check correction

R/clustering.R

@@ -53,11 +53,11 @@ Cluster<-function(object,
                   use.previous = FALSE, ...){
 
   invisible(gc())
+  if(any(names(reducedDims(object))=="CComponents"))
+    use.reduced.dims = "CComponents"
 
   # If using integrated embeddings for clustering
   if(!is.null(use.reduced.dims)){
-    if(any(object@metadata[["dropClust"]]=="Correction"))
-      use.reduced.dims = "CComponents"
     mat = reducedDim(object, use.reduced.dims)
 
     cat("Clustering on embedded dimensions...")

R/merge.R

@@ -98,7 +98,7 @@ Merge<-function(objects, use.de.genes = TRUE){
   SummarizedExperiment::colData(mix.data)$Batch = batch
 
   if(use.de.genes){
-    common_genes_de = intersect(unique(as.vector(unlist(all_de_genes))), unique(unlist(merge_genes)))
+    common_genes_de = intersect(unique(as.vector(unlist(all_de_genes))), unique(unlist(common_genes)))
     SummarizedExperiment::rowData(mix.data)$CommonDEGenes = rep(FALSE, nrow(mix.data))
     SummarizedExperiment::rowData(mix.data)$CommonDEGenes[which(rownames(mix.data) %in% common_genes_de)]<- TRUE
   }

compararisons/CellBench/compare_correction.R

@@ -3,7 +3,7 @@ library(SingleCellExperiment)
 library("magrittr")
 ## CellBench dataset
 
-load("C:/Projects/CellBench/CellBench_data-master/data/sincell_with_class.RData")
+load("C:/Projects/data/Cellbench/sincell_with_class.RData")
 load("~/GitHub/dropclust_benchmarks/analysis/bc/Cellbench/out_gene_ids.Rda")
 
 source("compararisons/Cellbench/plots.R")
@@ -37,7 +37,7 @@ merged_data<-Merge(all.objects)
 corrected_data <- Correction(merged_data, close_th = 0.1, cells_th = 0.1,
                        components = 10, n_neighbors = 20, init = "spca", min_dist = 0.5)
 
-PROJ_c_dc = reducedDim(corrected_data, "iComponents")
+PROJ_c_dc = reducedDim(corrected_data, "CComponents")
 plot_proj_df_true_dc<-data.frame(Y1 = PROJ_c_dc[,1],Y2 = PROJ_c_dc[,2],
                                  color = as.factor(corrected_data$cell_line),
                                  batch = as.integer(corrected_data$Batch))

vignettes/batchCorrection.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 ```
 
 ## Integrative analysis
-```{r}
+```{r,warning=FALSE,message=FALSE}
 
 library(dropClust)
 load(url("https://raw.githubusercontent.com/LuyiTian/CellBench_data/master/data/sincell_with_class.RData"))
@@ -49,17 +49,29 @@ batch.id = merged_data$Batch
 ## Perform correction and dimension reduction
 
 ```{r}
-
-dc.corr <- Correction(merged_data, close_th = 0.1, cells_th = 0.1,
-                       components = 10, n_neighbors = 30, init = "spca")
+set.seed(0)
+dc.corr <-  Correction(merged_data,  method="default", close_th = 0.1, cells_th = 0.1,
+                       components = 10, n_neighbors = 20, init = "spca", min_dist = 0.5)
 ```
 
 ## Perform Clustering on integrated dimensions
 
 ```{r}
-cc = Cluster(dc.corr,method = "kmeans",centers = 3)
+dc.corr = Cluster(dc.corr,method = "default",centers = 3)
 ```
+## Visualizing clusters
+
+Compute 2D embeddings for samples followed by post-hoc clustering.
 
+```{r}
+ScatterPlot(dc.corr,title = "Clusters")
+
+PROJ_c_dc = reducedDim(dc.corr, "CComponents")
+plot_proj_df_true_dc<-data.frame(Y1 = PROJ_c_dc[,1],Y2 = PROJ_c_dc[,2],
+                                 color = as.factor(dc.corr$cell_line),
+                                 batch = as.integer(dc.corr$Batch))
+batch_plot(plot_proj_df_true_dc,filename = NA, title = "Corrected DC ",  type=NULL)
+```
 
 ## Marker discovery from the merged dataset
 ```{r}