialCorFeats
 #' @export
-detectSpatialCorFeatsMatrix <- function(expression_matrix,
-    method = c("grid", "network"),
-    spatial_network,
-    spatial_grid,
-    spatial_locs,
-    subset_feats = NULL,
-    network_smoothing = NULL,
-    min_cells_per_grid = 4,
-    cor_method = c("pearson", "kendall", "spearman")) {
+detectSpatialCorFeats <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spat_loc_name = "raw",
+        method = c("grid", "network"),
+        expression_values = c("normalized", "scaled", "custom"),
+        subset_feats = NULL,
+        spatial_network_name = "Delaunay_network",
+        network_smoothing = NULL,
+        spatial_grid_name = "spatial_grid",
+        min_cells_per_grid = 4,
+        cor_method = c("pearson", "kendall", "spearman")
+) {
+    # set default spat_unit and feat_type
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject,
+        spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type
+    )
+
     ## correlation method to be used
     cor_method <- match.arg(
         cor_method, choices = c("pearson", "kendall", "spearman"))
@@ -3283,10 +3321,46 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
     ## method to be used
     method <- match.arg(method, choices = c("grid", "network"))
 
+    # get expression matrix
+    values <- match.arg(
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values)))
+    expr_values <- getExpression(
+        gobject = gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
+        values = values,
+        output = "matrix"
+    )
+
+    if (!is.null(subset_feats)) {
+        expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
+    }
+
+
+
+    # get spatial locations
+    spatial_locs <- getSpatialLocations(
+        gobject,
+        spat_unit = spat_unit,
+        name = spat_loc_name,
+        output = "data.table",
+        copy_obj = TRUE
+    )
+
     ## spatial averaging or smoothing
     if (method == "grid") {
+        # get spatial grid
+        spatial_grid <- getSpatialGrid(
+            gobject = gobject,
+            spat_unit = spat_unit,
+            feat_type = feat_type,
+            name = spatial_grid_name,
+            return_grid_Obj = FALSE
+        )
+
         loc_av_expr_matrix <- do_spatial_grid_averaging(
-            expression_matrix = as.matrix(expression_matrix),
+            expression_matrix = as.matrix(expr_values),
             spatial_grid = spatial_grid,
             spatial_locs = spatial_locs,
             subset_feats = subset_feats,
@@ -3296,8 +3370,8 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
         # data.table variables
         feat_ID <- variable <- NULL
 
-        cor_spat_matrix <- cor_flex(t_flex(
-            as.matrix(loc_av_expr_matrix)), method = cor_method)
+        cor_spat_matrix <- cor_flex(t_flex(as.matrix(
+            loc_av_expr_matrix)), method = cor_method)
         cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
         cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
         cor_spat_DT <- data.table::melt.data.table(
@@ -3307,8 +3381,16 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
     }
 
     if (method == "network") {
+        # get spatial network
+        spatial_network <- getSpatialNetwork(
+            gobject = gobject,
+            spat_unit = spat_unit,
+            name = spatial_network_name,
+            output = "networkDT"
+        )
+
         knn_av_expr_matrix <- do_spatial_knn_smoothing(
-            expression_matrix = as.matrix(expression_matrix),
+            expression_matrix = as.matrix(expr_values),
             spatial_network = spatial_network,
             subset_feats = subset_feats,
             b = network_smoothing
@@ -3316,6 +3398,7 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
 
 
 
+
         cor_spat_matrix <- cor_flex(t_flex(as.matrix(
             knn_av_expr_matrix)), method = cor_method)
         cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
@@ -3333,13 +3416,6 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
 
     ## 2. perform expression correlation at single-cell level without
     ## spatial information
-
-    # matrix
-    expr_values <- expression_matrix
-    if (!is.null(subset_feats)) {
-        expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
-    }
-
     cor_matrix <- cor_flex(t_flex(expr_values), method = cor_method)
     cor_matrixDT <- data.table::as.data.table(cor_matrix)
     cor_matrixDT[, feat_ID := rownames(cor_matrix)]
@@ -3357,9 +3433,9 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
     doubleDT[, cordiff := spat_cor - expr_cor]
 
     # difference in rank scores
-    doubleDT[, spatrank := data.table::frank(
+    doubleDT[, spatrank := frank(
         -spat_cor, ties.method = "first"), by = feat_ID]
-    doubleDT[, exprrank := data.table::frank(
+    doubleDT[, exprrank := frank(
         -expr_cor, ties.method = "first"), by = feat_ID]
     doubleDT[, rankdiff := spatrank - exprrank]
 
@@ -3373,73 +3449,23 @@ detectSpatialCorFeatsMatrix <- function(expression_matrix,
         cor_clusters = list()
     )
 
-    class(spatCorObject) <- append(class(spatCorObject), "spatCorObject")
+    class(spatCorObject) <- append("spatCorObject", class(spatCorObject))
 
     return(spatCorObject)
 }
 
 
-
-#' @title detectSpatialCorFeats
-#' @name detectSpatialCorFeats
-#' @description Detect features that are spatially correlated
-#' @param gobject giotto object
-#' @param spat_unit spatial unit
-#' @param feat_type feature type
-#' @param spat_loc_name name for spatial locations
-#' @param method method to use for spatial averaging
-#' @param expression_values gene expression values to use
-#' @param subset_feats subset of feats to use
-#' @param spatial_network_name name of spatial network to use
-#' @param network_smoothing  smoothing factor beteen 0 and 1
-#' (default: automatic)
-#' @param spatial_grid_name name of spatial grid to use
-#' @param min_cells_per_grid minimum number of cells to consider a grid
-#' @param cor_method correlation method
-#' @returns returns a spatial correlation object: "spatCorObject"
-#' @details
-#' For method = network, it expects a fully connected spatial network. You
-#' can make sure to create a
-#' fully connected network by setting minimal_k > 0 in the
-#'  \code{\link{createSpatialNetwork}} function.
-#' \itemize{
-#'  \item{1. grid-averaging: }{average gene expression values within a predefined spatial grid}
-#'  \item{2. network-averaging: }{smoothens the gene expression matrix by averaging the expression within one cell
-#'  by using the neighbours within the predefined spatial network. b is a smoothening factor
-#'  that defaults to 1 - 1/k, where k is the median number of  k-neighbors in the
-#'  selected spatial network. Setting b = 0 means no smoothing and b = 1 means no contribution
-#'  from its own expression.}
-#' }
-#' The spatCorObject can be further explored with showSpatialCorFeats()
-#' @seealso \code{\link{showSpatialCorFeats}}
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#'
-#' detectSpatialCorFeats(g, method = "network")
+#' @rdname detectSpatialCorFeats
 #' @export
-detectSpatialCorFeats <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    spat_loc_name = "raw",
+detectSpatialCorFeatsMatrix <- function(expression_matrix,
     method = c("grid", "network"),
-    expression_values = c("normalized", "scaled", "custom"),
+    spatial_network,
+    spatial_grid,
+    spatial_locs,
     subset_feats = NULL,
-    spatial_network_name = "Delaunay_network",
     network_smoothing = NULL,
-    spatial_grid_name = "spatial_grid",
     min_cells_per_grid = 4,
     cor_method = c("pearson", "kendall", "spearman")) {
-    # set default spat_unit and feat_type
-    spat_unit <- set_default_spat_unit(
-        gobject = gobject,
-        spat_unit = spat_unit
-    )
-    feat_type <- set_default_feat_type(
-        gobject = gobject,
-        spat_unit = spat_unit,
-        feat_type = feat_type
-    )
-
     ## correlation method to be used
     cor_method <- match.arg(
         cor_method, choices = c("pearson", "kendall", "spearman"))
@@ -3447,45 +3473,10 @@ detectSpatialCorFeats <- function(gobject,
     ## method to be used
     method <- match.arg(method, choices = c("grid", "network"))
 
-    # get expression matrix
-    values <- match.arg(
-        expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
-    expr_values <- getExpression(
-        gobject = gobject,
-        spat_unit = spat_unit,
-        feat_type = feat_type,
-        values = values,
-        output = "matrix"
-    )
-
-    if (!is.null(subset_feats)) {
-        expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
-    }
-
-
-
-    # get spatial locations
-    spatial_locs <- getSpatialLocations(gobject,
-        spat_unit = spat_unit,
-        name = spat_loc_name,
-        output = "data.table",
-        copy_obj = TRUE
-    )
-
     ## spatial averaging or smoothing
     if (method == "grid") {
-        # get spatial grid
-        spatial_grid <- getSpatialGrid(
-            gobject = gobject,
-            spat_unit = spat_unit,
-            feat_type = feat_type,
-            name = spatial_grid_name,
-            return_grid_Obj = FALSE
-        )
-
         loc_av_expr_matrix <- do_spatial_grid_averaging(
-            expression_matrix = as.matrix(expr_values),
+            expression_matrix = as.matrix(expression_matrix),
             spatial_grid = spatial_grid,
             spatial_locs = spatial_locs,
             subset_feats = subset_feats,
@@ -3495,8 +3486,8 @@ detectSpatialCorFeats <- function(gobject,
         # data.table variables
         feat_ID <- variable <- NULL
 
-        cor_spat_matrix <- cor_flex(t_flex(as.matrix(
-            loc_av_expr_matrix)), method = cor_method)
+        cor_spat_matrix <- cor_flex(t_flex(
+            as.matrix(loc_av_expr_matrix)), method = cor_method)
         cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
         cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
         cor_spat_DT <- data.table::melt.data.table(
@@ -3506,16 +3497,8 @@ detectSpatialCorFeats <- function(gobject,
     }
 
     if (method == "network") {
-        # get spatial network
-        spatial_network <- getSpatialNetwork(
-            gobject = gobject,
-            spat_unit = spat_unit,
-            name = spatial_network_name,
-            output = "networkDT"
-        )
-
         knn_av_expr_matrix <- do_spatial_knn_smoothing(
-            expression_matrix = as.matrix(expr_values),
+            expression_matrix = as.matrix(expression_matrix),
             spatial_network = spatial_network,
             subset_feats = subset_feats,
             b = network_smoothing
@@ -3523,7 +3506,6 @@ detectSpatialCorFeats <- function(gobject,
 
 
 
-
         cor_spat_matrix <- cor_flex(t_flex(as.matrix(
             knn_av_expr_matrix)), method = cor_method)
         cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
@@ -3541,6 +3523,13 @@ detectSpatialCorFeats <- function(gobject,
 
     ## 2. perform expression correlation at single-cell level without
     ## spatial information
+
+    # matrix
+    expr_values <- expression_matrix
+    if (!is.null(subset_feats)) {
+        expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
+    }
+
     cor_matrix <- cor_flex(t_flex(expr_values), method = cor_method)
     cor_matrixDT <- data.table::as.data.table(cor_matrix)
     cor_matrixDT[, feat_ID := rownames(cor_matrix)]
@@ -3558,9 +3547,9 @@ detectSpatialCorFeats <- function(gobject,
     doubleDT[, cordiff := spat_cor - expr_cor]
 
     # difference in rank scores
-    doubleDT[, spatrank := frank(
+    doubleDT[, spatrank := data.table::frank(
         -spat_cor, ties.method = "first"), by = feat_ID]
-    doubleDT[, exprrank := frank(
+    doubleDT[, exprrank := data.table::frank(
         -expr_cor, ties.method = "first"), by = feat_ID]
     doubleDT[, rankdiff := spatrank - exprrank]
 
@@ -3574,7 +3563,7 @@ detectSpatialCorFeats <- function(gobject,
         cor_clusters = list()
     )
 
-    class(spatCorObject) <- append("spatCorObject", class(spatCorObject))
+    class(spatCorObject) <- append(class(spatCorObject), "spatCorObject")
 
     return(spatCorObject)
 }
@@ -3583,6 +3572,7 @@ detectSpatialCorFeats <- function(gobject,
 
 
 
+
 #' @title showSpatialCorFeats
 #' @name showSpatialCorFeats
 #' @description Shows and filters spatially correlated features
diff --git a/man/detectSpatialCorFeats.Rd b/man/detectSpatialCorFeats.Rd
index 4cba6edaf..834197249 100644
--- a/man/detectSpatialCorFeats.Rd
+++ b/man/detectSpatialCorFeats.Rd
@@ -2,7 +2,8 @@
 % Please edit documentation in R/spatial_genes.R
 \name{detectSpatialCorFeats}
 \alias{detectSpatialCorFeats}
-\title{detectSpatialCorFeats}
+\alias{detectSpatialCorFeatsMatrix}
+\title{Detect spatially correlated features}
 \usage{
 detectSpatialCorFeats(
   gobject,
@@ -18,6 +19,18 @@ detectSpatialCorFeats(
   min_cells_per_grid = 4,
   cor_method = c("pearson", "kendall", "spearman")
 )
+
+detectSpatialCorFeatsMatrix(
+  expression_matrix,
+  method = c("grid", "network"),
+  spatial_network,
+  spatial_grid,
+  spatial_locs,
+  subset_feats = NULL,
+  network_smoothing = NULL,
+  min_cells_per_grid = 4,
+  cor_method = c("pearson", "kendall", "spearman")
+)
 }
 \arguments{
 \item{gobject}{giotto object}
@@ -32,44 +45,68 @@ detectSpatialCorFeats(
 
 \item{expression_values}{gene expression values to use}
 
-\item{subset_feats}{subset of feats to use}
+\item{subset_feats}{subset of features to use}
 
 \item{spatial_network_name}{name of spatial network to use}
 
-\item{network_smoothing}{smoothing factor beteen 0 and 1
-(default: automatic)}
+\item{network_smoothing}{smoothing factor between 0 and 1
+(has automatic default, see details)}
 
 \item{spatial_grid_name}{name of spatial grid to use}
 
 \item{min_cells_per_grid}{minimum number of cells to consider a grid}
 
 \item{cor_method}{correlation method}
+
+\item{expression_matrix}{provided expression matrix}
+
+\item{spatial_network}{provided spatial network}
+
+\item{spatial_grid}{provided spatial grid}
+
+\item{spatial_locs}{provided spatial locations}
 }
 \value{
-returns a spatial correlation object: "spatCorObject"
+returns a spatial correlation object: \code{spatCorObject}
 }
 \description{
-Detect features that are spatially correlated
+Detect features that are spatially correlated. Functions for
+starting from either a gobject (\code{detectSpatialCorFeats()}) or individual
+pieces of data (\code{detectSpatialCorFeatsMatrix()}) are provided.
 }
 \details{
-For method = network, it expects a fully connected spatial network. You
-can make sure to create a
+For \code{method = network}, it expects a fully connected spatial network.
+You can make sure to create a
 fully connected network by setting minimal_k > 0 in the
- \code{\link{createSpatialNetwork}} function.
-\itemize{
- \item{1. grid-averaging: }{average gene expression values within a predefined spatial grid}
- \item{2. network-averaging: }{smoothens the gene expression matrix by averaging the expression within one cell
- by using the neighbours within the predefined spatial network. b is a smoothening factor
- that defaults to 1 - 1/k, where k is the median number of  k-neighbors in the
- selected spatial network. Setting b = 0 means no smoothing and b = 1 means no contribution
- from its own expression.}
+\code{\link{createSpatialNetwork}} function.
+\enumerate{
+\item \strong{grid-averaging:} average gene expression values within a predefined
+spatial grid
+\item \strong{network-averaging:} smoothens the gene expression matrix by
+averaging the expression within one cell by using the neighbours within
+the predefined spatial network. \eqn{b} is a smoothening factor passed by
+\code{network_smoothing} param that defaults to \eqn{1 - 1/k}, where \eqn{k}
+is the median number of k-neighbors in the selected spatial network.
+Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
+contribution from its own expression.
 }
-The spatCorObject can be further explored with showSpatialCorFeats()
+
+The \code{spatCorObject} can be further explored with \code{showSpatialCorFeats()}
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
+# Perform with data in a gobject
 detectSpatialCorFeats(g, method = "network")
+
+# This analysis can also be performed with data outside of the gobject
+detectSpatialCorFeatsMatrix(
+    expression_matrix = getExpression(
+    g, output = "matrix"),
+    method = "network",
+    spatial_network = getSpatialNetwork(g, output = "networkDT")
+)
+
 }
 \seealso{
 \code{\link{showSpatialCorFeats}}
diff --git a/man/detectSpatialCorFeatsMatrix.Rd b/man/detectSpatialCorFeatsMatrix.Rd
deleted file mode 100644
index 515557f80..000000000
--- a/man/detectSpatialCorFeatsMatrix.Rd
+++ /dev/null
@@ -1,73 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_genes.R
-\name{detectSpatialCorFeatsMatrix}
-\alias{detectSpatialCorFeatsMatrix}
-\title{detectSpatialCorFeatsMatrix}
-\usage{
-detectSpatialCorFeatsMatrix(
-  expression_matrix,
-  method = c("grid", "network"),
-  spatial_network,
-  spatial_grid,
-  spatial_locs,
-  subset_feats = NULL,
-  network_smoothing = NULL,
-  min_cells_per_grid = 4,
-  cor_method = c("pearson", "kendall", "spearman")
-)
-}
-\arguments{
-\item{expression_matrix}{provided expression matrix}
-
-\item{method}{method to use for spatial averaging}
-
-\item{spatial_network}{provided spatial network}
-
-\item{spatial_grid}{provided spatial grid}
-
-\item{spatial_locs}{provided spatial locations}
-
-\item{subset_feats}{subset of features to use}
-
-\item{network_smoothing}{smoothing factor beteen 0 and 1
-(has automatic default, see details)}
-
-\item{min_cells_per_grid}{minimum number of cells to consider a grid}
-
-\item{cor_method}{correlation method}
-}
-\value{
-returns a spatial correlation object: \code{spatCorObject}
-}
-\description{
-Detect genes that are spatially correlated
-}
-\details{
-For \code{method = network}, it expects a fully connected spatial network.
-You can make sure to create a
-fully connected network by setting minimal_k > 0 in the
-\code{\link{createSpatialNetwork}} function.
-\enumerate{
-\item \strong{grid-averaging:} average gene expression values within a predefined
-spatial grid
-\item \strong{network-averaging:} smoothens the gene expression matrix by
-averaging the expression within one cell by using the neighbours within
-the predefined spatial network. \eqn{b} is a smoothening factor passed by
-\code{network_smoothing} param that defaults to \eqn{1 - 1/k}, where \eqn{k}
-is the median number of k-neighbors in the selected spatial network.
-Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
-contribution from its own expression.
-}
-
-The \code{spatCorObject} can be further explored with \code{showSpatialCorGenes()}
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-detectSpatialCorFeatsMatrix(expression_matrix = getExpression(
-g, output = "matrix"), method = "network",
-spatial_network = getSpatialNetwork(g, output = "networkDT"))
-}
-\seealso{
-\code{\link{showSpatialCorFeats}}
-}
diff --git a/man/detectSpatialCorGenes.Rd b/man/detectSpatialCorGenes.Rd
deleted file mode 100644
index 6c5a2ce6b..000000000
--- a/man/detectSpatialCorGenes.Rd
+++ /dev/null
@@ -1,74 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_genes.R
-\name{detectSpatialCorGenes}
-\alias{detectSpatialCorGenes}
-\title{detectSpatialCorGenes}
-\usage{
-detectSpatialCorGenes(
-  gobject,
-  feat_type = NULL,
-  spat_unit = NULL,
-  method = c("grid", "network"),
-  expression_values = c("normalized", "scaled", "custom"),
-  subset_feats = NULL,
-  subset_genes = NULL,
-  spatial_network_name = "Delaunay_network",
-  network_smoothing = NULL,
-  spatial_grid_name = "spatial_grid",
-  min_cells_per_grid = 4,
-  cor_method = c("pearson", "kendall", "spearman")
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{feat_type}{feature type}
-
-\item{spat_unit}{spatial unit}
-
-\item{method}{method to use for spatial averaging}
-
-\item{expression_values}{gene expression values to use}
-
-\item{subset_feats}{subset of feats to use}
-
-\item{subset_genes}{deprecated, use \code{subset_feats}}
-
-\item{spatial_network_name}{name of spatial network to use}
-
-\item{network_smoothing}{smoothing factor beteen 0 and 1
-(default: automatic)}
-
-\item{spatial_grid_name}{name of spatial grid to use}
-
-\item{min_cells_per_grid}{minimum number of cells to consider a grid}
-
-\item{cor_method}{correlation method}
-}
-\value{
-returns a spatial correlation object: "spatCorObject"
-}
-\description{
-Detect genes that are spatially correlated
-}
-\details{
-For method = network, it expects a fully connected spatial network. You
-can make sure to create a
-fully connected network by setting minimal_k > 0 in the
-\code{\link{createSpatialNetwork}} function.
-\itemize{
- \item{1. grid-averaging: }{average gene expression values within a
- predefined spatial grid}
- \item{2. network-averaging: }{smoothens the gene expression matrix by
- averaging the expression within one cell
- by using the neighbours within the predefined spatial network. b is a
- smoothening factor that defaults to 1 - 1/k, where k is the median
- number of  k-neighbors in the selected spatial network. Setting b = 0
- means no smoothing and b = 1 means no contribution
- from its own expression.}
-}
-The spatCorObject can be further explored with showSpatialCorGenes()
-}
-\seealso{
-\code{\link{showSpatialCorGenes}}
-}