Merge pull request #143 from omnideconv/giotto_update

Giotto update

R/SpatialDWLS.R

@@ -6,16 +6,13 @@
 #' @param cell_type_col column of single_cell_obj containing cell type information
 #' @param dim_method dimension reduction method
 #' @param cluster_method cluster method to  use when calculating marker genes
-#' @param ... additional paramters
+#' @param ... additional paramdataters
 build_model_spatial_dwls <- function(single_cell_obj, assay_sc = "counts", marker_method = "scran", topNgenes = 100, cell_type_col = "cell_ontology_class", dim_method = "pca", cluster_method = "leiden", ...) {
   # TODO Checks
   if (!checkCol(single_cell_obj, cell_type_col)) {
     stop("cell_type_col not available")
   }
 
-  # init_python()
-
-  # TODO, implement instructions
   if (!exists("giotto_instructions")) {
     giotto_instructions <<- Giotto::createGiottoInstructions(python_path = reticulate::conda_list()$python[which(reticulate::conda_list()$name == "r-omnideconv")])
   }
@@ -35,27 +32,21 @@ build_model_spatial_dwls <- function(single_cell_obj, assay_sc = "counts", marke
 
 
   # markers
-  # NOTE the makeSignMatrix function requires a list of genes to use in the signature
-  # They just mention "e.g. markers", so i thought to use the marker gene function provided by giotto
   if (marker_method %in% c("scran", "gini", "mast")) {
     message("Calculating markers by using the method: ", marker_method)
-    # turn into giotto for markers!!
-    obj <- Giotto::createGiottoObject(scExpression, instructions = giotto_instructions)
+    obj <- Giotto::createGiottoObject(scExpression, instructions = giotto_instructions, ...)
 
     obj <- doGiottoWorkflow(obj, dim_method = dim_method, cluster_method = cluster_method)
 
 
     message("Calculating Markers")
-    # calculate based on selection
-    # TODO add assay to use or select default one by handling all that during Giotto object creation
 
     markers <- Giotto::findMarkers(obj, method = marker_method, cluster_column = paste0(cluster_method, "_clus"))
 
-    # markers are a complex list with marker genes for each cluster
-    # extract the top genes for each cluster and intersect
+    # extract the top genes for each cluster
     genes <- character()
     for (cluster in markers) {
-      genes <- c(genes, cluster$genes[1:topNgenes])
+      genes <- c(genes, cluster$feats[1:topNgenes])
     }
 
     genes <- unique(genes)
@@ -69,8 +60,7 @@ build_model_spatial_dwls <- function(single_cell_obj, assay_sc = "counts", marke
   # get cell type vector from object
   cell_types <- as.vector(single_cell_obj[[cell_type_col]])
 
-  # TODO Check if markers are necessary!!!
-  signature <- Giotto::makeSignMatrixDWLSfromMatrix(matrix = scExpression, sign_gene = genes, cell_type_vector = cell_types)
+  signature <- Giotto::makeSignMatrixDWLSfromMatrix(matrix = scExpression, sign_gene = genes, cell_type_vector = cell_types, ...)
 
   return(signature)
 }
@@ -83,32 +73,29 @@ build_model_spatial_dwls <- function(single_cell_obj, assay_sc = "counts", marke
 #' @param result_name token to identify deconvolution results in object, default = "spatialdwls"
 #' @param ... additional parameters
 deconvolute_spatial_dwls <- function(spatial_obj, signature, assay_sp = "counts", result_name = "spatialdwls", ...) {
-  # TODO checks
-
   # create Giotto Object
   spExpression <- SummarizedExperiment::assay(spatial_obj, assay_sp) %>% as("dgCMatrix")
   spCoords <- SpatialExperiment::spatialCoords(spatial_obj)
 
-  # init_python()
-
-  ##########
   if (!exists("giotto_instructions")) {
     giotto_instructions <<- Giotto::createGiottoInstructions(python_path = reticulate::conda_list()$python[which(reticulate::conda_list()$name == "r-omnideconv")])
   }
 
 
-  obj <- Giotto::createGiottoObject(raw_exprs = spExpression, spatial_locs = spCoords, instructions = giotto_instructions)
+  obj <- Giotto::createGiottoObject(expression = spExpression, spatial_locs = spCoords, instructions = giotto_instructions)
 
   obj <- doGiottoWorkflow(obj, ...)
 
-  deconvolution <- Giotto::runDWLSDeconv(obj, sign_matrix = signature, n_cell = 10, return_gobject = FALSE)
+  deconvolution <- Giotto::runDWLSDeconv(obj, sign_matrix = signature, n_cell = 10, return_gobject = TRUE)
+
+  df <- data.frame(getSpatialEnrichment(deconvolution, output = "data.table"))
 
-  deconvolution$cell_ID <- NULL
+  df$cell_ID <- NULL
 
   # attach method token
-  deconvolution <- attachToken(deconvolution, result_name)
+  df <- attachToken(df, result_name)
 
-  return(deconvolution)
+  return(df)
 }
 
 #' Perform Giotto Workflow of Normalization, HVG, Dimension Reduction and Clustering
@@ -117,7 +104,11 @@ deconvolute_spatial_dwls <- function(spatial_obj, signature, assay_sp = "counts"
 #' @param calculateHVG Wheter to calculate HVG
 #' @param dim_method dimension reduction method to use: pca, tsne, umap
 #' @param cluster_method cluster method to use c(leiden, kmeans, hclust, louvain)
-doGiottoWorkflow <- function(obj, calculateHVG = TRUE, dim_method = "pca", cluster_method = "leiden") {
+#' @param show_plot show plot
+#' @param return_plot return plot
+#' @param save_plot save plot
+#' @param ... additional parameters
+doGiottoWorkflow <- function(obj, calculateHVG = TRUE, dim_method = "pca", cluster_method = "leiden", show_plot = FALSE, return_plot = FALSE, save_plot = FALSE, ...) {
   if (!class(obj)[[1]] == "giotto") {
     stop("Object needs to be a Giotto object!")
   }
@@ -126,29 +117,28 @@ doGiottoWorkflow <- function(obj, calculateHVG = TRUE, dim_method = "pca", clust
     message("Starting Giotto Workflow")
   }
 
-  # TODO check if all those steps have already been performed???
   obj <- Giotto::normalizeGiotto(obj)
 
-  # HVG
-  # NOTE: the workflow fails often when not calculating hvgs, so this should be default!!!!!
+  # HVF
   if (calculateHVG) {
-    message("Giotto: Calculating HVGs")
-    obj <- Giotto::calculateHVG(obj, show_plot = FALSE, return_plot = FALSE, save_plot = FALSE)
+    message("Giotto: Calculating HVFs")
+    obj <- Giotto::calculateHVF(obj, show_plot = show_plot, return_plot = return_plot, save_plot = save_plot)
   }
 
 
   # Dimension Reduction
   message("Giotto: Performing Dimension Reduction")
-  obj <- Giotto::runPCA(obj) # run anayway, either pca is used or it needs to be run for tnse and umap?
+  obj <- Giotto::runPCA(obj)
+
   obj <- switch(dim_method,
     pca = {
       obj
-    }, # just return the object, already performed
+    },
     tsne = {
-      Giotto::runtSNE(obj) # run based on pca, could also be done directly
+      Giotto::runtSNE(obj)
     },
     umap = {
-      Giotto::runUMAP(obj) # run based on pca
+      Giotto::runUMAP(obj)
     }
   )
 
@@ -160,8 +150,6 @@ doGiottoWorkflow <- function(obj, calculateHVG = TRUE, dim_method = "pca", clust
     dim_reduction_name = dim_method
   )
 
-
-  # TODO add parameters for all methods
   # Clustering
   message("Giotto: Clustering")
   obj <- switch(cluster_method,

R/utils.R

@@ -258,10 +258,6 @@ attachToken <- function(deconvolution, token = "deconv") {
     stop("Deconvolution result is missing but is required")
   }
 
-  if (is.null(token)) {
-    message("No token provided, using 'deconv' as token")
-  }
-
   # get colnames, attach token and overwrite
   celltypes <- colnames(deconvolution)
   celltypes <- paste0(token, "_", celltypes)