DLPFC
Last updated: 2024-12-20
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Knit directory: KODAMA-Analysis/
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Introduction
Here, we apply KODAMA to analyze the human dorsolateral prefrontal cortex (DLPFC) data by 10x Visium from Maynard et al., 2021. The links to download the raw data and H&E full resolution images can be found in the LieberInstitute/spatialLIBD github page.
Loading the required libraries
library("nnSVG")
library("scater")
library("scran")
library("scry")
library("SPARK")
library("harmony")
library("Seurat")
library("spatialLIBD")
library("KODAMAextra")
library("mclust")
library("slingshot")
library("irlba")
library("Rnanoflann")
library("ggpubr")Download the dataset
spe <- fetch_data(type = 'spe')Extract the metadata information
n.cores=40
splitting = 100
spatial.resolution = 0.3
aa_noise=3
gene_number=2000
graph = 20
seed=543210
set.seed(seed)
ID=unlist(lapply(strsplit(rownames(colData(spe)),"-"),function(x) x[1]))
samples=colData(spe)$sample_id
rownames(colData(spe))=paste(ID,samples,sep="-")
txtfile=paste(splitting,spatial.resolution,aa_noise,2,gene_number,sep="_")
sample_names=c("151507",
"151508",
"151509",
"151510",
"151669",
"151670",
"151671",
"151672",
"151673",
"151674",
"151675",
"151676")
subject_names= c("Br5292","Br5595", "Br8100")
metaData = SingleCellExperiment::colData(spe)
expr = SingleCellExperiment::counts(spe)
sample_names <- paste0("sample_", unique(colData(spe)$sample_id))
sample_names <- unique(colData(spe)$sample_id)
dim(spe)[1] 33538 47681# identify mitochondrial genes
is_mito <- grepl("(^MT-)|(^mt-)", rowData(spe)$gene_name)
table(is_mito)is_mito
FALSE TRUE
33525 13 # calculate per-spot QC metrics
spe <- addPerCellQC(spe, subsets = list(mito = is_mito))
# select QC thresholds
qc_lib_size <- colData(spe)$sum < 500
qc_detected <- colData(spe)$detected < 250
qc_mito <- colData(spe)$subsets_mito_percent > 30
qc_cell_count <- colData(spe)$cell_count > 12
# spots to discard
discard <- qc_lib_size | qc_detected | qc_mito | qc_cell_count
table(discard)discard
FALSE TRUE
46653 1028 colData(spe)$discard <- discard
# filter low-quality spots
spe <- spe[, !colData(spe)$discard]
dim(spe)[1] 33538 46653spe <- filter_genes(
spe,
filter_genes_ncounts = 2, #ncounts
filter_genes_pcspots = 0.5,
filter_mito = TRUE
)
dim(spe)[1] 6623 46653sel= !is.na(colData(spe)$layer_guess_reordered)
spe = spe[,sel]
dim(spe)[1] 6623 46318spe <- computeLibraryFactors(spe)
spe <- logNormCounts(spe)
subjects=colData(spe)$subject
labels=as.factor(colData(spe)$layer_guess_reordered)
xy=as.matrix(spatialCoords(spe))
samples=colData(spe)$sample_id
cols_cluster <- c("#0000b6", "#81b29a", "#f2cc8f","#e07a5f",
"#cc00b6", "#81ccff", "#33b233")
plot_slide(xy,samples,labels,col=cols_cluster)
png
2 Gene selection
The identification of genes that display spatial expression patterns is performed using the SPARKX method (Zhu et al. (2021)). The genes are ranked based on the median value of the logarithm value of the p-value obtained in each slide individually.
top=multi_SPARKX(spe,n.cores=n.cores)Warning in asMethod(object): sparse->dense coercion: allocating vector of size
2.3 GiBdata=as.matrix(t(logcounts(spe)[top[1:gene_number],]))
samples=colData(spe)$sample_id
labels=as.factor(colData(spe)$layer_guess_reordered)
names(labels)=rownames(colData(spe))
subjects=colData(spe)$subjectPatient Br5595
subject_names="Br5595"
nclusters=5
spe_sub <- spe[, colData(spe)$subject == subject_names]
# subjects=colData(spe_sub)$subject
dim(spe_sub)[1] 6623 14646# spe_sub <- runPCA(spe_sub, 50,subset_row = top[1:gene_number], scale=TRUE)
#pca=reducedDim(spe_sub,type = "PCA")[,1:50]
spe_sub <- spe[, colData(spe)$subject == subject_names]
sel= subjects == subject_names
data_Br5595=data[sel,top[1:gene_number]]
RNA.scaled=scale(data_Br5595)
pca_results <- irlba(A = RNA.scaled, nv = 50)
pca_Br5595 <- pca_results$u %*% diag(pca_results$d)[,1:50]
rownames(pca_Br5595)=rownames(data_Br5595)
colnames(pca_Br5595)=paste("PC",1:50,sep="")
labels=as.factor(colData(spe_sub)$layer_guess_reordered)
names(labels)=rownames(colData(spe_sub))
xy=as.matrix(spatialCoords(spe_sub))
rownames(xy)=rownames(colData(spe_sub))
samples=colData(spe_sub)$sample_id
plot(pca_Br5595, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA analysis
set.seed(seed)
kk=KODAMA.matrix.parallel(pca_Br5595,
spatial = xy,
samples=samples,
FUN= "fastpls" ,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================print("KODAMA finished")[1] "KODAMA finished"config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=cols_cluster[labels])
png
2 Graph-based clustering
# Graph-based clustering
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = 20)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = 2))
plot(kk_UMAP,pch=20,col=cols_cluster[as.factor(clu)])
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
| 374d5f0 | Stefano Cacciatore | 2024-12-14 |
| fd8d092 | Stefano Cacciatore | 2024-10-15 |
| c9d54ee | Stefano Cacciatore | 2024-10-11 |
| 1352d91 | Stefano Cacciatore | 2024-10-10 |
| 6038af1 | Stefano Cacciatore | 2024-10-09 |
| 35ce733 | Stefano Cacciatore | 2024-08-03 |
| f8ca54a | tkcaccia | 2024-07-14 |
plot_slide(xy,as.factor(samples),clu,col=cols_cluster)
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
png
2 FB=names(which.min(table(clu)))
selFB=clu!=FB
# kk_UMAP=kk_UMAP[selFB,]
# labels=labels[selFB]
# samples=samples[selFB]
# xy=xy[selFB,]
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP[selFB,]), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
plot(kk_UMAP[selFB,],pch=20,col=as.factor(clu))
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
| 374d5f0 | Stefano Cacciatore | 2024-12-14 |
| fd8d092 | Stefano Cacciatore | 2024-10-15 |
| 1edc32b | Stefano Cacciatore | 2024-10-11 |
| c9d54ee | Stefano Cacciatore | 2024-10-11 |
| 1352d91 | Stefano Cacciatore | 2024-10-10 |
| 6038af1 | Stefano Cacciatore | 2024-10-09 |
| 35ce733 | Stefano Cacciatore | 2024-08-03 |
| f8ca54a | tkcaccia | 2024-07-14 |
ref=refine_SVM(xy[selFB,],clu,samples[selFB],cost=100)[1] "151669"
[1] "151670"
[1] "151671"
[1] "151672"u=unique(samples[selFB])
for(j in u){
sel=samples[selFB]==j
print(mclust::adjustedRandIndex(labels[selFB][sel],ref[sel]))
}[1] 0.7433529
[1] 0.7503761
[1] 0.8065421
[1] 0.745585 ###########
plot_slide(xy,samples,labels,col=cols_cluster)
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
| 374d5f0 | Stefano Cacciatore | 2024-12-14 |
| fd8d092 | Stefano Cacciatore | 2024-10-15 |
| 1edc32b | Stefano Cacciatore | 2024-10-11 |
| c9d54ee | Stefano Cacciatore | 2024-10-11 |
| 1352d91 | Stefano Cacciatore | 2024-10-10 |
| 35ce733 | Stefano Cacciatore | 2024-08-03 |
| f8ca54a | tkcaccia | 2024-07-14 |
plot_slide(xy[selFB,],samples[selFB],ref,col=cols_cluster)
kk_UMAP_Br5595=kk_UMAP
samples_Br5595=samples
xy_Br5595=xy
labels_Br5595=labels
subject_names_Br5595=subject_names
ref_Br5595=ref
clu_Br5595=clu
save(kk_UMAP_Br5595,samples_Br5595,xy_Br5595,labels_Br5595,subject_names_Br5595,ref_Br5595,clu_Br5595,selFB,file="output/DLFPC-Br5595.RData")
save(data_Br5595,pca_Br5595,samples_Br5595,xy_Br5595,labels_Br5595,subject_names_Br5595,selFB,file="data/DLFPC-Br5595-input.RData")Patient Br5292
subject_names="Br5292"
nclusters=7
spe_sub <- spe[, colData(spe)$subject == subject_names]
dim(spe_sub)[1] 6623 17734# spe_sub <- runPCA(spe_sub, 50,subset_row = top[1:gene_number], scale=TRUE)
#pca=reducedDim(spe_sub,type = "PCA")[,1:50]
spe_sub <- spe[, colData(spe)$subject == subject_names]
sel= subjects == subject_names
data_Br5292=data[sel,top[1:gene_number]]
RNA.scaled=scale(data_Br5292)
pca_results <- irlba(A = RNA.scaled, nv = 50)
pca_Br5292 <- pca_results$u %*% diag(pca_results$d)[,1:50]
rownames(pca_Br5292)=rownames(data_Br5292)
colnames(pca_Br5292)=paste("PC",1:50,sep="")
labels=as.factor(colData(spe_sub)$layer_guess_reordered)
names(labels)=rownames(colData(spe_sub))
xy=as.matrix(spatialCoords(spe_sub))
rownames(xy)=rownames(colData(spe_sub))
samples=colData(spe_sub)$sample_id
plot(pca_Br5292, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA analysis
set.seed(seed)
kk=KODAMA.matrix.parallel(pca_Br5292,
spatial = xy,
samples=samples,
FUN= "fastpls" ,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================ print("KODAMA finished")[1] "KODAMA finished" config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=as.factor(labels))
Graph-based clustering
# Graph-based clustering
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
plot(kk_UMAP,pch=20,col=as.factor(clu))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.4462599
[1] 0.4843513
[1] 0.4496646
[1] 0.4064282 ###########
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.4462599
[1] 0.4843513
[1] 0.4496646
[1] 0.4064282 ###########
plot_slide(xy,samples,labels,col=cols_cluster)
plot_slide(xy,samples,ref,col=cols_cluster)
kk_UMAP_Br5292=kk_UMAP
samples_Br5292=samples
xy_Br5292=xy
labels_Br5292=labels
subject_names_Br5292=subject_names
ref_Br5292=ref
clu_Br5292=clu
save(kk_UMAP_Br5292,pca_Br5292,samples_Br5292,xy_Br5292,subject_names_Br5292,labels_Br5292,ref_Br5292,clu_Br5292,file="output/DLFPC-Br5292.RData")
save(data_Br5292,pca_Br5292,samples_Br5292,xy_Br5292,labels_Br5292,subject_names_Br5292,file="data/DLFPC-Br5292-input.RData")Patient Br8100
subject_names="Br8100"
nclusters=7
spe_sub <- spe[, colData(spe)$subject == subject_names]
dim(spe_sub)[1] 6623 13938# spe_sub <- runPCA(spe_sub, 50,subset_row = top[1:gene_number], scale=TRUE)
#pca=reducedDim(spe_sub,type = "PCA")[,1:50]
spe_sub <- spe[, colData(spe)$subject == subject_names]
sel= subjects == subject_names
data_Br8100=data[sel,top[1:gene_number]]
RNA.scaled=scale(data_Br8100)
pca_results <- irlba(A = RNA.scaled, nv = 50)
pca_Br8100 <- pca_results$u %*% diag(pca_results$d)[,1:50]
rownames(pca_Br8100)=rownames(data_Br8100)
colnames(pca_Br8100)=paste("PC",1:50,sep="")
labels=as.factor(colData(spe_sub)$layer_guess_reordered)
names(labels)=rownames(colData(spe_sub))
xy=as.matrix(spatialCoords(spe_sub))
rownames(xy)=rownames(colData(spe_sub))
samples=colData(spe_sub)$sample_id
plot(pca_Br8100, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA analysis
set.seed(seed)
kk=KODAMA.matrix.parallel(pca_Br8100,
spatial = xy,
samples=samples,
FUN= "fastpls" ,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================ print("KODAMA finished")[1] "KODAMA finished" config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=as.factor(labels))
Graph-based clustering
# Graph-based clustering
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.599265
[1] 0.658843
[1] 0.6486061
[1] 0.5928946 ###########
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
plot(kk_UMAP,pch=20,col=as.factor(clu))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.599265
[1] 0.658843
[1] 0.6486061
[1] 0.5928946 ###########
plot_slide(xy,samples,labels,col=cols_cluster)
plot_slide(xy,samples,ref,col=cols_cluster)
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
kk_UMAP_Br8100=kk_UMAP
samples_Br8100=samples
xy_Br8100=xy
labels_Br8100=labels
subject_names_Br8100=subject_names
ref_Br8100=ref
clu_Br8100=clu
save(kk_UMAP_Br8100,pca_Br8100,samples_Br8100,xy_Br8100,subject_names_Br8100,labels_Br8100,ref_Br8100,clu_Br8100,file="output/DLFPC-Br8100.RData")
save(data_Br8100,pca_Br8100,samples_Br8100,xy_Br8100,labels_Br8100,subject_names_Br8100,file="data/DLFPC-Br8100-input.RData")Saving the results
results_KODAMA <- list()
results_KODAMA$clusters <- list()
results_KODAMA$labels <- list()
results_KODAMA$feature_extraction <- list()
results_KODAMA$xy <- list()
results_KODAMA$clusters=c(results_KODAMA$clusters,tapply(ref_Br5292,samples_Br5292,function(x) x))
results_KODAMA$clusters=c(results_KODAMA$clusters,tapply(ref_Br5595,samples_Br5595[selFB],function(x) x))
results_KODAMA$clusters=c(results_KODAMA$clusters,tapply(ref_Br8100,samples_Br8100,function(x) x))
results_KODAMA$labels=c(results_KODAMA$labels,tapply(labels_Br5292,samples_Br5292,function(x) x))
results_KODAMA$labels=c(results_KODAMA$labels,tapply(labels_Br5595[selFB],samples_Br5595[selFB],function(x) x))
results_KODAMA$labels=c(results_KODAMA$labels,tapply(labels_Br8100,samples_Br8100,function(x) x))
results_KODAMA$feature_extraction=c(results_KODAMA$feature_extraction,by(kk_UMAP_Br5292,samples_Br5292,function(x) x))
results_KODAMA$feature_extraction=c(results_KODAMA$feature_extraction,by(kk_UMAP_Br5595[selFB,],samples_Br5595[selFB],function(x) x))
results_KODAMA$feature_extraction=c(results_KODAMA$feature_extraction,by(kk_UMAP_Br8100,samples_Br8100,function(x) x))
results_KODAMA$xy=c(results_KODAMA$xy,by(xy_Br5292,samples_Br5292,function(x) x))
results_KODAMA$xy=c(results_KODAMA$xy,by(xy_Br5595[selFB,],samples_Br5595[selFB],function(x) x))
results_KODAMA$xy=c(results_KODAMA$xy,by(xy_Br8100,samples_Br8100,function(x) x))
save(results_KODAMA,file="output/KODAMA-results.RData")#seurat list preprocessing
source("code/DLFPC_preprocessing.R")[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 1212 Slides
PCA and HARMONY
dim(spe_sub)[1] 6623 13938 spe <- runPCA(spe, 50,subset_row = top[1:gene_number], scale=TRUE)
subjects=colData(spe)$subject
labels=as.factor(colData(spe)$layer_guess_reordered)
xy=as.matrix(spatialCoords(spe))
samples=colData(spe)$sample_id
spe <- RunHarmony(spe, "subject",lambda=NULL)
pca=reducedDim(spe,type = "HARMONY")[,1:50]
plot(pca, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA
set.seed(seed)
kk=KODAMA.matrix.parallel(pca,
spatial = xy,
samples=samples,
FUN= "fastpls" ,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================print("KODAMA finished")[1] "KODAMA finished"config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=as.factor(labels))
CLUSTER
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = 7))
plot(kk_UMAP,pch=20,col=as.factor(clu)) 
plot_slide(xy,samples,clu,col=cols_cluster)
mito=colData(spe)$subsets_mito_percent
sel_local=(labels=="Layer3" | labels=="Layer4") &
(samples=="151669" | samples=="151670" | samples=="151671" | samples=="151672") &
(clu==1 | clu==6)
boxplot(mito[sel_local]~clu[sel_local],col=cols_cluster[c(1,6)] ,ylab="mito percent")
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
wilcox.test(mito[sel_local]~clu[sel_local])
Wilcoxon rank sum test with continuity correction
data: mito[sel_local] by clu[sel_local]
W = 323194, p-value = 0.01216
alternative hypothesis: true location shift is not equal to 0CLUSTER
sel_rem=which(clu %in% names(sort(table(clu)))[1:2])
kk_UMAP_clear=kk_UMAP[-sel_rem,]
labels_clear=labels[-sel_rem]
samples_clear=samples[-sel_rem]
xy_clear=xy[-sel_rem,]
data_clear=data[-sel_rem,]
subjects_clear=subjects[-sel_rem]
clu=kmeans(kk_UMAP_clear,7,nstart = 100)$cluster
plot(kk_UMAP_clear,col=cols_cluster[labels_clear],pch=20)
svg("output/KODAMA_DLPFC_All_original.svg")
plot(kk_UMAP_clear,col=cols_cluster[labels_clear],pch=20)
dev.off()png
2 plot(kk_UMAP_clear,col=cols_cluster[clu],pch=20)
u=unique(samples)
for(i in 1:length(u)){
sel=samples==u[i]
print(adjustedRandIndex(labels_clear[sel],clu[sel]))
}[1] 0.5538318
[1] 0.5015062
[1] 0.4896804
[1] 0.4760243
[1] 0.3739001
[1] 0.3609056
[1] 0.4066322
[1] 0.4383128
[1] 0.548363
[1] 0.572728
[1] 0.536385
[1] 0.5275014ref=refine_SVM(xy_clear,clu,samples_clear,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510"
[1] "151669"
[1] "151670"
[1] "151671"
[1] "151672"
[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676"names(ref)=rownames(data_clear)
names(labels_clear)=rownames(data_clear)
save(labels_clear,ref,subjects_clear,samples_clear,file="output/DL.RData")
u=unique(samples)
for(i in 1:length(u)){
sel=samples[-sel_rem]==u[i]
print(adjustedRandIndex(labels_clear[sel],ref[sel]))
}[1] 0.5874289
[1] 0.5343093
[1] 0.507712
[1] 0.5028689
[1] 0.4241738
[1] 0.426007
[1] 0.4838957
[1] 0.5635095
[1] 0.5759575
[1] 0.6173067
[1] 0.6233622
[1] 0.5917995plot_slide(xy_clear,samples_clear,ref,col=cols_cluster)
png
2 subclusters=sort(names(sort(table(ref,labels_clear)[,"Layer3"],decreasing = TRUE)[1:3]))
sa_sel=(samples_clear=="151669" | samples_clear=="151670" | samples_clear=="151671" | samples_clear=="151672")
aa=apply(data_clear,2,function(x) wilcox.test(x[ref==subclusters[1] & sa_sel],x[(ref==subclusters[2] ) & sa_sel],alternative = "greater")$p.value) +
apply(data_clear,2,function(x) wilcox.test(x[ref==subclusters[1] & sa_sel],x[(ref==subclusters[3]) & sa_sel],alternative = "greater")$p.value)
sort(aa)[1:10]ENSG00000115756 ENSG00000162545 ENSG00000251562 ENSG00000128989 ENSG00000156508
4.077044e-87 1.297111e-58 1.167145e-45 3.158706e-27 7.428920e-27
ENSG00000170323 ENSG00000173432 ENSG00000162734 ENSG00000174444 ENSG00000144119
8.491823e-24 2.299894e-22 5.756291e-19 2.143882e-17 1.281161e-16 box_sel=(samples_clear=="151669" | samples_clear=="151670" | samples_clear=="151671" | samples_clear=="151672") & (ref==subclusters[1] | ref==subclusters[2] | ref==subclusters[3])
tapply(data_clear[box_sel,names(sort(aa)[1])],as.numeric(as.vector(ref[box_sel])),mean) 1 3 5
0.8993759 0.2901012 0.4536130 df=data.frame(variable=data_clear[box_sel,names(sort(aa)[1])],labels=as.numeric(as.vector(ref[box_sel])))
my_comparisons=list()
my_comparisons[[1]]=c(1,3)
my_comparisons[[2]]=c(1,2)
Nplot1=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=cols_cluster)+
ylab(names(sort(aa)[1]))+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot1
a1=apply(data_clear,2,function(x) wilcox.test(x[ref==subclusters[2] & sa_sel],x[(ref==subclusters[3]) & sa_sel],alternative = "greater")$p.value)
a2=apply(data_clear,2,function(x) wilcox.test(x[ref==subclusters[2] & sa_sel],x[(ref==subclusters[1] ) & sa_sel],alternative = "greater")$p.value)
aa=a1+a2
sort(aa)[1:10]ENSG00000197971 ENSG00000100285 ENSG00000198963 ENSG00000134986 ENSG00000105711
8.272578e-43 7.250085e-27 4.083530e-18 4.807172e-18 1.834065e-17
ENSG00000123560 ENSG00000123416 ENSG00000151552 ENSG00000118785 ENSG00000100362
2.257179e-17 2.991447e-17 6.118685e-17 5.887837e-16 8.522788e-16 a_choice=names(sort(aa)[1])
tapply(data_clear[box_sel,a_choice],as.numeric(as.vector(ref[box_sel])),mean) 1 3 5
1.581691 2.245112 1.917619 df=data.frame(variable=data_clear[box_sel,a_choice],labels=as.numeric(as.vector(ref[box_sel])))
my_comparisons=list()
my_comparisons[[1]]=c(2,3)
my_comparisons[[2]]=c(1,2)
Nplot2=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=cols_cluster)+
ylab(a_choice)+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot2
aa=apply(data_clear,2,function(x) wilcox.test(x[ref==subclusters[3] & sa_sel],x[(ref==subclusters[1] ) & sa_sel],alternative = "greater")$p.value) +
apply(data_clear,2,function(x) wilcox.test(x[ref==subclusters[3] & sa_sel],x[(ref==subclusters[2]) & sa_sel],alternative = "greater")$p.value)
sort(aa)[1:10]ENSG00000120885 ENSG00000154146 ENSG00000128245 ENSG00000162706 ENSG00000205542
4.442257e-09 1.696138e-06 1.515157e-05 7.404897e-04 1.177251e-03
ENSG00000167996 ENSG00000141433 ENSG00000117152 ENSG00000163032 ENSG00000143933
1.414809e-03 1.648107e-03 1.919411e-03 2.398576e-03 2.477883e-03 tapply(data_clear[box_sel,names(sort(aa)[1])],as.numeric(as.vector(ref[box_sel])),mean) 1 3 5
3.288123 3.306209 3.391248 df=data.frame(variable=data_clear[box_sel,names(sort(aa)[1])],labels=as.numeric(as.vector(ref[box_sel])))
my_comparisons=list()
my_comparisons[[1]]=c(2,3)
my_comparisons[[2]]=c(1,3)
Nplot3=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=cols_cluster)+
ylab(names(sort(aa)[1]))+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot3
library(ggalluvial)
sel_sub=subjects_clear=="Br5292"
al=data.frame(expand.grid(list(levels(labels_clear),1:7)),freq=as.numeric(table(labels_clear[sel_sub],ref[sel_sub])))
al$Var2=as.factor(al$Var2)
ggplot(data = al,
aes(axis1 = Var1, axis2 = Var2, y = freq)) +
geom_alluvium(aes(fill = Var2)) +
geom_stratum() +
geom_text(stat = "stratum",
aes(label = after_stat(stratum))) +
theme_void()
sel_sub=subjects_clear=="Br5595"
al=data.frame(expand.grid(list(levels(labels_clear),1:7)),freq=as.numeric(table(labels_clear[sel_sub],ref[sel_sub])))
al$Var2=as.factor(al$Var2)
ggplot(data = al,
aes(axis1 = Var1, axis2 = Var2, y = freq)) +
geom_alluvium(aes(fill = Var2)) +
geom_stratum() +
geom_text(stat = "stratum",
aes(label = after_stat(stratum))) +
theme_void()
sel_sub=subjects_clear=="Br8100"
al=data.frame(expand.grid(list(levels(labels_clear),1:7)),freq=as.numeric(table(labels_clear[sel_sub],ref[sel_sub])))
al$Var2=as.factor(al$Var2)
ggplot(data = al,
aes(axis1 = Var1, axis2 = Var2, y = freq)) +
geom_alluvium(aes(fill = Var2)) +
geom_stratum() +
geom_text(stat = "stratum",
aes(label = after_stat(stratum))) +
theme_void()
library("GSVA")
library("GSA")
library("VAM")
geneset=GSA.read.gmt("../Genesets/msigdb_v2023.2.Hs_GMTs/h.all.v2023.2.Hs.symbols.gmt")12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849501
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49 names(geneset$genesets)=geneset$geneset.names
genesets=geneset$genesets
countdata <- as.matrix(t(logcounts(spe)))
# library("gprofiler2")
# genes=gconvert(rownames(spe),organism="hsapiens",target="GENECARDS",filter_na = F)$target
genes=rowData(spe)[,"gene_name"]
spot_name=colnames(spe)
colnames(countdata)=genes
li=lapply(genesets,function(x) which(genes %in% x))
VAM=vamForCollection(gene.expr=countdata, gene.set.collection=li)
pathway=VAM$distance.sq
pathway_clear=pathway[-sel_rem,]
aa=apply(pathway_clear,2,function(x) wilcox.test(x[ref==subclusters[1] & sa_sel],x[(ref==subclusters[2] ) & sa_sel],alternative = "greater")$p.value) +
apply(pathway_clear,2,function(x) wilcox.test(x[ref==subclusters[1] & sa_sel],x[(ref==subclusters[3]) & sa_sel],alternative = "greater")$p.value)
sort(aa)[1:10] HALLMARK_MYC_TARGETS_V1 HALLMARK_MITOTIC_SPINDLE
1.990192e-46 6.156770e-31
HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_G2M_CHECKPOINT
7.508055e-31 3.118049e-29
HALLMARK_P53_PATHWAY HALLMARK_UV_RESPONSE_DN
4.828974e-28 3.861350e-26
HALLMARK_APICAL_JUNCTION HALLMARK_ALLOGRAFT_REJECTION
4.553265e-25 4.117158e-24
HALLMARK_UNFOLDED_PROTEIN_RESPONSE HALLMARK_ESTROGEN_RESPONSE_EARLY
2.893726e-23 2.658940e-22 box_sel=(samples_clear=="151669" | samples_clear=="151670" | samples_clear=="151671" | samples_clear=="151672") & (ref==subclusters[1] | ref==subclusters[2] | ref==subclusters[3])
tapply(pathway_clear[box_sel,names(sort(aa)[1])],as.numeric(as.vector(ref[box_sel])),mean) 1 3 5
284.1904 261.3498 267.3446 df=data.frame(variable=log(1+pathway_clear[box_sel,names(sort(aa)[1])]),labels=as.numeric(as.vector(ref[box_sel])))
my_comparisons=list()
my_comparisons[[1]]=c(1,3)
my_comparisons[[2]]=c(1,2)
Nplot1=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=cols_cluster)+
ylab(names(sort(aa)[1]))+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot1
a1=apply(pathway_clear,2,function(x) wilcox.test(x[ref==subclusters[2] & sa_sel],x[(ref==subclusters[3]) & sa_sel],alternative = "greater")$p.value)
a2=apply(pathway_clear,2,function(x) wilcox.test(x[ref==subclusters[2] & sa_sel],x[(ref==subclusters[1] ) & sa_sel],alternative = "greater")$p.value)
aa=a1+a2
sort(aa)[1:10] HALLMARK_PANCREAS_BETA_CELLS
0.4485750
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY
0.6973665
HALLMARK_OXIDATIVE_PHOSPHORYLATION
0.9973218
HALLMARK_INTERFERON_ALPHA_RESPONSE
1.2778413
HALLMARK_APICAL_SURFACE
1.6199914
HALLMARK_MYC_TARGETS_V2
1.7282614
HALLMARK_KRAS_SIGNALING_UP
1.8416405
HALLMARK_CHOLESTEROL_HOMEOSTASIS
1.8770509
HALLMARK_WNT_BETA_CATENIN_SIGNALING
1.9169702
HALLMARK_INFLAMMATORY_RESPONSE
1.9761701 a_choice=names(sort(aa)[1])
tapply(pathway_clear[box_sel,a_choice],as.numeric(as.vector(ref[box_sel])),mean) 1 3 5
24.94728 24.15937 24.28457 df=data.frame(variable=pathway_clear[box_sel,a_choice],labels=as.numeric(as.vector(ref[box_sel])))
my_comparisons=list()
my_comparisons[[1]]=c(2,3)
my_comparisons[[2]]=c(1,2)
Nplot2=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=cols_cluster)+
ylab(a_choice)+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot2
aa=apply(pathway_clear,2,function(x) wilcox.test(x[ref==subclusters[3] & sa_sel],x[(ref==subclusters[1] ) & sa_sel],alternative = "greater")$p.value) +
apply(pathway_clear,2,function(x) wilcox.test(x[ref==subclusters[3] & sa_sel],x[(ref==subclusters[2]) & sa_sel],alternative = "greater")$p.value)
sort(aa)[1:10] HALLMARK_OXIDATIVE_PHOSPHORYLATION
0.002678399
HALLMARK_ADIPOGENESIS
0.129896240
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY
0.304488866
HALLMARK_CHOLESTEROL_HOMEOSTASIS
0.845445559
HALLMARK_FATTY_ACID_METABOLISM
0.919218821
HALLMARK_ANGIOGENESIS
0.946597877
HALLMARK_HEDGEHOG_SIGNALING
0.987543573
HALLMARK_MTORC1_SIGNALING
0.994376725
HALLMARK_SPERMATOGENESIS
0.995250421
HALLMARK_PEROXISOME
0.999158738 tapply(pathway_clear[box_sel,names(sort(aa)[1])],as.numeric(as.vector(ref[box_sel])),mean) 1 3 5
397.0898 405.1837 409.4987 df=data.frame(variable=pathway_clear[box_sel,names(sort(aa)[1])],labels=as.numeric(as.vector(ref[box_sel])))
my_comparisons=list()
my_comparisons[[1]]=c(2,3)
my_comparisons[[2]]=c(1,3)
Nplot3=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=cols_cluster)+
ylab(names(sort(aa)[1]))+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot3
TRAJECTORY
d <- slingshot(kk_UMAP_clear, clusterLabels = clu)
trajectory=d@metadata$curves$Lineage1$s
k=Rnanoflann::nn(trajectory,kk_UMAP_clear,1)
map_color=rainbow(nrow(trajectory))[k$indices]
plot(kk_UMAP_clear,pch=20,col=map_color)
plot_slide(xy_clear,samples_clear,k$indices,col=rainbow(nrow(trajectory)))
sessionInfo()R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] VAM_1.1.0 MASS_7.3-61
[3] GSA_1.03.3 GSVA_1.52.3
[5] ggalluvial_0.12.5 ggpubr_0.6.0
[7] Rnanoflann_0.0.3 irlba_2.3.5.1
[9] slingshot_2.12.0 TrajectoryUtils_1.12.0
[11] princurve_2.1.6 mclust_6.1.1
[13] KODAMAextra_1.2 e1071_1.7-16
[15] doParallel_1.0.17 iterators_1.0.14
[17] foreach_1.5.2 KODAMA_4.0
[19] Matrix_1.7-1 umap_0.2.10.0
[21] Rtsne_0.17 minerva_1.5.10
[23] spatialLIBD_1.16.2 SpatialExperiment_1.14.0
[25] Seurat_5.1.0 SeuratObject_5.0.2
[27] sp_2.1-4 harmony_1.2.3
[29] Rcpp_1.0.13-1 SPARK_1.1.1
[31] scry_1.16.0 scran_1.32.0
[33] scater_1.32.1 ggplot2_3.5.1
[35] scuttle_1.14.0 SingleCellExperiment_1.26.0
[37] SummarizedExperiment_1.34.0 Biobase_2.64.0
[39] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[41] IRanges_2.38.1 S4Vectors_0.42.1
[43] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[45] matrixStats_1.4.1 nnSVG_1.8.0
[47] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] GSEABase_1.66.0 goftest_1.2-3
[3] DT_0.33 HDF5Array_1.32.1
[5] Biostrings_2.72.1 vctrs_0.6.5
[7] spatstat.random_3.3-2 digest_0.6.37
[9] png_0.1-8 proxy_0.4-27
[11] git2r_0.33.0 ggrepel_0.9.6
[13] deldir_2.0-4 parallelly_1.41.0
[15] magick_2.8.4 reshape2_1.4.4
[17] httpuv_1.6.15 withr_3.0.1
[19] xfun_0.49 survival_3.8-3
[21] memoise_2.0.1 benchmarkme_1.0.8
[23] ggbeeswarm_0.7.2 zoo_1.8-12
[25] pbapply_1.7-2 Formula_1.2-5
[27] rematch2_2.1.2 KEGGREST_1.44.1
[29] promises_1.3.2 httr_1.4.7
[31] rstatix_0.7.2 restfulr_0.0.15
[33] rhdf5filters_1.16.0 globals_0.16.3
[35] fitdistrplus_1.2-1 rhdf5_2.48.0
[37] ps_1.8.1 rstudioapi_0.17.1
[39] UCSC.utils_1.0.0 miniUI_0.1.1.1
[41] generics_0.1.3 processx_3.8.4
[43] curl_6.0.1 fields_16.3
[45] zlibbioc_1.50.0 ScaledMatrix_1.12.0
[47] polyclip_1.10-7 doSNOW_1.0.20
[49] GenomeInfoDbData_1.2.12 ExperimentHub_2.12.0
[51] SparseArray_1.4.8 golem_0.5.1
[53] xtable_1.8-4 stringr_1.5.1
[55] pracma_2.4.4 evaluate_1.0.1
[57] S4Arrays_1.4.1 BiocFileCache_2.12.0
[59] colorspace_2.1-1 filelock_1.0.3
[61] ROCR_1.0-11 reticulate_1.38.0
[63] spatstat.data_3.1-4 shinyWidgets_0.8.7
[65] magrittr_2.0.3 lmtest_0.9-40
[67] later_1.4.0 viridis_0.6.5
[69] lattice_0.22-6 misc3d_0.9-1
[71] spatstat.geom_3.3-4 future.apply_1.11.3
[73] getPass_0.2-4 scattermore_1.2
[75] XML_3.99-0.17 cowplot_1.1.3
[77] RcppAnnoy_0.0.22 class_7.3-22
[79] pillar_1.9.0 nlme_3.1-166
[81] compiler_4.4.2 beachmat_2.20.0
[83] RSpectra_0.16-2 stringi_1.8.4
[85] tensor_1.5 GenomicAlignments_1.40.0
[87] plyr_1.8.9 crayon_1.5.3
[89] abind_1.4-8 BiocIO_1.14.0
[91] locfit_1.5-9.10 bit_4.5.0.1
[93] dplyr_1.1.4 whisker_0.4.1
[95] codetools_0.2-20 BiocSingular_1.20.0
[97] openssl_2.2.2 bslib_0.8.0
[99] paletteer_1.6.0 plotly_4.10.4
[101] mime_0.12 splines_4.4.2
[103] fastDummies_1.7.4 dbplyr_2.5.0
[105] sparseMatrixStats_1.16.0 attempt_0.3.1
[107] knitr_1.49 blob_1.2.4
[109] utf8_1.2.4 BiocVersion_3.19.1
[111] fs_1.6.5 listenv_0.9.1
[113] DelayedMatrixStats_1.26.0 rdist_0.0.5
[115] ggsignif_0.6.4 tibble_3.2.1
[117] callr_3.7.6 statmod_1.5.0
[119] pkgconfig_2.0.3 tools_4.4.2
[121] BRISC_1.0.6 cachem_1.1.0
[123] RhpcBLASctl_0.23-42 RSQLite_2.3.9
[125] viridisLite_0.4.2 DBI_1.2.3
[127] fastmap_1.2.0 rmarkdown_2.29
[129] scales_1.3.0 grid_4.4.2
[131] ica_1.0-3 Rsamtools_2.20.0
[133] broom_1.0.7 AnnotationHub_3.12.0
[135] sass_0.4.9 patchwork_1.3.0
[137] BiocManager_1.30.25 dotCall64_1.2
[139] graph_1.82.0 carData_3.0-5
[141] RANN_2.6.2 snow_0.4-4
[143] farver_2.1.2 yaml_2.3.10
[145] rtracklayer_1.64.0 cli_3.6.3
[147] purrr_1.0.2 leiden_0.4.3.1
[149] lifecycle_1.0.4 askpass_1.2.1
[151] uwot_0.2.2 backports_1.5.0
[153] bluster_1.14.0 sessioninfo_1.2.2
[155] annotate_1.82.0 BiocParallel_1.38.0
[157] gtable_0.3.5 rjson_0.2.23
[159] ggridges_0.5.6 progressr_0.14.0
[161] limma_3.60.3 jsonlite_1.8.9
[163] edgeR_4.2.2 RcppHNSW_0.6.0
[165] bitops_1.0-9 benchmarkmeData_1.0.4
[167] bit64_4.5.2 spatstat.utils_3.1-1
[169] BiocNeighbors_1.22.0 matlab_1.0.4.1
[171] jquerylib_0.1.4 metapod_1.12.0
[173] config_0.3.2 dqrng_0.4.1
[175] spatstat.univar_3.1-1 lazyeval_0.2.2
[177] shiny_1.10.0 htmltools_0.5.8.1
[179] sctransform_0.4.1 rappdirs_0.3.3
[181] glue_1.8.0 tcltk_4.4.2
[183] spam_2.11-0 XVector_0.44.0
[185] RCurl_1.98-1.16 rprojroot_2.0.4
[187] gridExtra_2.3 igraph_2.0.3
[189] R6_2.5.1 tidyr_1.3.1
[191] labeling_0.4.3 CompQuadForm_1.4.3
[193] cluster_2.1.8 Rhdf5lib_1.26.0
[195] DelayedArray_0.30.1 tidyselect_1.2.1
[197] vipor_0.4.7 maps_3.4.2.1
[199] car_3.1-3 AnnotationDbi_1.66.0
[201] future_1.34.0 rsvd_1.0.5
[203] munsell_0.5.1 KernSmooth_2.23-24
[205] data.table_1.15.4 htmlwidgets_1.6.4
[207] RColorBrewer_1.1-3 rlang_1.1.4
[209] spatstat.sparse_3.1-0 spatstat.explore_3.3-3
[211] fansi_1.0.6 beeswarm_0.4.0