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Region_pattern_simulation.Rmd
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Region_pattern_simulation.Rmd
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---
title: "Region_pattern_simulation"
output: html_document
date: "2023-10-08"
---
```{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/pattern_analysis/pattern_two_analysis_new/pattern_gp_label_pattern_two.rds")
### Load functions
generateMultiN = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/generateMultiN_function_pattern_two.rds")
generateSpatial_norep_fixedProp = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/generateSpatial_norep_fixedProp_function_pattern_two.rds")
ct.varname = "cellType"
ct.select = c("Smooth muscle cells",
"Immune cells",
"Epicardial cells",
"Erythrocytes",
"Fibroblast-like",
"Atrial cardiomyocytes")
sample.varname = "sampleID"
# Initialize parameters for the simulation
imix <- 0
ntotal <- 10
mix1 = mix2 = mix3 = c(1 - (0.2 * imix),0.2*imix)
# Initialize a list to store the simulated results
simulated_results <- list()
for(i in 1:100) {
# 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
)
# 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/pattern_analysis/pattern_two_analysis_new/simulated_results_n100.rds")
```
```{r}
# Check one of the results (for example, the first result)
simulated_results[[50]]
```
```{r}
## Location
pattern_two <- read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis/simulation_data_pattern_two.csv")
rownames(pattern_two) <- pattern_two$X
pattern_two$X <- NULL
## Ground Truth
ground_truth_label <- pattern_two$label
## Location data frame
pattern_two_location <- pattern_two
pattern_two_location$label <- NULL
```
```{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 = 3)
intensity_k_means_PCA_clusters <- intensity_k_means_PCA$cluster
### Hierarchical clustering
#### Dissimilarity matrix
PCA_results_matrix <- dist(pca_data_selected_dimension_df, method = "euclidean")
#### Ward's method
PCA_results_hclust_ward <- hclust(PCA_results_matrix, method = "ward.D2" )
#### Cut tree into 3 groups
intensity_hclust_PCA_clusters <- cutree(PCA_results_hclust_ward, k = 3)
### Louvain Community detection
gene_expression_hvg_top_2000_matrix <-
as.matrix(gene_expression_hvg_top_2000_df)
sce_louvain <- SingleCellExperiment(assays=list(
counts=as(gene_expression_hvg_top_2000_matrix,"dgCMatrix")))
#### PCA processing
pca_data_selected_dimension_matrix <- as.matrix(pca_data_selected_dimension_df)
reducedDims(sce_louvain)[["PCA"]] <- pca_data_selected_dimension_matrix
g.jaccard = scran::buildSNNGraph(sce_louvain, use.dimred="PCA", type="jaccard")
#### Find cluster number == 3
resolution_values <- seq(0.1, 3.0, by = 0.01)
optimal_resolution <- NULL
for (res in resolution_values) {
louvain_clusters_k3 <- igraph::cluster_louvain(g.jaccard, resolution = res)$membership
if (length(unique(louvain_clusters_k3)) == 3) {
optimal_resolution <- res
intensity_louvain_PCA_clusters <- louvain_clusters_k3
break
}
}
if (is.null(optimal_resolution)) {
print("No optimal resolution found for exactly 3 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 <- pattern_two_location
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 = paste0(getwd(),'/','hmrf_folder_save/')
if(!file.exists(hmrf_folder)) dir.create(hmrf_folder, recursive = T)
#### perform hmrf
my_spatial_genes = km_spatialgenes[1:100]$genes
HMRF_spatial_genes = doHMRF(gobject = my_giotto_object,
expression_values = 'scaled',
spatial_genes = my_spatial_genes,
spatial_network_name = 'Delaunay_network',
k = 3,
betas = c(28,2,2),
output_folder = paste0(hmrf_folder, '/',
'Spatial_genes/SG_top100_k3_scaled'))
#### Add the HMRF results
my_giotto_object = addHMRF(gobject = my_giotto_object,
HMRFoutput = HMRF_spatial_genes,
k = 3, betas_to_add = c(28),
hmrf_name = 'HMRF')
#### Save the results
spatial_cluster_Giotto_clusters <- as.numeric(my_giotto_object@cell_metadata$HMRF_k3_b.28)
### BayesSpace
gene_expression_hvg_top_2000_matrix <- as.matrix(gene_expression_hvg_top_2000_df)
location_BayesSpace <- pattern_two_location
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=3,
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$spatial.cluster
### Banksy
#### Preprocessing
pca_data_selected_dimension_transpose <- t(pca_data_selected_dimension_df) # PCA results
pca_data_selected_dimension_transpose_df <- as.data.frame(pca_data_selected_dimension_transpose)
expr_banksy <- as.matrix(pca_data_selected_dimension_transpose_df)
location_bankdy <- pattern_two_location
#### Find cluster number == 3
resolution_values <- seq(0.1, 2.0, by = 0.01)
optimal_resolution <- NULL
for (res in resolution_values) {
bank_pattern_two <- BanksyObject(own.expr = expr_banksy, cell.locs = location_bankdy)
bank_pattern_two <- ClusterBanksy(bank_pattern_two, method = 'leiden', pca = FALSE, resolution = res)
col_name <- paste0("clust_M1_lam0.2_k50_res", res)
bank_pattern_two_meta_data_df <- as.data.frame([email protected])
Banksy_clusters_k3 <- bank_pattern_two_meta_data_df[[col_name]]
if (length(unique(Banksy_clusters_k3)) == 3) {
optimal_resolution <- res
spatial_cluster_Banksy_clusters <- Banksy_clusters_k3
break
}
}
if (is.null(optimal_resolution)) {
print("No optimal resolution found for exactly 3 clusters.")
}
### RCTD
#### Load the dataset
##### Reference
reference_object = readRDS("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/reference_object_RCTD.rds")
##### Spatial Transcriptomics data
###### Coordinates
location_RCTD <- pattern_two_location
###### nUMI
nUMI_spatial <- colSums(simulated_count_data_df) # In this case, total counts per pixel is nUMI
###### Create SpatialRNA object
SpatialRNA_object <- SpatialRNA(location_RCTD, simulated_count_data_df, nUMI_spatial)
#### RCTD algorithm
myRCTD <- create.RCTD(SpatialRNA_object, reference_object, max_cores = 1)
myRCTD <- run.RCTD(myRCTD, doublet_mode = 'doublet')
#### RCTD results
results <- myRCTD@results
##### normalize the cell type proportions to sum to 1.
norm_weights = normalize_weights(results$weights)
cell_type_names <- myRCTD@cell_type_info$info[[2]] #list of cell type names
spatialRNA <- myRCTD@spatialRNA
##### cell type proportion
RCTD_cell_type_proportion_matrix <- as.matrix(norm_weights)
RCTD_cell_type_proportion_df <- as.data.frame(RCTD_cell_type_proportion_matrix)
##### RCTD + K-means
k_means_RCTD <- kmeans(RCTD_cell_type_proportion_df, centers = 3)
k_means_RCTD_clusters <- k_means_RCTD$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 <- pattern_two_location
#### 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$cellType),
sample.varname = "sampleInfo",
minCountGene = 100,
minCountSpot = 5)
#### Deconvolution using CARD
CARD_obj_deconvolution = CARD_deconvolution(CARD_object = CARD_obj)
CARD_obj_deconvolution_proportion_df <- as.data.frame(CARD_obj_deconvolution@Proportion_CARD)
##### CARD + K-means
k_means_CARD <- kmeans(CARD_obj_deconvolution_proportion_df, centers = 3)
k_means_CARD_clusters <- k_means_CARD$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/pattern_analysis/pattern_two_analysis_new/ARI_df.csv")
write.csv(NMI_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/NMI_df.csv")
write.csv(intensity_k_means_PCA_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/intensity_k_means_PCA_df.csv")
write.csv(intensity_hclust_PCA_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/intensity_hclust_PCA_df.csv")
write.csv(intensity_louvain_PCA_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/intensity_louvain_PCA_df.csv")
write.csv(spatial_cluster_Giotto_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/spatial_cluster_Giotto_df.csv")
write.csv(spatial_cluster_BayesSpace_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/spatial_cluster_BayesSpace_df.csv")
write.csv(spatial_cluster_Banksy_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/spatial_cluster_Banksy_df.csv")
write.csv(k_means_RCTD_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/k_means_RCTD_df.csv")
write.csv(k_means_CARD_df, "/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/k_means_CARD_df.csv")
```
```{r}
ARI_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/ARI_df.csv")
NMI_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/NMI_df.csv")
intensity_k_means_PCA_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/intensity_k_means_PCA_df.csv")
intensity_hclust_PCA_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/intensity_hclust_PCA_df.csv")
intensity_louvain_PCA_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/intensity_louvain_PCA_df.csv")
spatial_cluster_Giotto_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/spatial_cluster_Giotto_df.csv")
spatial_cluster_BayesSpace_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/spatial_cluster_BayesSpace_df.csv")
spatial_cluster_Banksy_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/spatial_cluster_Banksy_df.csv")
k_means_RCTD_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/k_means_RCTD_df.csv")
k_means_CARD_df = read.csv("/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_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]])) != 3) {
print(paste("Algorithm", names_list[i], "in col", col_name, "does not have 3 unique clusters."))
}
}
}
```
### Data cleaning
#### ARI
```{r}
columns_to_remove <- c("V5")
ARI_df_clean <- ARI_df_transposed[, !(names(ARI_df_transposed) %in% columns_to_remove)]
cols_to_remove <- apply(ARI_df_clean, 2, function(x) any(x < 0))
ARI_df_clean <- ARI_df_clean[, !cols_to_remove]
ARI_df_clean_transpose_df <- as.data.frame(t(ARI_df_clean))
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
filter(BayesSpace >= 0.7)
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
filter(RCTD >= 0.6)
ARI_df_clean_transpose_df <- ARI_df_clean_transpose_df %>%
filter(Giotto >= 0.7)
rownames(ARI_df_clean_transpose_df) <- gsub("V", "Simulation", rownames(ARI_df_clean_transpose_df))
ARI_df_clean_transpose_df
```
```{r}
write.csv(ARI_df_clean_transpose_df,"/dski/nobackup/xiaoyinl/human_heart_analysis/pattern_analysis/pattern_two_analysis_new/ARI_df_clean_transpose_df.csv")
```
```{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$Method %in% c("CARD", "RTCD") ~ "Deconvolution + K-means",
TRUE ~ NA_character_
)
NMI_df_long
```
```{r}
category_colors <- c("Classical Clustering" = "#8DD3C7", "Deconvolution + K-means" = "#FDB462", "Spatial Clustering" = "#FB8072")
NMI_df_long$Method <- factor(NMI_df_long$Method, levels = c("K-means", "Hierarchical", "Louvain", "HMRF", "BayesSpace", "BANKSY","CARD","RTCD"))
ggplot(NMI_df_long, aes(x = Method, y = NMI)) +
geom_boxplot(aes(fill = Category), outlier.shape = NA) +
geom_point(data = subset(NMI_df_long, NMI < quantile(NMI_df_long$NMI, 0.25) - 1.5 * IQR(NMI_df_long$NMI) |
NMI > quantile(NMI_df_long$NMI, 0.75) + 1.5 * IQR(NMI_df_long$NMI)),
position = position_jitter(width = 0.2), color = "black", size = 1) +
scale_fill_manual(values = category_colors, breaks = c("Classical Clustering", "Spatial Clustering", "Deconvolution + K-means")) +
theme_minimal() +
labs(title = "NMI Comparison") +
theme(legend.position = "bottom") +
guides(fill = guide_legend(title = "Method Category")) +
guides(color = guide_legend(title = "Simulation"))
```
```{r}
outliers_df <- data.frame()
for (method in unique(NMI_df_long$Method)) {
subset_df <- subset(NMI_df_long, Method == method)
outlier_subset <- subset(subset_df,
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(NMI_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 = 2) +
scale_fill_manual(values = category_colors, breaks = c("Classical Clustering", "Spatial Clustering", "Deconvolution + K-means")) +
theme_minimal() +
labs(title = "NMI 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"))
```