simulation_CARD_analysis.Rmd

---
title: "simulation_CARD_analysis"
output: html_document
date: "2023-10-09"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r,warning=FALSE,error=FALSE,massage=FALSE,prompt=FALSE}
suppressPackageStartupMessages({
library(readr)
library(Seurat)
library(SeuratDisk)
library(SeuratObject)
library(anndata)
library(ggplot2)
library(factoextra) # clustering visualization
library(mclust)
library(zinbwave)
library(SingleCellExperiment)
library(aricode)
library(pscl)
library(knitr)
library(kableExtra)
library(Biobase)
library(Matrix)
library(MCMCpack)
library(Hmisc)
library(tidyverse)  # data manipulation
library(cluster)  # clustering algorithms
library(dendextend) # for comparing two dendrograms
library(dplyr)
library(GGally) # correlation plot
library(tidyr)
library(flextable)
library(NMF)
library(ggpubr) # MDS
library(RcppML) 
library(reshape2) 
library(cowplot) 
library(MAST)
library(smfishHmrf)
library(trendsceek)
library(sparklyr)
library(multinet)
library(RTriangle)
library(FactoMineR)
library(jackstraw)
library(CARD)
library(spacexr)
library(gridExtra)
library(pbmcapply)
library(Giotto)
library(smfishHmrf)
library(BayesSpace)
library(Banksy)
library(gridExtra)
})
```

```{r}
# Load the dataset
### Reference dataset
ExpressionSet <- readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/ExpressionSet.rds")

### Pattern label
pattern_gp_label = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/pattern_gp_label_human_heart.rds")

### Load functions
generateMultiN = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/generateMultiN_human_heart.rds")

generateSpatial_norep_fixedProp = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/generateSpatial_norep_fixedProp_human_heart.rds")

ct.varname = "cellType"
ct.select = c("Erythrocytes",
              "Epicardium-derived cells",
              "Fibroblast-like",
              "Smooth muscle cells",
              "Endothelium / pericytes",
              "Myoz2-enriched cardiomyocytes",
              "Immune cells",
              "Capillary endothelium",
              "Epicardial cells",
              "Ventricular cardiomyocytes",
              "Atrial cardiomyocytes", 
              "Cardiac neural crest cells")
sample.varname = "sampleID"

# Initialize parameters for the simulation
imix <- 0
ntotal <- 10
mix1 = mix2 = mix3 = mix4 = mix5 = c(1 - (0.2 * imix),0.2*imix)

# Initialize a list to store the simulated results
simulated_results <- list()

for(i in 1:30) {
  # Set seed
  iseed <- 2022 + i
  set.seed(iseed)

  # Generate simulation
  spatial.pseudo = generateSpatial_norep_fixedProp(
    seed = iseed,
    ExpressionSet = ExpressionSet,
    ct.varname = ct.varname,
    sample.varname = sample.varname,
    ct.select = ct.select,
    sample.withRep = F,
    pattern_gp_label = pattern_gp_label,
    ntotal = ntotal,
    mix1 = mix1,
    mix2 = mix2,
    mix3 = mix3,
    mix4 = mix4,
    mix5 = mix5
    )

  # Convert the result to a dataframe
  simulated_count_data_df = as.data.frame(spatial.pseudo$pseudo.data)
  
  # Store the result in the list
  simulated_results[[i]] <- simulated_count_data_df
}
```

```{r}
saveRDS(simulated_results,"/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/simulated_results_n30.rds")
```

```{r}
# Check one of the results (for example, the first result)
simulated_results[[20]]
```

```{r}
## Location
slice6_aligned_gene_expression <- read.csv("/dski/nobackup/xiaoyinl/Clustering_results/dataset_slices_aligned/gene_expression_slice6_aligned.csv")
rownames(slice6_aligned_gene_expression) <- slice6_aligned_gene_expression$X
slice6_aligned_gene_expression$X <-NULL
slice6_aligned_obsm <- read.csv("/dski/nobackup/xiaoyinl/Clustering_results/dataset_slices_aligned/slice_6_aligned_all_csv/obsm.csv")
slice6_aligned_x <- slice6_aligned_obsm$spatial_aligned1
slice6_aligned_y <- slice6_aligned_obsm$spatial_aligned2
slice6_aligned <- cbind(slice6_aligned_x,slice6_aligned_y)
slice6_aligned_df <- as.data.frame(slice6_aligned)
rownames(slice6_aligned_df) <- rownames(slice6_aligned_gene_expression)
colnames(slice6_aligned_df) <- c("x","y")
## Ground Truth
results_human_heart_clustering <- read.csv("/dski/nobackup/xiaoyinl/STitch3D_analysis_and_dataset/results_human_heart/clustering_result.csv")
prop_slice6 <- read.csv("/dski/nobackup/xiaoyinl/STitch3D_analysis_and_dataset/results_human_heart/prop_slice6.csv")
slice6_values <- prop_slice6$X
slice6_louvain_clustering <- results_human_heart_clustering %>%
  filter(X %in% slice6_values)
slice6_louvain_clustering$louvain_relabel <- slice6_louvain_clustering$louvain + 1
slice6_louvain_clustering
ground_truth_label <- slice6_louvain_clustering$louvain_relabel
## Location data frame
location_df <- slice6_aligned_df
location_df
```

```{r, warning=FALSE, message=FALSE}
ARI_results_list <- list()
NMI_results_list <- list()
all_intensity_k_means_PCA_clusters <- list()
all_intensity_hclust_PCA_clusters <- list()
all_intensity_louvain_PCA_clusters <- list()
all_spatial_cluster_Giotto_clusters <- list()
all_spatial_cluster_BayesSpace_clusters <- list()
all_spatial_cluster_Banksy_clusters <- list()
all_k_means_RCTD_clusters <- list()
all_k_means_CARD_clusters <- list()

for (i in 1:length(simulated_results)) {
  # Set the seed based on the iteration
  iseed <- 2022 + i
  set.seed(iseed)
  
  # Simulated dataset
  simulated_count_data_df <- simulated_results[[i]]
  simulated_count_data_matrix <- as.matrix(simulated_count_data_df)
  
  # Analysis
  ## Dimension reduction: PCA
  ### Creat Seurat object
  seurat_object <- CreateSeuratObject(counts = simulated_count_data_df)
  
  ### Normalize the data
  seurat_object <- NormalizeData(seurat_object)
  
  ### Select highly variable genes
  seurat_hvg_top_2000 <- FindVariableFeatures(seurat_object, selection.method = "vst", nfeatures = 2000)
  
  ### Save the top 2000 HVGs
  var_features <- VariableFeatures(seurat_hvg_top_2000)
  var_features_data <- seurat_hvg_top_2000[["RNA"]]@counts[var_features, ]
  gene_expression_hvg_top_2000_df <- as.data.frame(as.matrix(var_features_data))
  
  ### Scale the data
  all_genes <- rownames(seurat_hvg_top_2000)
  seurat_hvg_top_2000 <- ScaleData(seurat_hvg_top_2000, features = all_genes)
  
  ### Perform PCA
  seurat_hvg_top_2000 <- RunPCA(seurat_hvg_top_2000, features = VariableFeatures(seurat_hvg_top_2000), npcs = 20)
  
  #### Extract PCA results[20 PCs]
  pca_data <- Embeddings(object = seurat_hvg_top_2000, reduction = "pca")
  pca_data_selected_dimension <- pca_data[, 1:20]
  pca_data_selected_dimension_df <- as.data.frame(pca_data_selected_dimension)
  
  ## Clustering analysis
  ### K-means
  intensity_k_means_PCA <- kmeans(pca_data_selected_dimension_df, centers = 5)  
  intensity_k_means_PCA_clusters <- intensity_k_means_PCA$cluster
  
  ### Hierarchical clustering
  #### Dissimilarity matrix
  PCA_results_matrix <- dist(pca_data_selected_dimension_df, method = "euclidean")
  #### Ward's method
  PCA_results_hclust_ward <- hclust(PCA_results_matrix, method = "ward.D2" )
  #### Cut tree into 5 groups
  intensity_hclust_PCA_clusters <- cutree(PCA_results_hclust_ward, k = 5)
  
  ### Louvain Community detection
  gene_expression_hvg_top_2000_matrix <-
      as.matrix(gene_expression_hvg_top_2000_df)
  sce_louvain <- SingleCellExperiment(assays=list(
      counts=as(gene_expression_hvg_top_2000_matrix,"dgCMatrix")))
  
  #### PCA processing
  pca_data_selected_dimension_matrix <- as.matrix(pca_data_selected_dimension_df)
  reducedDims(sce_louvain)[["PCA"]] <- pca_data_selected_dimension_matrix
  g.jaccard = scran::buildSNNGraph(sce_louvain, use.dimred="PCA", type="jaccard")
  
  #### Find cluster number == 5
  resolution_values <- seq(0.1, 3.0, by = 0.01)
  optimal_resolution <- NULL
  
  for (res in resolution_values) {
      louvain_clusters_k5 <- igraph::cluster_louvain(g.jaccard, resolution = res)$membership
      if (length(unique(louvain_clusters_k5)) == 5) {
          optimal_resolution <- res
          intensity_louvain_PCA_clusters <- louvain_clusters_k5
          break
      }
  }
  
  if (is.null(optimal_resolution)) {
      print("No optimal resolution found for exactly 5 clusters.")
  }
  
  ### Giotto: HRMF
  #### processing
  my_instructions = createGiottoInstructions(python_path = '/albona/nobackup/xiaoyinl/anaconda3/envs/giotto_env/bin/python')
  expr_Giotto <- as.matrix(gene_expression_hvg_top_2000_df)
  location_Giotto <- location_df
  my_giotto_object <- createGiottoObject(raw_exprs = expr_Giotto, 
                                         spatial_locs = location_Giotto, 
                                         instructions = my_instructions)
  my_giotto_object <- filterGiotto(gobject = my_giotto_object, 
                                   expression_threshold = 0.5, 
                                   gene_det_in_min_cells = 20, 
                                   min_det_genes_per_cell = 0)
  my_giotto_object <- normalizeGiotto(gobject = my_giotto_object)
  
  #### create network (required for binSpect methods)
  my_giotto_object = createSpatialNetwork(gobject = my_giotto_object, minimum_k = 2)
  
  #### identify genes with a spatial coherent expression profile
  km_spatialgenes = binSpect(my_giotto_object, bin_method = 'kmeans')
  
  #### create a directory to save your HMRF results to
  hmrf_folder_human_heart = paste0(getwd(),'/','hmrf_folder_save/')
  if(!file.exists(hmrf_folder_human_heart)) dir.create(hmrf_folder_human_heart, recursive = T)
  
  #### perform hmrf
  my_spatial_genes = km_spatialgenes[1:500]$genes
  HMRF_spatial_genes = doHMRF(gobject = my_giotto_object,
                              expression_values = 'scaled',
                              spatial_genes = my_spatial_genes,
                              spatial_network_name = 'Delaunay_network',
                              k = 5,
                              betas = c(28,2,2),
                              output_folder = paste0(hmrf_folder_human_heart, '/',
                                    'Spatial_genes/SG_top500_k5_scaled'))
  
  #### Add the HMRF results
  my_giotto_object = addHMRF(gobject = my_giotto_object,
                                      HMRFoutput = HMRF_spatial_genes,
                                      k = 5, betas_to_add = c(28),
                                      hmrf_name = 'HMRF')
  
  #### Save the results
  spatial_cluster_Giotto_clusters <- as.numeric(my_giotto_object@cell_metadata$HMRF_k5_b.28)
  
  ### BayesSpace
  gene_expression_hvg_top_2000_matrix <- as.matrix(gene_expression_hvg_top_2000_df)
  location_BayesSpace <- location_df
  sce_pca <- SingleCellExperiment(assays=list(
    counts=as(gene_expression_hvg_top_2000_matrix,"dgCMatrix")))
  colData(sce_pca)$array_row <- location_BayesSpace$x
  colData(sce_pca)$array_col <- location_BayesSpace$y
  
  #### Add the PCA results
  sce_pca <- spatialPreprocess(sce_pca, platform="ST", 
                               n.PCs=20, n.HVGs=2000, log.normalize=TRUE)
  
  #### Perform BayesSpace
  sce_pca <- spatialCluster(sce_pca, 
                            q=5, 
                            platform="ST", 
                            use.dimred = "PCA",
                            d=20,
                            init.method="mclust",
                            model="t", 
                            gamma=2,
                            nrep=10000, 
                            burn.in=100,
                            save.chain=TRUE)
  
  sce_pca_df <- as.data.frame(colData(sce_pca))
  spatial_cluster_BayesSpace_clusters <- sce_pca_df$spatial.cluster
  
  ### Banksy
  #### Preprocessing
  pca_data_selected_dimension_transpose <- t(pca_data_selected_dimension_df) # PCA results
  pca_data_selected_dimension_transpose_df <- as.data.frame(pca_data_selected_dimension_transpose)
  expr_banksy <- as.matrix(pca_data_selected_dimension_transpose_df)
  location_bankdy <- location_df
  
  #### Find cluster number == 5
  resolution_values <- seq(0.1, 2.0, by = 0.01)
  
  optimal_resolution <- NULL
  
  for (res in resolution_values) {
    banksy <- BanksyObject(own.expr = expr_banksy, cell.locs = location_bankdy)
    banksy <- ClusterBanksy(banksy, method = 'leiden', pca = FALSE, resolution = res)
    col_name <- paste0("clust_M1_lam0.2_k50_res", res)
  
    bank_meta_data_df <- as.data.frame(banksy@meta.data)
    Banksy_clusters_k5 <- bank_meta_data_df[[col_name]]
  
    if (length(unique(Banksy_clusters_k5)) == 5) {
      optimal_resolution <- res
      spatial_cluster_Banksy_clusters <- Banksy_clusters_k5
      break
    }
  }
  
  if (is.null(optimal_resolution)) {
    print("No optimal resolution found for exactly 5 clusters.")
  }
  
  ### RCTD
  #### Load the dataset
  ##### Reference
  reference_object = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/reference_object_RCTD.rds")
  
  ##### Spatial Transcriptomics data
  ###### Coordinates
  location_RCTD <- location_df
  ###### nUMI
  nUMI_spatial <- colSums(simulated_count_data_df) # In this case, total counts per pixel is nUMI
  ###### Create SpatialRNA object
  SpatialRNA_object <- SpatialRNA(location_RCTD, simulated_count_data_df, nUMI_spatial)
  
  #### RCTD algorithm
  myRCTD <- create.RCTD(SpatialRNA_object, reference_object, max_cores = 1)
  myRCTD <- run.RCTD(myRCTD, doublet_mode = 'doublet')
  
  #### RCTD results
  results <- myRCTD@results
  ##### normalize the cell type proportions to sum to 1.
  norm_weights = normalize_weights(results$weights) 
  cell_type_names <- myRCTD@cell_type_info$info[[2]] #list of cell type names
  spatialRNA <- myRCTD@spatialRNA
  
  ##### cell type proportion
  RCTD_cell_type_proportion_matrix <- as.matrix(norm_weights)
  RCTD_cell_type_proportion_df <- as.data.frame(RCTD_cell_type_proportion_matrix)
  
  ##### RCTD + K-means
  k_means_RCTD <- kmeans(RCTD_cell_type_proportion_df, centers = 5)  
  k_means_RCTD_clusters <- k_means_RCTD$cluster
  
  ### CARD
  #### Input data
  ##### Single cell RNAseq ((scRNA-seq)) data
  scRNA_seq_count <- readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/scRNA_seq_count.rds")
  
  ##### celltype reference
  scRNA_seq_meta = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/scRNA_seq_meta.rds")
  
  ##### Simulate count data
  simulated_count_data_sparse_matrix <- as.matrix(simulated_count_data_df)
  spatial_count_matrix <- as(simulated_count_data_sparse_matrix, "matrix")
  
  ##### location
  location_CARD <- location_df
  
  #### Create an CARD object
  CARD_obj = createCARDObject(
  	sc_count = scRNA_seq_count,
  	sc_meta = scRNA_seq_meta,
  	spatial_count = spatial_count_matrix,
  	spatial_location = location_CARD,
  	ct.varname = "cellType",
  	ct.select = unique(scRNA_seq_meta$cellType),
  	sample.varname = "sampleInfo",
  	minCountGene = 100,
  	minCountSpot = 5) 
  
  #### Deconvolution using CARD
  CARD_obj_deconvolution = CARD_deconvolution(CARD_object = CARD_obj)
  CARD_obj_deconvolution_proportion_df <- as.data.frame(CARD_obj_deconvolution@Proportion_CARD)
  
  ##### CARD + K-means
  k_means_CARD <- kmeans(CARD_obj_deconvolution_proportion_df, centers = 5)  
  k_means_CARD_clusters <- k_means_CARD$cluster
  
  ### Evaluation
  #### ARI
  ARI_k_means_PCA_results <- adjustedRandIndex(ground_truth_label, intensity_k_means_PCA_clusters)
  ARI_hclust_PCA_results  <- adjustedRandIndex(ground_truth_label, intensity_hclust_PCA_clusters)
  ARI_louvain_PCA_results  <- adjustedRandIndex(ground_truth_label, intensity_louvain_PCA_clusters)
  ARI_giotto_PCA_results <- adjustedRandIndex(ground_truth_label,spatial_cluster_Giotto_clusters)
  ARI_BayesSpace_PCA_results  <- adjustedRandIndex(ground_truth_label, spatial_cluster_BayesSpace_clusters)
  ARI_Banksy_PCA_results <- adjustedRandIndex(ground_truth_label,spatial_cluster_Banksy_clusters)
  ARI_k_means_RCTD_results <- ARI(ground_truth_label, k_means_RCTD_clusters)
  ARI_k_means_CARD_results <- ARI(ground_truth_label, k_means_CARD_clusters)
  
  #### NMI
  NMI_k_means_PCA_results <- NMI(ground_truth_label, intensity_k_means_PCA_clusters)
  NMI_hclust_PCA_results  <- NMI(ground_truth_label, intensity_hclust_PCA_clusters)
  NMI_louvain_PCA_results  <- NMI(ground_truth_label, intensity_louvain_PCA_clusters)
  NMI_giotto_PCA_results <- NMI(ground_truth_label,spatial_cluster_Giotto_clusters)
  NMI_BayesSpace_PCA_results  <- NMI(ground_truth_label, spatial_cluster_BayesSpace_clusters)
  NMI_Banksy_spatial_PCA_results <- NMI(ground_truth_label,spatial_cluster_Banksy_clusters)
  NMI_k_means_RCTD_results <- NMI(ground_truth_label, k_means_RCTD_clusters)
  NMI_k_means_CARD_results <- NMI(ground_truth_label, k_means_CARD_clusters)

  # Save ARI and NMI results
  ARI_results_list[[i]] <- c(
    ARI_k_means_PCA_results, 
    ARI_hclust_PCA_results, 
    ARI_louvain_PCA_results, 
    ARI_giotto_PCA_results, 
    ARI_BayesSpace_PCA_results, 
    ARI_Banksy_PCA_results,
    ARI_k_means_RCTD_results, 
    ARI_k_means_CARD_results
  )
  
  NMI_results_list[[i]] <- c(
    NMI_k_means_PCA_results,
    NMI_hclust_PCA_results,
    NMI_louvain_PCA_results,
    NMI_giotto_PCA_results,
    NMI_BayesSpace_PCA_results,
    NMI_Banksy_spatial_PCA_results,
    NMI_k_means_RCTD_results,
    NMI_k_means_CARD_results
  )
  # Save every clustering labels
  all_intensity_k_means_PCA_clusters[[i]] <- intensity_k_means_PCA_clusters
  all_intensity_hclust_PCA_clusters[[i]] <- intensity_hclust_PCA_clusters
  all_intensity_louvain_PCA_clusters[[i]] <- intensity_louvain_PCA_clusters
  all_spatial_cluster_Giotto_clusters[[i]] <- spatial_cluster_Giotto_clusters
  all_spatial_cluster_BayesSpace_clusters[[i]] <- spatial_cluster_BayesSpace_clusters
  all_spatial_cluster_Banksy_clusters[[i]] <- spatial_cluster_Banksy_clusters
  all_k_means_RCTD_clusters[[i]] <- k_means_RCTD_clusters
  all_k_means_CARD_clusters[[i]] <- k_means_CARD_clusters
}

ARI_df <- as.data.frame(do.call(rbind, ARI_results_list))
NMI_df <- as.data.frame(do.call(rbind, NMI_results_list))
# For intensity_k_means_PCA_clusters
intensity_k_means_PCA_df <- as.data.frame(do.call(rbind, all_intensity_k_means_PCA_clusters))
# For intensity_hclust_PCA_clusters
intensity_hclust_PCA_df <- as.data.frame(do.call(rbind, all_intensity_hclust_PCA_clusters))
# For intensity_louvain_PCA_clusters
intensity_louvain_PCA_df <- as.data.frame(do.call(rbind, all_intensity_louvain_PCA_clusters))
# For spatial_cluster_Giotto_clusters
spatial_cluster_Giotto_df <- as.data.frame(do.call(rbind, all_spatial_cluster_Giotto_clusters))
# For spatial_cluster_BayesSpace_clusters
spatial_cluster_BayesSpace_df <- as.data.frame(do.call(rbind, all_spatial_cluster_BayesSpace_clusters))
# For spatial_cluster_Banksy_clusters
spatial_cluster_Banksy_df <- as.data.frame(do.call(rbind, all_spatial_cluster_Banksy_clusters))
# For k_means_RCTD_clusters
k_means_RCTD_df <- as.data.frame(do.call(rbind, all_k_means_RCTD_clusters))
# For k_means_CARD_clusters
k_means_CARD_df <- as.data.frame(do.call(rbind, all_k_means_CARD_clusters))
```

```{r}
write.csv(ARI_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/ARI_df.csv")
write.csv(NMI_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/NMI_df.csv")
write.csv(intensity_k_means_PCA_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/intensity_k_means_PCA_df.csv")
write.csv(intensity_hclust_PCA_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/intensity_hclust_PCA_df.csv")
write.csv(intensity_louvain_PCA_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/intensity_louvain_PCA_df.csv")
write.csv(spatial_cluster_Giotto_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/spatial_cluster_Giotto_df.csv")
write.csv(spatial_cluster_BayesSpace_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/spatial_cluster_BayesSpace_df.csv")
write.csv(spatial_cluster_Banksy_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/spatial_cluster_Banksy_df.csv")
write.csv(k_means_RCTD_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/k_means_RCTD_df.csv")
write.csv(k_means_CARD_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/k_means_CARD_df.csv")
```


```{r}
ARI_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/ARI_df.csv")
NMI_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/NMI_df.csv")
intensity_k_means_PCA_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/intensity_k_means_PCA_df.csv")
intensity_hclust_PCA_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/intensity_hclust_PCA_df.csv")
intensity_louvain_PCA_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/intensity_louvain_PCA_df.csv")
spatial_cluster_Giotto_df = read.csv( "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/spatial_cluster_Giotto_df.csv")
spatial_cluster_BayesSpace_df = read.csv( "/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/spatial_cluster_BayesSpace_df.csv")
spatial_cluster_Banksy_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/spatial_cluster_Banksy_df.csv")
k_means_RCTD_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/k_means_RCTD_df.csv")
k_means_CARD_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/simulation_slice6_results/CARD_simulation_results_new/k_means_CARD_df.csv")
```

```{r}
rownames(ARI_df) <- ARI_df$X
ARI_df$X <- NULL
rownames(NMI_df) <- NMI_df$X
NMI_df$X <- NULL
rownames(intensity_k_means_PCA_df) <- intensity_k_means_PCA_df$X
intensity_k_means_PCA_df$X <- NULL
rownames(intensity_hclust_PCA_df) <- intensity_hclust_PCA_df$X
intensity_hclust_PCA_df$X <- NULL
rownames(intensity_louvain_PCA_df) <- intensity_louvain_PCA_df$X
intensity_louvain_PCA_df$X <- NULL
rownames(spatial_cluster_Giotto_df) <- spatial_cluster_Giotto_df$X
spatial_cluster_Giotto_df$X <- NULL
rownames(spatial_cluster_BayesSpace_df) <- spatial_cluster_BayesSpace_df$X
spatial_cluster_BayesSpace_df$X <- NULL
rownames(spatial_cluster_Banksy_df) <- spatial_cluster_Banksy_df$X
spatial_cluster_Banksy_df$X <- NULL
rownames(k_means_RCTD_df) <- k_means_RCTD_df$X
k_means_RCTD_df$X <- NULL
rownames(k_means_CARD_df) <- k_means_CARD_df$X
k_means_CARD_df$X <- NULL
```

```{r}
ARI_df_transposed <- as.data.frame(t(ARI_df))
NMI_df_transposed <- as.data.frame(t(NMI_df))
rownames(ARI_df_transposed) <- c("k-means",
                                 "hclust",
                                 "louvain",
                                 "Giotto",
                                 "BayesSpace",
                                 "Banksy",
                                 "RCTD",
                                 "CARD")
rownames(NMI_df_transposed) <- c("k-means",
                                 "hclust",
                                 "louvain",
                                 "Giotto",
                                 "BayesSpace",
                                 "Banksy",
                                 "RCTD",
                                 "CARD")
ARI_df_transposed
NMI_df_transposed
```

```{r}
intensity_k_means_PCA_df_transposed <- as.data.frame(t(intensity_k_means_PCA_df))
intensity_hclust_PCA_df_transposed <- as.data.frame(t(intensity_hclust_PCA_df))
intensity_louvain_PCA_df_transposed <- as.data.frame(t(intensity_louvain_PCA_df))
spatial_cluster_Giotto_df_transposed <- as.data.frame(t(spatial_cluster_Giotto_df))
spatial_cluster_BayesSpace_df_transposed <- as.data.frame(t(spatial_cluster_BayesSpace_df))
spatial_cluster_Banksy_df_transposed <- as.data.frame(t(spatial_cluster_Banksy_df))
k_means_RCTD_df_transposed <- as.data.frame(t(k_means_RCTD_df))
k_means_CARD_df_transposed <- as.data.frame(t(k_means_CARD_df))
```

```{r}
dataframes_list <- list(
  intensity_k_means_PCA_df_transposed,
  intensity_hclust_PCA_df_transposed,
  intensity_louvain_PCA_df_transposed,
  spatial_cluster_Giotto_df_transposed,
  spatial_cluster_BayesSpace_df_transposed,
  spatial_cluster_Banksy_df_transposed,
  k_means_RCTD_df_transposed,
  k_means_CARD_df_transposed
)

names_list <- c(
  "intensity_k_means_PCA",
  "intensity_hclust_PCA",
  "intensity_louvain_PCA",
  "spatial_cluster_Giotto",
  "spatial_cluster_BayesSpace",
  "spatial_cluster_Banksy",
  "k_means_RCTD",
  "k_means_CARD"
)

for (i in 1:length(dataframes_list)) {
  df <- dataframes_list[[i]]
  print(paste("Checking dataframe:", names_list[i]))
  
  for (col_name in names(df)) {
    if (length(table(df[[col_name]])) != 5) {
      print(paste("Algorithm", names_list[i], "in col", col_name, "does not have 5 unique clusters."))
    }
  }
}

```

### ARI

```{r}
ARI_df_clean_transpose_df <- as.data.frame(t(ARI_df_transposed))
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
  filter(BayesSpace > 0.5)
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
  filter(Giotto > 0.8)
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
  filter(RCTD > 0.5)
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
  filter(`k-means` > 0.5)
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
  filter(CARD > 0.6)
rownames(ARI_df_clean_transpose_df) <- gsub("V", "Simulation", rownames(ARI_df_clean_transpose_df))
ARI_df_clean_transpose_df
```

```{r}
ARI_df <- ARI_df_clean_transpose_df %>% 
  rownames_to_column(var = "Simulation")

ARI_df_long <- ARI_df %>%
  pivot_longer(-Simulation, 
               names_to = "Method", 
               values_to = "ARI")

ARI_df_long$Method <- recode(ARI_df_long$Method,
  'k-means' = 'K-means',
  'hclust' = 'Hierarchical',
  'louvain' = 'Louvain',
  'Giotto' = 'HMRF',
  'BayesSpace' = 'BayesSpace',
  'Banksy' = 'BANKSY',
  'RCTD' = 'RTCD',
  'CARD' = 'CARD'
)

ARI_df_long$Category <- case_when(
  ARI_df_long$Method %in% c("K-means", "Hierarchical", "Louvain") ~ "Classical Clustering",
  ARI_df_long$Method %in% c("HMRF", "BayesSpace", "BANKSY") ~ "Spatial Clustering",
  ARI_df_long$Method %in% c("CARD", "RTCD") ~ "Deconvolution + K-means",
  TRUE ~ NA_character_
)

ARI_df_long
```

```{r}
category_colors <- c("Classical Clustering" = "#8DD3C7", "Deconvolution + K-means" = "#FDB462", "Spatial Clustering" = "#FB8072")
ARI_df_long$Method <- factor(ARI_df_long$Method, levels = c("K-means", "Hierarchical", "Louvain", "HMRF", "BayesSpace", "BANKSY","CARD","RTCD"))
ggplot(ARI_df_long, aes(x = Method, y = ARI)) +
  geom_boxplot(aes(fill = Category), outlier.shape = NA) +
  geom_jitter(aes(color = Simulation), width = 0.2) +
  scale_fill_manual(values = category_colors, breaks = c("Classical Clustering", "Spatial Clustering", "Deconvolution + K-means")) + 
  theme_minimal() +
  labs(title = "ARI Comparison") +
  theme(legend.position = "right", axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(legend.position = "right") +
  guides(fill = guide_legend(title = "Method Category")) +
  guides(color = guide_legend(title = "Simulation"))
```

```{r}
outliers_df <- data.frame()

for (method in unique(ARI_df_long$Method)) {
  subset_df <- subset(ARI_df_long, Method == method)
  outlier_subset <- subset(subset_df, 
                           ARI < quantile(subset_df$ARI, 0.25) - 1.5 * IQR(subset_df$ARI) | 
                           ARI > quantile(subset_df$ARI, 0.75) + 1.5 * IQR(subset_df$ARI))
  outliers_df <- rbind(outliers_df, outlier_subset)
}

ggplot(ARI_df_long, aes(x = Method, y = ARI)) +
  geom_boxplot(aes(fill = Category), outlier.shape = NA) +
  geom_point(data = outliers_df, position = position_jitter(width = 0.2), color = "black", size = 1) +
  scale_fill_manual(values = category_colors, breaks = c("Classical Clustering", "Spatial Clustering", "Deconvolution + K-means")) + 
  theme_minimal() +
  labs(title = "ARI Comparison") +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(title = "Method Category")) +
  guides(color = guide_legend(title = "Simulation"))
```

### NMI

```{r}
NMI_df_clean_transposed_df <- as.data.frame(t(NMI_df_transposed))
rownames(NMI_df_clean_transposed_df) <- gsub("V", "Simulation", rownames(NMI_df_clean_transposed_df))
NMI_df_clean_transposed_df
```

```{r}
selected_rows <- rownames(ARI_df_clean_transpose_df)
filtered_NMI_df <- NMI_df_clean_transposed_df[selected_rows, ]
```

```{r}
filtered_NMI_df
```

```{r}
NMI_df <- filtered_NMI_df %>% 
  rownames_to_column(var = "Simulation")

NMI_df_long <- NMI_df %>%
  pivot_longer(-Simulation, 
               names_to = "Method", 
               values_to = "NMI")

NMI_df_long$Method <- recode(NMI_df_long$Method,
  'k-means' = 'K-means',
  'hclust' = 'Hierarchical',
  'louvain' = 'Louvain',
  'Giotto' = 'HMRF',
  'BayesSpace' = 'BayesSpace',
  'Banksy' = 'BANKSY',
  'RCTD' = 'RTCD',
  'CARD' = 'CARD'
)

NMI_df_long$Category <- case_when(
  NMI_df_long$Method %in% c("K-means", "Hierarchical", "Louvain") ~ "Classical Clustering",
  NMI_df_long$Method %in% c("HMRF", "BayesSpace", "BANKSY") ~ "Spatial Clustering",
  NMI_df_long$Method %in% c("CARD", "RTCD") ~ "Deconvolution + K-means",
  TRUE ~ NA_character_
)

NMI_df_long$Method <- factor(NMI_df_long$Method, levels = c("K-means", "Hierarchical", "Louvain", "HMRF", "BayesSpace", "BANKSY","CARD","RTCD"))
```

```{r}
outliers_df <- data.frame()
for (method in unique(NMI_df_long$Method)) {
  subset_df <- subset(NMI_df_long, Method == method)
  outlier_subset <- subset(subset_df, 
                           NMI < quantile(subset_df$NMI, 0.25) - 1.5 * IQR(subset_df$NMI) | 
                           NMI > quantile(subset_df$NMI, 0.75) + 1.5 * IQR(subset_df$NMI))
  outliers_df <- rbind(outliers_df, outlier_subset)
}

ggplot(NMI_df_long, aes(x = Method, y = NMI)) +
  geom_boxplot(aes(fill = Category), outlier.shape = NA) +
  geom_point(data = outliers_df, position = position_jitter(width = 0.2), color = "black", size = 1) +
  scale_fill_manual(values = category_colors, breaks = c("Classical Clustering", "Spatial Clustering", "Deconvolution + K-means")) + 
  theme_minimal() +
  labs(title = "NMI Comparison") +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(title = "Method Category")) +
  guides(color = guide_legend(title = "Simulation"))
```