DLPFC
Last updated: 2025-04-13
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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,size.dot = 1)
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],]))
genes=spe@rowRanges@elementMetadata$gene_name
names(genes)=spe@rowRanges@elementMetadata$gene_id
samples=colData(spe)$sample_id
labels=as.factor(colData(spe)$layer_guess_reordered)
names(labels)=rownames(colData(spe))
subjects=colData(spe)$subject
genes=rowData(spe)[,"gene_name"]
names(genes)=rowData(spe)$gene_id
genes_top=genes[top[1:gene_number]]Patient 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)$sample_id
subject_names_Br5595=colData(spe_sub)$subject
plot(pca_Br5595, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA analysis
set.seed(seed)
kk=KODAMA.matrix.parallel(pca_Br5595,
spatial = xy,
samples=samples,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================print("KODAMA finished")[1] "KODAMA finished"config=umap.defaults
config$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,size.dot = 1)
| 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] 0.745585plot_slide(xy,samples,labels,col=cols_cluster,size.dot = 1)
| 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,size.dot = 1)
kk_UMAP_Br5595=kk_UMAP
samples_Br5595=samples
xy_Br5595=xy
labels_Br5595=labels
ref_Br5595=ref
clu_Br5595=clu
save(top,kk_UMAP_Br5595,samples_Br5595,xy_Br5595,labels_Br5595,subject_names_Br5595,ref_Br5595,clu_Br5595,selFB,file="output/DLFPC-Br5595.RData")
save(top,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 17734spe_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)$sample_id
subject_names_Br5292=colData(spe_sub)$subject
plot(pca_Br5292, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA analysis
set.seed(seed)
kk=KODAMA.matrix.parallel(pca_Br5292,
spatial = xy,
samples=samples,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================ print("KODAMA finished")[1] "KODAMA finished" config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=as.factor(labels))
Graph-based clustering
# Graph-based clustering
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
plot(kk_UMAP,pch=20,col=as.factor(clu))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.4462599
[1] 0.4843513
[1] 0.4496646
[1] 0.4064282 g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.4462599
[1] 0.4843513
[1] 0.4496646
[1] 0.4064282plot_slide(xy,samples,labels,col=cols_cluster,size.dot = 1)
plot_slide(xy,samples,ref,col=cols_cluster,size.dot = 1)
kk_UMAP_Br5292=kk_UMAP
samples_Br5292=samples
xy_Br5292=xy
labels_Br5292=labels
ref_Br5292=ref
clu_Br5292=clu
save(top,kk_UMAP_Br5292,pca_Br5292,samples_Br5292,xy_Br5292,subject_names_Br5292,labels_Br5292,ref_Br5292,clu_Br5292,file="output/DLFPC-Br5292.RData")
save(top,data_Br5292,pca_Br5292,samples_Br5292,xy_Br5292,labels_Br5292,subject_names_Br5292,file="data/DLFPC-Br5292-input.RData")Patient Br8100
subject_names="Br8100"
nclusters=7
spe_sub <- spe[, colData(spe)$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)$sample_id
subject_names_Br8100=colData(spe_sub)$subject
plot(pca_Br8100, pch=20,col=as.factor(colData(spe_sub)$sample_id))
KODAMA analysis
set.seed(seed)
kk=KODAMA.matrix.parallel(pca_Br8100,
spatial = xy,
samples=samples,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================ print("KODAMA finished")[1] "KODAMA finished" config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=as.factor(labels))
Graph-based clustering
# Graph-based clustering
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.599265
[1] 0.658843
[1] 0.6486061
[1] 0.5928946 g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = nclusters))
plot(kk_UMAP,pch=20,col=as.factor(clu))
ref=refine_SVM(xy,clu,samples,cost=100)[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676" u=unique(samples)
for(j in u){
sel=samples==j
print(mclust::adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.599265
[1] 0.658843
[1] 0.6486061
[1] 0.5928946 plot_slide(xy,samples,labels,col=cols_cluster,size.dot = 1)
plot_slide(xy,samples,ref,col=cols_cluster,size.dot = 1)
| Version | Author | Date |
|---|---|---|
| 7b2cb8c | Stefano Cacciatore | 2024-12-16 |
kk_UMAP_Br8100=kk_UMAP
samples_Br8100=samples
xy_Br8100=xy
labels_Br8100=labels
ref_Br8100=ref
clu_Br8100=clu
save(top,kk_UMAP_Br8100,pca_Br8100,samples_Br8100,xy_Br8100,subject_names_Br8100,labels_Br8100,ref_Br8100,clu_Br8100,file="output/DLFPC-Br8100.RData")
save(top,data_Br8100,pca_Br8100,samples_Br8100,xy_Br8100,labels_Br8100,subject_names_Br8100,file="data/DLFPC-Br8100-input.RData")Saving the results
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 1212 Slides
PCA and HARMONY
spe <- runPCA(spe, 50,subset_row = top[1:gene_number], scale=TRUE)
subjects=colData(spe)$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)$sample_id))
KODAMA
set.seed(seed)
kk=KODAMA.matrix.parallel(pca,
spatial = xy,
samples=samples,
landmarks = 100000,
splitting = splitting,
ncomp = 50,
spatial.resolution = spatial.resolution,
n.cores=n.cores,
seed = seed)Calculating Network
Calculating Network spatial
socket cluster with 40 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================print("KODAMA finished")[1] "KODAMA finished"config=umap.defaults
config$n_threads = n.cores
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)
plot(kk_UMAP,pch=20,col=as.factor(labels))
df <- data.frame(kk_UMAP[,1:2], tissue=labels,check.names = FALSE)
plot1 = ggplot(df, aes(`Dimension 1`, `Dimension 2`, color = tissue)) +labs(title="KODAMA") +
geom_point(size = 1) +
theme_bw() + theme(legend.position = "bottom",
legend.text = element_text(size = 20),
legend.title = element_text(size = 20),
axis.title = element_text(size = 22), # x and y axis labels
axis.text = element_text(size = 16), # tick labels
plot.title = element_text(size = 26, face = "bold", hjust = 0) )+
scale_color_manual("Domain", values = cols_cluster) +
guides(color = guide_legend(nrow = 1,override.aes = list(size = 10)))
plot1
png
2 CLUSTER
g <- bluster::makeSNNGraph(as.matrix(kk_UMAP), k = graph)
g_walk <- igraph::cluster_walktrap(g)
clu <- as.character(igraph::cut_at(g_walk, no = 7))
plot(kk_UMAP,pch=20,col=cols_cluster[as.factor(clu)]) 
plot_slide(xy,samples,clu,col=cols_cluster,size.dot = 1)
mito=colData(spe)$subsets_mito_percent
sel_local=(labels=="Layer3" | labels=="Layer4") &
(samples=="151669" | samples=="151670" | samples=="151671" | samples=="151672") &
(clu==1 | clu==6)
library(ggpubr)
library(ggplot2)
df=data.frame(mito=mito[sel_local],labels=as.character(clu[sel_local]))
my_comparisons=list(c("1","6"))
Nplot1=ggboxplot(df, x = "labels", y = "mito", width = 0.8,palette = cols_cluster[c(1,6)] ,las=2,
fill="labels",
shape=21)+
ylab("Mitochondrial gene percentage")+
xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")+
theme(legend.position = "none",plot.margin = unit(c(2,1,1,1), "cm"))
Nplot1
png
2 png
2 CLUSTER
We removed the spots with uncertain quality level.
sel_rem=which((clu %in% names(sort(table(clu)))[1:2]) |
rownames(kk_UMAP) %in% rownames(xy_Br5595[!selFB,]))
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_clear=kmeans(kk_UMAP_clear,7,nstart = 100)$cluster
plot(kk_UMAP_clear,col=cols_cluster[labels_clear],pch=20)
png
2 KODAMA plot colored by cluster.
plot(kk_UMAP_clear,col=cols_cluster[clu_clear],pch=20)
u=unique(samples)
for(i in 1:length(u)){
sel=samples==u[i]
print(adjustedRandIndex(labels_clear[sel],clu_clear[sel]))
}[1] 0.5451047
[1] 0.4831456
[1] 0.4813363
[1] 0.4645803
[1] 0.3600907
[1] 0.3385299
[1] 0.4023525
[1] 0.4332784
[1] 0.5425434
[1] 0.5684745
[1] 0.5398805
[1] 0.5276553ref=refine_SVM(xy_clear,clu_clear,samples_clear,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510"
[1] "151669"
[1] "151670"
[1] "151671"
[1] "151672"
[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676"names(ref)=rownames(data_clear)
names(labels_clear)=rownames(data_clear)
u=unique(samples)
for(i in 1:length(u)){
sel=samples[-sel_rem]==u[i]
print(adjustedRandIndex(labels_clear[sel],ref[sel]))
}[1] 0.5757955
[1] 0.5134738
[1] 0.4944026
[1] 0.4885008
[1] 0.4065839
[1] 0.3925779
[1] 0.4671617
[1] 0.5502752
[1] 0.5714412
[1] 0.6139012
[1] 0.6011734
[1] 0.5864472Trajectory color
d <- slingshot(kk_UMAP_clear, clusterLabels = clu_clear)
trajectory=d@metadata$curves$Lineage1$s
k=Rnanoflann::nn(trajectory,kk_UMAP_clear,1)
map_color=rainbow(nrow(trajectory))[k$indices]
plot(kk_UMAP_clear,pch=20,col=map_color)
points(trajectory,pch=21,bg="white",col=2,lwd=2)
plot_slide(xy_clear,samples_clear,k$indices,col=rainbow(nrow(trajectory)),size.dot = 1)
png
2 The cluster number is reorder based on the trajectory
oo=order(tapply(k$indices,ref,mean))
tra=1:7
names(tra)=oo
ref_ordered=tra[as.character(ref)]
plot_slide(xy_clear,samples_clear,ref_ordered,col=cols_cluster,size.dot = 1)
| Version | Author | Date |
|---|---|---|
| 3305d55 | Stefano Cacciatore | 2024-12-20 |
png
2 png
2 png
2 The variable to be used into the deep learning approach
save(ref_ordered,samples_clear,subjects_clear,labels_clear,
file="output/DLFPC-variablesXdeeplearning.RData")library(ggalluvial)
plot_sankey_subject <- function(subject_id, labels_clear, ref_ordered, subjects_clear, cols_cluster) {
sel_sub <- subjects_clear == subject_id
al <- data.frame(
expand.grid(list(levels(labels_clear), 1:7)),
freq = as.numeric(table(labels_clear[sel_sub], ref_ordered[sel_sub]))
)
al$Var2 <- as.factor(al$Var2)
ggplot(data = al, aes(axis1 = Var1, axis2 = Var2, y = freq)) +
geom_alluvium(aes(fill = Var2)) +
geom_stratum() +
scale_fill_manual(values = cols_cluster) +
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
theme_void()
}
Sankey_Br5595 <- plot_sankey_subject("Br5595", labels_clear, ref_ordered, subjects_clear, cols_cluster)
Sankey_Br5595
Sankey_Br5292 <- plot_sankey_subject("Br5292", labels_clear, ref_ordered, subjects_clear, cols_cluster)
Sankey_Br5292
Sankey_Br8100 <- plot_sankey_subject("Br8100", labels_clear, ref_ordered, subjects_clear, cols_cluster)
Sankey_Br8100
png
2 subclusters=sort(names(sort(table(ref_ordered,labels_clear)[,"Layer3"],decreasing = TRUE)[1:3]))
print(subclusters)
sa_sel=(ref_ordered %in% subclusters) & (samples_clear %in% c("151669","151670","151671","151672")) & c(labels_clear %in% "Layer3")
plot_slide(xy_clear[sa_sel,],samples_clear[sa_sel],ref_ordered[sa_sel],col=cols_cluster,size.dot = 1)
s1=rownames(data_clear)[which(ref_ordered==subclusters[1] & sa_sel)]
s2=rownames(data_clear)[which(ref_ordered==subclusters[2] & sa_sel)]
s3=rownames(data_clear)[which(ref_ordered==subclusters[3] & sa_sel)]
data_clear_N=data_clear[c(s1,s2,s3),]
data_clear_N=10*t(t(data_clear_N)/colMaxs(data_clear_N))
colnames(data_clear_N)=genes[colnames(data_clear_N)]
lab_S123 <- factor(rep(c("Group1", "Group2", "Group3"), times = c(length(s1), length(s2), length(s3))),levels=c("Group1","Group2","Group3"))
da123=multi_analysis(data_clear_N[c(s1,s2,s3),],lab_S123,FUN="continuous.test",range="95%CI")
lab_S12 <- factor(rep(c("Group1", "Group2"), times = c(length(s1), length(s2))))
lab_S13 <- factor(rep(c("Group1", "Group3"), times = c(length(s1), length(s3))))
da12=multi_analysis(data_clear_N[c(s1,s2),],lab_S12,FUN="continuous.test",alternative = "greater")
da13=multi_analysis(data_clear_N[c(s1,s3),],lab_S13,FUN="continuous.test",alternative = "greater")
rank=order(-log(1+as.numeric(da12$`p-value`))-log(1+as.numeric(da13$`p-value`)),decreasing = TRUE)[1:50]
df=data.frame(da123[,1],
sublayer1=da123[,2],
sublayer3=da123[,3],
sublayer5=da123[,4],
L1vsL3_pvalue=da12[,4],
L1vsL3_FDR=da12[,5],
L1vsL5_pvalue=da13[,4],
L1vsL5_FDR=da13[,5])[rank,]
write.csv(df,"output/subclusters1.csv")
####
lab_S21 <- factor(rep(c("Group2", "Group1"), times = c(length(s2), length(s1))),levels=c("Group2","Group1"))
lab_S23 <- factor(rep(c("Group2", "Group3"), times = c(length(s2), length(s3))),levels=c("Group2","Group3"))
da21=multi_analysis(data_clear_N[c(s2,s1),],lab_S21,FUN="continuous.test",alternative = "greater",range="95%CI")
da23=multi_analysis(data_clear_N[c(s2,s3),],lab_S23,FUN="continuous.test",alternative = "greater",range="95%CI")
rank=order(-log(1+as.numeric(da21$`p-value`))-log(1+as.numeric(da23$`p-value`)),decreasing = TRUE)[1:50]
df=data.frame(da123[,1],
sublayer1=da123[,2],
sublayer3=da123[,3],
sublayer5=da123[,4],
L3vsL1_pvalue=da21[,4],
L3vsL1_FDR=da21[,5],
L3vsL5_pvalue=da23[,4],
L3vsL5_FDR=da23[,5])[rank,]
write.csv(df,"output/subclusters2.csv")
####
lab_S31 <- factor(rep(c("Group3", "Group1"), times = c(length(s3), length(s1))),levels=c("Group3","Group1"))
lab_S32 <- factor(rep(c("Group3", "Group2"), times = c(length(s3), length(s2))),levels=c("Group3","Group2"))
da31=multi_analysis(data_clear_N[c(s3,s1),],lab_S31,FUN="continuous.test",alternative = "greater")
da32=multi_analysis(data_clear_N[c(s3,s2),],lab_S32,FUN="continuous.test",alternative = "greater")
rank=order(-log(1+as.numeric(da31$`p-value`))-log(1+as.numeric(da32$`p-value`)),decreasing = TRUE)[1:50]
df=data.frame(da123[,1],
sublayer1=da123[,2],
sublayer3=da123[,3],
sublayer5=da123[,4],
L5vsL1_pvalue=da31[,4],
L5vsL1_FDR=da31[,5],
L5vsL3_pvalue=da32[,4],
L5vsL3_FDR=da32[,5])[rank,]
write.csv(df,"output/subclusters3.csv")
sessionInfo()R version 4.4.3 (2025-02-28)
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] patchwork_1.3.0 ggalluvial_0.12.5
[3] ggpubr_0.6.0 Rnanoflann_0.0.3
[5] irlba_2.3.5.1 slingshot_2.12.0
[7] TrajectoryUtils_1.12.0 princurve_2.1.6
[9] mclust_6.1.1 KODAMAextra_1.2
[11] e1071_1.7-16 doParallel_1.0.17
[13] iterators_1.0.14 foreach_1.5.2
[15] KODAMA_3.0 Matrix_1.7-3
[17] umap_0.2.10.0 Rtsne_0.17
[19] minerva_1.5.10 spatialLIBD_1.16.2
[21] SpatialExperiment_1.14.0 Seurat_5.2.1
[23] SeuratObject_5.0.2 sp_2.2-0
[25] harmony_1.2.3 Rcpp_1.0.14
[27] SPARK_1.1.1 scry_1.16.0
[29] scran_1.32.0 scater_1.32.1
[31] ggplot2_3.5.1 scuttle_1.14.0
[33] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[35] Biobase_2.64.0 GenomicRanges_1.56.2
[37] GenomeInfoDb_1.40.1 IRanges_2.38.1
[39] S4Vectors_0.42.1 BiocGenerics_0.50.0
[41] MatrixGenerics_1.16.0 matrixStats_1.5.0
[43] nnSVG_1.8.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] goftest_1.2-3 DT_0.33
[3] Biostrings_2.72.1 vctrs_0.6.5
[5] spatstat.random_3.3-3 digest_0.6.37
[7] png_0.1-8 proxy_0.4-27
[9] git2r_0.33.0 ggrepel_0.9.6
[11] deldir_2.0-4 parallelly_1.43.0
[13] magick_2.8.6 MASS_7.3-65
[15] reshape2_1.4.4 httpuv_1.6.15
[17] withr_3.0.2 xfun_0.51
[19] survival_3.8-3 memoise_2.0.1
[21] benchmarkme_1.0.8 ggbeeswarm_0.7.2
[23] zoo_1.8-13 pbapply_1.7-2
[25] Formula_1.2-5 rematch2_2.1.2
[27] KEGGREST_1.44.1 promises_1.3.2
[29] httr_1.4.7 rstatix_0.7.2
[31] restfulr_0.0.15 globals_0.16.3
[33] fitdistrplus_1.2-2 ps_1.9.0
[35] rstudioapi_0.17.1 UCSC.utils_1.0.0
[37] miniUI_0.1.1.1 generics_0.1.3
[39] processx_3.8.6 curl_6.2.2
[41] fields_16.3.1 zlibbioc_1.50.0
[43] ScaledMatrix_1.12.0 polyclip_1.10-7
[45] doSNOW_1.0.20 GenomeInfoDbData_1.2.12
[47] ExperimentHub_2.12.0 SparseArray_1.4.8
[49] golem_0.5.1 xtable_1.8-4
[51] stringr_1.5.1 pracma_2.4.4
[53] evaluate_1.0.3 S4Arrays_1.4.1
[55] BiocFileCache_2.12.0 colorspace_2.1-1
[57] filelock_1.0.3 ROCR_1.0-11
[59] reticulate_1.42.0 spatstat.data_3.1-6
[61] shinyWidgets_0.9.0 magrittr_2.0.3
[63] lmtest_0.9-40 later_1.4.1
[65] viridis_0.6.5 lattice_0.22-7
[67] misc3d_0.9-1 spatstat.geom_3.3-6
[69] future.apply_1.11.3 getPass_0.2-4
[71] scattermore_1.2 XML_3.99-0.18
[73] cowplot_1.1.3 RcppAnnoy_0.0.22
[75] class_7.3-23 pillar_1.10.1
[77] nlme_3.1-168 compiler_4.4.3
[79] beachmat_2.20.0 RSpectra_0.16-2
[81] stringi_1.8.7 tensor_1.5
[83] GenomicAlignments_1.40.0 plyr_1.8.9
[85] crayon_1.5.3 abind_1.4-8
[87] BiocIO_1.14.0 locfit_1.5-9.12
[89] bit_4.6.0 dplyr_1.1.4
[91] whisker_0.4.1 codetools_0.2-20
[93] BiocSingular_1.20.0 openssl_2.3.2
[95] bslib_0.9.0 paletteer_1.6.0
[97] plotly_4.10.4 mime_0.13
[99] splines_4.4.3 fastDummies_1.7.5
[101] dbplyr_2.5.0 sparseMatrixStats_1.16.0
[103] attempt_0.3.1 knitr_1.50
[105] blob_1.2.4 BiocVersion_3.19.1
[107] fs_1.6.5 listenv_0.9.1
[109] DelayedMatrixStats_1.26.0 rdist_0.0.5
[111] ggsignif_0.6.4 tibble_3.2.1
[113] callr_3.7.6 statmod_1.5.0
[115] pkgconfig_2.0.3 tools_4.4.3
[117] BRISC_1.0.6 cachem_1.1.0
[119] RhpcBLASctl_0.23-42 RSQLite_2.3.9
[121] viridisLite_0.4.2 DBI_1.2.3
[123] fastmap_1.2.0 rmarkdown_2.29
[125] scales_1.3.0 grid_4.4.3
[127] ica_1.0-3 Rsamtools_2.20.0
[129] broom_1.0.8 AnnotationHub_3.12.0
[131] sass_0.4.9 BiocManager_1.30.25
[133] dotCall64_1.2 carData_3.0-5
[135] RANN_2.6.2 snow_0.4-4
[137] farver_2.1.2 yaml_2.3.10
[139] rtracklayer_1.64.0 cli_3.6.4
[141] purrr_1.0.4 lifecycle_1.0.4
[143] askpass_1.2.1 uwot_0.2.3
[145] backports_1.5.0 bluster_1.14.0
[147] sessioninfo_1.2.3 BiocParallel_1.38.0
[149] gtable_0.3.6 rjson_0.2.23
[151] ggridges_0.5.6 progressr_0.15.1
[153] limma_3.60.6 jsonlite_2.0.0
[155] edgeR_4.2.2 RcppHNSW_0.6.0
[157] bitops_1.0-9 benchmarkmeData_1.0.4
[159] bit64_4.6.0-1 spatstat.utils_3.1-3
[161] BiocNeighbors_1.22.0 matlab_1.0.4.1
[163] jquerylib_0.1.4 metapod_1.12.0
[165] config_0.3.2 dqrng_0.4.1
[167] spatstat.univar_3.1-2 lazyeval_0.2.2
[169] shiny_1.10.0 htmltools_0.5.8.1
[171] sctransform_0.4.1 rappdirs_0.3.3
[173] glue_1.8.0 tcltk_4.4.3
[175] spam_2.11-1 XVector_0.44.0
[177] RCurl_1.98-1.17 rprojroot_2.0.4
[179] gridExtra_2.3 igraph_2.1.4
[181] R6_2.6.1 tidyr_1.3.1
[183] labeling_0.4.3 CompQuadForm_1.4.3
[185] cluster_2.1.8.1 DelayedArray_0.30.1
[187] tidyselect_1.2.1 vipor_0.4.7
[189] maps_3.4.2.1 car_3.1-3
[191] AnnotationDbi_1.66.0 future_1.34.0
[193] rsvd_1.0.5 munsell_0.5.1
[195] KernSmooth_2.23-26 data.table_1.17.0
[197] htmlwidgets_1.6.4 RColorBrewer_1.1-3
[199] rlang_1.1.5 spatstat.sparse_3.1-0
[201] spatstat.explore_3.4-2 beeswarm_0.4.0