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Warning messages:
1: package ‘ensembldb’ was built under R version 4.3.3
2: package ‘GenomeInfoDb’ was built under R version 4.3.3
3: package ‘GenomicFeatures’ was built under R version 4.3.3 library(spatstat.utils)Warning: package 'spatstat.utils' was built under R version 4.3.3library(SeuratObject)Warning: package 'SeuratObject' was built under R version 4.3.3
Loading required package: sp
Attaching package: 'sp'
The following object is masked from 'package:IRanges':
%over%
Attaching package: 'SeuratObject'
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intersect
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intersect
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intersect
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intersect
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intersect
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intersect, tlibrary(harmony)Warning: package 'harmony' was built under R version 4.3.3
Loading required package: Rcpplibrary(Signac)Warning: package 'Signac' was built under R version 4.3.3library(Seurat)Warning: package 'Seurat' was built under R version 4.3.3library(GenomeInfoDb)
library(EnsDb.Mmusculus.v79)
library(dplyr)
Attaching package: 'dplyr'
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filter, select
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intersect, setdiff, setequal, unionlibrary(hdf5r)Warning: package 'hdf5r' was built under R version 4.3.3
Attaching package: 'hdf5r'
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The following object is masked from 'package:S4Vectors':
valuesannotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
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count_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/filtered_peak_bc_matrix.h5"
meta_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/singlecell.csv"
fragment_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/fragments.tsv.gz"import_atac <-function(count_path, meta_path, fragment_path)
{
counts <- Read10X_h5(filename = count_path)
print(fragment_path)
counts_Y <- counts[grep("Y", row.names(counts)),]
counts.keep <- counts[,apply(counts_Y, 2, function(x) all(x==0))]
meta <- read.csv(
file = meta_path,
header = TRUE,
row.names = 1)
chrom_assay <- CreateChromatinAssay(
counts = counts,
sep = c(":", "-"),
genome = 'GRCm38',
fragments = fragment_path,
min.cells = 10,
min.features = 200
)
data <- CreateSeuratObject(
counts = chrom_assay,
assay = "peaks",
meta.data = meta
)
Annotation(data) <- annotations
data <- NucleosomeSignal(object = data)
data <- TSSEnrichment(object = data, fast = FALSE)
data$blacklist_ratio <- data$blacklist_region_fragments / data$peak_region_fragments
data$pct_reads_in_peaks <- data$peak_region_fragments / data$passed_filters * 100
low_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.02)
hig_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.98)
low_prp <- quantile(data[["pct_reads_in_peaks"]]$pct_reads_in_peaks, probs = 0.02)
hig_ns <- quantile(data[["nucleosome_signal"]]$nucleosome_signal, probs = 0.98)
low_ts <- quantile(data[["TSS.enrichment"]]$TSS.enrichment, probs = 0.02)
data <- subset(
x = data,
subset = peak_region_fragments > low_prf &
peak_region_fragments < hig_prf &
pct_reads_in_peaks > low_prp &
nucleosome_signal < hig_ns &
TSS.enrichment > low_ts
)
data<-subset(data,subset=nCount_peaks > 1000 & nCount_peaks<10500)
data<-subset(data,subset=nFeature_peaks >500 & nFeature_peaks<5500)
VlnPlot(data,features=c("nCount_peaks","nFeature_peaks"),ncol=2)
#normalisation
data<- RunTFIDF(data)
data <- FindTopFeatures(data, min.cutoff = 'q0')
data <- RunSVD(data)
data <- RunUMAP(object = data, reduction = 'lsi', dims = 2:30)
data <- FindNeighbors(object = data, reduction = 'lsi', dims = 2:30)
data <- FindClusters(object = data, verbose = FALSE, algorithm = 3)
DimPlot(object = data, label = TRUE) + NoLegend()
return(data)
}#genome 1
count_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/filtered_peak_bc_matrix.h5"
meta_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/singlecell.csv"
fragment_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/fragments.tsv.gz"
allele1=import_atac(count_path,meta_path,fragment_path)[1] "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/fragments.tsv.gz"Computing hash
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Finding + strand cut sites
Finding - strand cut sites
Computing mean insertion frequency in flanking regions
Normalizing TSS score
Performing TF-IDF normalization
Running SVD
Scaling cell embeddings
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
20:25:32 UMAP embedding parameters a = 0.9922 b = 1.112
20:25:32 Read 10060 rows and found 29 numeric columns
20:25:32 Using Annoy for neighbor search, n_neighbors = 30
20:25:32 Building Annoy index with metric = cosine, n_trees = 50
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20:25:35 Writing NN index file to temp file C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\file9918335f3094
20:25:35 Searching Annoy index using 1 thread, search_k = 3000
20:25:43 Annoy recall = 100%
20:25:51 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
20:25:56 Initializing from normalized Laplacian + noise (using RSpectra)
20:25:56 Commencing optimization for 200 epochs, with 366034 positive edges
Using method 'umap'
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20:26:13 Optimization finished
Computing nearest neighbor graph
Computing SNN#genome 2
count_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/filtered_peak_bc_matrix.h5"
meta_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/singlecell.csv"
fragment_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/fragments.tsv.gz"
allele2=import_atac(count_path,meta_path,fragment_path)[1] "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/fragments.tsv.gz"Computing hash
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Done Processing 54 million linesExtracting TSS positions
Finding + strand cut sites
[W::hts_idx_load3] The index file is older than the data file: C:\Users\helper\Documents\yofith\fichiersprojetsbioinfo\genome2\fragments.tsv.gz.tbi
Finding - strand cut sites
[W::hts_idx_load3] The index file is older than the data file: C:\Users\helper\Documents\yofith\fichiersprojetsbioinfo\genome2\fragments.tsv.gz.tbi
Computing mean insertion frequency in flanking regions
Normalizing TSS score
Performing TF-IDF normalization
Running SVD
Scaling cell embeddings
20:36:53 UMAP embedding parameters a = 0.9922 b = 1.112
20:36:54 Read 10018 rows and found 29 numeric columns
20:36:54 Using Annoy for neighbor search, n_neighbors = 30
20:36:54 Building Annoy index with metric = cosine, n_trees = 50
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20:36:56 Writing NN index file to temp file C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\file99181beb2076
20:36:56 Searching Annoy index using 1 thread, search_k = 3000
20:37:03 Annoy recall = 100%
20:37:52 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
20:38:02 Initializing from normalized Laplacian + noise (using RSpectra)
20:38:03 Commencing optimization for 200 epochs, with 358414 positive edges
Using method 'umap'
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20:38:20 Optimization finished
Computing nearest neighbor graph
Computing SNNsave.image("C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/scATAC.RData")
cells.to.keep <- intersect(row.names(allele2@meta.data),
row.names(allele1@meta.data))
cells.to.keep <- intersect(cells.to.keep, row.names(allele2@meta.data))
length(cells.to.keep)[1] 9321allele1 <- subset(allele1,
cells = cells.to.keep)
allele2 <- subset(allele2,
cells = cells.to.keep)
allele1$dataset <- "allele1"
allele2$dataset <- "allele2"
allele1_cells <- rownames(allele1@meta.data)
allele2_cells <- rownames(allele2@meta.data)
cell_names_identical <- identical( sort(allele1_cells),sort(allele2_cells))
unintegrated <- merge(x=allele1, y=allele2, add.cell.ids=c("allele1","allele2"))
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Binding matrix rowsunintegrated <- RunTFIDF(unintegrated)Performing TF-IDF normalizationunintegrated <- FindTopFeatures(unintegrated, min.cutoff = 20)
unintegrated <- RunSVD(unintegrated)Running SVD
Scaling cell embeddingsunintegrated <- RunUMAP(unintegrated, reduction = "lsi", dims = 2:50)21:33:11 UMAP embedding parameters a = 0.9922 b = 1.112
21:33:11 Read 18642 rows and found 49 numeric columns
21:33:11 Using Annoy for neighbor search, n_neighbors = 30
21:33:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:19 Writing NN index file to temp file C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\file991830d310b2
21:33:19 Searching Annoy index using 1 thread, search_k = 3000
21:33:39 Annoy recall = 100%
21:34:11 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
21:34:18 Initializing from normalized Laplacian + noise (using RSpectra)
21:34:19 Commencing optimization for 200 epochs, with 702158 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:06 Optimization finishedp1 <- DimPlot(unintegrated, group.by = 'dataset',pt.size=0.1)
hm.integrated <- RunHarmony(
object = unintegrated,
group.by.vars = 'dataset',
reduction.use = "lsi",
dims.use = 1:30,
reduction.save = "harmony",
project.dim = FALSE,
)Transposing data matrix
Initializing state using k-means centroids initialization
Harmony 1/10
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Harmony 2/10
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Harmony 3/10
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Harmony converged after 3 iterationshm.integrated<- RunUMAP(hm.integrated, dims=1:30,reduction='harmony')21:35:51 UMAP embedding parameters a = 0.9922 b = 1.112
21:35:51 Read 18642 rows and found 30 numeric columns
21:35:51 Using Annoy for neighbor search, n_neighbors = 30
21:35:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:57 Writing NN index file to temp file C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\file99183d6114db
21:35:57 Searching Annoy index using 1 thread, search_k = 3000
21:36:13 Annoy recall = 100%
21:36:16 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
21:36:23 Initializing from normalized Laplacian + noise (using RSpectra)
21:36:24 Commencing optimization for 200 epochs, with 708728 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:10 Optimization finishedhm.integrated <- FindNeighbors(hm.integrated, reduction = "harmony", dims = 1:30)Computing nearest neighbor graph
Computing SNNhm.integrated <- FindClusters(hm.integrated, resolution = 0.5) Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 18642
Number of edges: 388059
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7960
Number of communities: 17
Elapsed time: 5 seconds2 singletons identified. 15 final clusters.p2<-DimPlot(hm.integrated,reduction="umap",group.by='seurat_clusters',label=TRUE,pt.size=0.1)+NoLegend()save(hm.integrated,file="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/hm_integrated.RData")selected_clusters <- c(0, 1, 2, 3)
cells_to_keep <- WhichCells(hm.integrated, idents = selected_clusters)
filtered_hm.integrated <- subset(hm.integrated, cells = cells_to_keep)
save(filtered_hm.integrated, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/filtered_hm_integrated.RData")
p_filtered <- DimPlot(filtered_hm.integrated, reduction = "umap", group.by = 'seurat_clusters', label = TRUE, pt.size = 0.1) + NoLegend()
print(p_filtered)
filtered_allele1<-subset(filtered_hm.integrated, dataset == "allele1")
filtered_allele2<-subset(filtered_hm.integrated, dataset == "allele2")
library(GenomicRanges)
async_master_list_path <- "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/async_master_list.GRCm38.noX[6776].bed"
filtered_allele1_counts <-filtered_allele1[["peaks"]]@counts
peaks <- row.names(filtered_allele1_counts)
# Create a new data frame using lapply for efficiency
peaks.df <- as.data.frame(do.call(rbind, lapply(peaks, function(x) unlist(strsplit(x, "[:-]")))))
# Set column names
colnames(peaks.df) <- c("seqnames", "start", "end")
peaks.gr <- makeGRangesFromDataFrame(peaks.df)
asrt <- read.delim(async_master_list_path, header = F, col.names = c("seqnames", "start", "end", "ASRT"))
asrt$seqnames <- gsub("chr", "", asrt$seqnames)
asrt.gr <- makeGRangesFromDataFrame(asrt)
overlaps <- findOverlaps(peaks.gr, asrt.gr)
peaks.overlap.asrt <- subsetByOverlaps(peaks.gr, asrt.gr, type="any")
peaks.overlap.asrt.names = paste(peaks.overlap.asrt@seqnames, peaks.overlap.asrt@ranges, sep="-")
counts.asrt <- filtered_allele1_counts[row.names(filtered_allele1_counts) %in% peaks.overlap.asrt.names,]
rownames(counts.asrt) <- rownames(counts.asrt)
filtered_allele1 <- subset(filtered_allele1, features = rownames(counts.asrt))
filtered_allele1[["peaks"]]@counts <- counts.asrt
dim(filtered_allele1[["peaks"]]@counts)[1] 12690 7161#normalisation
filtered_allele1<- RunTFIDF(filtered_allele1)Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total countsfiltered_allele1 <- FindTopFeatures(filtered_allele1, min.cutoff = 'q0')
filtered_allele1 <- RunSVD(filtered_allele1) Running SVD
Scaling cell embeddingsfiltered_allele1 <- RunUMAP(filtered_allele1, reduction = "lsi", dims = 1:30)21:45:50 UMAP embedding parameters a = 0.9922 b = 1.112
21:45:50 Read 7161 rows and found 30 numeric columns
21:45:50 Using Annoy for neighbor search, n_neighbors = 30
21:45:50 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:53 Writing NN index file to temp file C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\file99186e8b1714
21:45:53 Searching Annoy index using 1 thread, search_k = 3000
21:45:58 Annoy recall = 100%
21:46:21 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
21:46:26 Initializing from normalized Laplacian + noise (using RSpectra)
21:46:26 Commencing optimization for 500 epochs, with 238618 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:09 Optimization finished
async_master_list_path <- "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/async_master_list.GRCm38.noX[6776].bed"
filtered_allele2_counts <-filtered_allele2[["peaks"]]@counts
peaks <- row.names(filtered_allele2_counts)
peaks.df <- as.data.frame(do.call(rbind, lapply(peaks, function(x) unlist(strsplit(x, "[:-]")))))
# Set column names
colnames(peaks.df) <- c("seqnames", "start", "end")
peaks.gr <- makeGRangesFromDataFrame(peaks.df)
asrt <- read.delim(async_master_list_path, header = F, col.names = c("seqnames", "start", "end", "ASRT"))
asrt$seqnames <- gsub("chr", "", asrt$seqnames)
asrt.gr <- makeGRangesFromDataFrame(asrt)
overlaps <- findOverlaps(peaks.gr, asrt.gr)
peaks.overlap.asrt <- subsetByOverlaps(peaks.gr, asrt.gr, type="any")
peaks.overlap.asrt.names = paste(peaks.overlap.asrt@seqnames, peaks.overlap.asrt@ranges, sep="-")
counts.asrt <- filtered_allele2_counts[row.names(filtered_allele2_counts) %in% peaks.overlap.asrt.names,]
rownames(counts.asrt) <- rownames(counts.asrt)
filtered_allele2 <- subset(filtered_allele2, features = rownames(counts.asrt))
filtered_allele2[["peaks"]]@counts <- counts.asrt
dim(filtered_allele2[["peaks"]]@counts)[1] 12690 7897#normalisation
filtered_allele2<- RunTFIDF(filtered_allele2)Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total countsfiltered_allele2 <- FindTopFeatures(filtered_allele2, min.cutoff = 'q0')
filtered_allele2 <- RunSVD(filtered_allele2) Running SVD
Scaling cell embeddingsfiltered_allele2 <- RunUMAP(filtered_allele2, reduction = "lsi", dims = 1:30)21:49:03 UMAP embedding parameters a = 0.9922 b = 1.112
21:49:03 Read 7897 rows and found 30 numeric columns
21:49:03 Using Annoy for neighbor search, n_neighbors = 30
21:49:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:05 Writing NN index file to temp file C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\file99185ec97246
21:49:05 Searching Annoy index using 1 thread, search_k = 3000
21:49:11 Annoy recall = 100%
21:49:14 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
21:49:20 Initializing from normalized Laplacian + noise (using RSpectra)
21:49:20 Commencing optimization for 500 epochs, with 261906 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:06 Optimization finished# Hierarchical clustering
hc <- hclust(dist(Embeddings(filtered_allele2, "umap")))
clusters <- cutree(hc, k = 2) # Adjust k as needed
# K-means
km <- kmeans(Embeddings(filtered_allele2, "umap"), centers = 2) # Adjust centers as needed
clusters<-km$cluster
filtered_allele2$hierarchical_clusters <- as.factor(clusters)
DimPlot(filtered_allele2, reduction = "umap", group.by = "hierarchical_clusters", label = TRUE, pt.size = 0.5) 
cluster_id <- "hierarchical_clusters"
markers<- FindMarkers(filtered_allele2, ident.1 = 1, ident.2 = 2, test.use = "wilcox", latent.vars = cluster_id)Warning: 'latent.vars' is only used for the following tests: negbinom, poisson, MAST, LR
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 04shead(markers) write.table(markers, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/Markersgenome2.bed",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
significant_markers2<- markers[markers$p_val_adj < 0.05, ]
num_significant_genes2<- nrow(significant_markers2)
print(num_significant_genes2)[1] 142gene_list2 <- rownames(significant_markers2)
coords <- strsplit(row.names(significant_markers1), "-")
bed <- do.call(rbind, lapply(coords, function(x) {
c(paste0("chr", x[1]), as.numeric(x[2]), as.numeric(x[3]))
}))
bed <- as.data.frame(bed, stringsAsFactors = FALSE)
colnames(bed) <- c("chrom", "chromStart", "chromEnd")
bed$score <- significant_markers1$p_val_adj
bed$chromStart <- as.numeric(bed$chromStart)
bed$chromEnd <- as.numeric(bed$chromEnd)
write.table(bed, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/markers_genome1.bed",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
coords <- strsplit(row.names(significant_markers2), "-")
bed <- do.call(rbind, lapply(coords, function(x) {
c(paste0("chr", x[1]), as.numeric(x[2]), as.numeric(x[3]))
}))
bed <- as.data.frame(bed, stringsAsFactors = FALSE)
colnames(bed) <- c("chrom", "chromStart", "chromEnd")
bed$score <- significant_markers2$p_val_adj
bed$chromStart <- as.numeric(bed$chromStart)
bed$chromEnd <- as.numeric(bed$chromEnd)
# Écrire dans un fichier BED
write.table(bed, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/markers_genome2.bed",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)library(ensembldb)
library(rtracklayer)
library(EnsDb.Mmusculus.v79)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ensembldb")'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")' for
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C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\downloaded_packagesBiocManager::install("EnsDb.Mmusculus.v79")'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")' for
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Bioconductor version 3.18 (BiocManager 1.30.25), R 4.3.2 (2023-10-31 ucrt)
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C:\Users\helper\AppData\Local\Temp\Rtmpktlz1U\downloaded_packagesensdb <- EnsDb.Mmusculus.v79
gr <- genes(ensdb)
export(gr, format = "gff3", con = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/EnsDb.Mmusculus.v79.gff3")
library("ChIPseeker")
library("EnsDb.Mmusculus.v79")
txdb <- EnsDb.Mmusculus.v79
library("org.Mm.eg.db")
library("clusterProfiler")
peaks1<- readPeakFile("C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/markers_genome1.bed")
#write.table(peaks, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/markers_peaks.txt",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
seqlevels(peaks1) <- gsub("^chr", "", seqlevels(peaks1))
peakAnno <- annotatePeak(peaks1,tssRegion = c(1000,3000),TxDb=txdb,annoDb="org.Mm.eg.db")>> preparing features information... 2024-08-29 11:11:28
>> identifying nearest features... 2024-08-29 11:11:28
>> calculating distance from peak to TSS... 2024-08-29 11:11:29
>> assigning genomic annotation... 2024-08-29 11:11:29
>> adding gene annotation... 2024-08-29 11:11:32 'select()' returned 1:1 mapping between keys and columns>> assigning chromosome lengths 2024-08-29 11:11:32
>> done... 2024-08-29 11:11:32 plotAnnoPie(peakAnno)
# Extraire les gènes associés
anno_data <- as.data.frame(peakAnno)
# Vérifier les colonnes disponibles
head(anno_data)
# Filtrer les gènes uniques
unique_genes <- unique(anno_data$geneId)
# Compter le nombre total de gènes uniques
total_genes <- length(unique_genes)
total_genes[1] 85#library(biomaRt)
#ensembl <- useMart ("ensembl",dataset=musculus_gene_ensembl)
#gene_info <-getBM(attributes="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/markers_genome1.bed",filters=')
#Accéder aux donnéesdannotation
anno<- peakAnno@anno
gene_names<-anno$SYMBOL
write.table(gene_names, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/allgenesmarker1.txt",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)library("ChIPseeker")
library("EnsDb.Mmusculus.v79")
txdb <- EnsDb.Mmusculus.v79
library("org.Mm.eg.db")
library("clusterProfiler")
peaks2<- readPeakFile("C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/markers_genome2.bed")
#write.table(peaks, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/markers_peaks.txt",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
seqlevels(peaks2) <- gsub("^chr", "", seqlevels(peaks2))
peakAnno <- annotatePeak(peaks2,tssRegion = c(1000,3000),TxDb=txdb,annoDb="org.Mm.eg.db")>> preparing features information... 2024-08-29 10:10:03
>> identifying nearest features... 2024-08-29 10:10:03
>> calculating distance from peak to TSS... 2024-08-29 10:10:03
>> assigning genomic annotation... 2024-08-29 10:10:03
>> adding gene annotation... 2024-08-29 10:10:05 'select()' returned 1:many mapping between keys and columns>> assigning chromosome lengths 2024-08-29 10:10:05
>> done... 2024-08-29 10:10:05 plotAnnoPie(peakAnno)
# Accéder aux données d'annotation
anno<- peakAnno@anno
gene_names<-anno$SYMBOL
write.table(gene_names, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/allgenesmarker2.txt",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)```
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 


```{r}



library(spatstat.utils)
library(SeuratObject)

library(harmony)
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Mmusculus.v79)
library(dplyr)
library(hdf5r)

annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)


```


```{r}



  count_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/filtered_peak_bc_matrix.h5"
  meta_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/singlecell.csv"
  fragment_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/fragments.tsv.gz"
```


```{r}
import_atac <-function(count_path, meta_path, fragment_path) 
  {
    
  

  
  counts <- Read10X_h5(filename = count_path)
  
  print(fragment_path) 
  
  counts_Y <- counts[grep("Y", row.names(counts)),] 
  
  counts.keep <- counts[,apply(counts_Y, 2, function(x) all(x==0))] 
  meta <- read.csv(
    file = meta_path,
    header = TRUE,
    row.names = 1)

  
  
  
  
  
chrom_assay <- CreateChromatinAssay(
  
  counts = counts,
  
  sep = c(":", "-"),
  
  genome = 'GRCm38',
  
  fragments = fragment_path,
  
  min.cells = 10,
  
  min.features = 200
  
)






              

  
  data <- CreateSeuratObject(  
    counts = chrom_assay,
    assay = "peaks",
    meta.data = meta
  )
  
  Annotation(data) <- annotations
  
  
  
  data <- NucleosomeSignal(object = data) 
  data <- TSSEnrichment(object = data, fast = FALSE) 
  
  data$blacklist_ratio <- data$blacklist_region_fragments / data$peak_region_fragments 
  
 

  data$pct_reads_in_peaks <- data$peak_region_fragments / data$passed_filters * 100 
  low_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.02)
  hig_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.98)
  low_prp <- quantile(data[["pct_reads_in_peaks"]]$pct_reads_in_peaks, probs = 0.02)
  hig_ns <- quantile(data[["nucleosome_signal"]]$nucleosome_signal, probs = 0.98)
  
  low_ts <- quantile(data[["TSS.enrichment"]]$TSS.enrichment, probs = 0.02)
  
  data <- subset(
    x = data,
    subset = peak_region_fragments > low_prf &
      peak_region_fragments < hig_prf &
      pct_reads_in_peaks > low_prp &
      nucleosome_signal < hig_ns &
      TSS.enrichment > low_ts
  )
  
  data<-subset(data,subset=nCount_peaks > 1000 & nCount_peaks<10500)
  data<-subset(data,subset=nFeature_peaks >500 & nFeature_peaks<5500)
  VlnPlot(data,features=c("nCount_peaks","nFeature_peaks"),ncol=2)
  #normalisation
  data<- RunTFIDF(data)
  data <- FindTopFeatures(data, min.cutoff = 'q0')
  data <- RunSVD(data) 
  
  
  data <- RunUMAP(object = data, reduction = 'lsi', dims = 2:30)
  data <- FindNeighbors(object = data, reduction = 'lsi', dims = 2:30)
  data <- FindClusters(object = data, verbose = FALSE, algorithm = 3)
  DimPlot(object = data, label = TRUE) + NoLegend()
  
  return(data)
}
```




















```{r}
#genome 1
count_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/filtered_peak_bc_matrix.h5"
meta_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/singlecell.csv"
fragment_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/fragments.tsv.gz"
allele1=import_atac(count_path,meta_path,fragment_path)

#genome 2
count_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/filtered_peak_bc_matrix.h5"
meta_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/singlecell.csv"
fragment_path="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/fragments.tsv.gz"
allele2=import_atac(count_path,meta_path,fragment_path)


save.image("C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/scATAC.RData")



cells.to.keep <- intersect(row.names(allele2@meta.data),
                           row.names(allele1@meta.data))

cells.to.keep <- intersect(cells.to.keep, row.names(allele2@meta.data))

length(cells.to.keep)





allele1 <- subset(allele1,
                  cells = cells.to.keep)

allele2 <- subset(allele2,
                  cells = cells.to.keep)


allele1$dataset <- "allele1"
allele2$dataset <- "allele2"

allele1_cells <- rownames(allele1@meta.data)
allele2_cells <- rownames(allele2@meta.data)
cell_names_identical <- identical( sort(allele1_cells),sort(allele2_cells))


unintegrated <- merge(x=allele1, y=allele2, add.cell.ids=c("allele1","allele2"))
unintegrated <- RunTFIDF(unintegrated)
unintegrated <- FindTopFeatures(unintegrated, min.cutoff = 20)
unintegrated <- RunSVD(unintegrated)
unintegrated <- RunUMAP(unintegrated, reduction = "lsi", dims = 2:50)
p1 <- DimPlot(unintegrated, group.by = 'dataset',pt.size=0.1)


hm.integrated <- RunHarmony(
  object = unintegrated,        
  group.by.vars = 'dataset',     
  reduction.use = "lsi",
  dims.use = 1:30,
  reduction.save = "harmony",
  project.dim = FALSE,
)
hm.integrated<- RunUMAP(hm.integrated, dims=1:30,reduction='harmony')
hm.integrated <- FindNeighbors(hm.integrated, reduction = "harmony", dims = 1:30)
hm.integrated <- FindClusters(hm.integrated, resolution = 0.5)  

p2<-DimPlot(hm.integrated,reduction="umap",group.by='seurat_clusters',label=TRUE,pt.size=0.1)+NoLegend()
```


```{r}
save(hm.integrated,file="C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/hm_integrated.RData")
```




```{r}
selected_clusters <- c(0, 1, 2, 3)
cells_to_keep <- WhichCells(hm.integrated, idents = selected_clusters)

filtered_hm.integrated <- subset(hm.integrated, cells = cells_to_keep)




save(filtered_hm.integrated, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/filtered_hm_integrated.RData")


p_filtered <- DimPlot(filtered_hm.integrated, reduction = "umap", group.by = 'seurat_clusters', label = TRUE, pt.size = 0.1) + NoLegend()
print(p_filtered)

```






```{r}
filtered_allele1<-subset(filtered_hm.integrated, dataset == "allele1")
filtered_allele2<-subset(filtered_hm.integrated, dataset == "allele2")


```


```{r}


library(GenomicRanges)


async_master_list_path <- "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/async_master_list.GRCm38.noX[6776].bed"



filtered_allele1_counts <-filtered_allele1[["peaks"]]@counts
peaks <- row.names(filtered_allele1_counts)
# Create a new data frame using lapply for efficiency
peaks.df <- as.data.frame(do.call(rbind, lapply(peaks, function(x) unlist(strsplit(x, "[:-]")))))
# Set column names
colnames(peaks.df) <- c("seqnames", "start", "end")
peaks.gr <- makeGRangesFromDataFrame(peaks.df)

asrt <- read.delim(async_master_list_path, header = F, col.names = c("seqnames", "start", "end", "ASRT"))
asrt$seqnames <- gsub("chr", "", asrt$seqnames)
asrt.gr <- makeGRangesFromDataFrame(asrt)
overlaps <- findOverlaps(peaks.gr, asrt.gr)
peaks.overlap.asrt <- subsetByOverlaps(peaks.gr, asrt.gr, type="any")

peaks.overlap.asrt.names = paste(peaks.overlap.asrt@seqnames, peaks.overlap.asrt@ranges, sep="-")
counts.asrt <- filtered_allele1_counts[row.names(filtered_allele1_counts) %in% peaks.overlap.asrt.names,]


rownames(counts.asrt) <- rownames(counts.asrt)


filtered_allele1 <- subset(filtered_allele1, features = rownames(counts.asrt))


filtered_allele1[["peaks"]]@counts <- counts.asrt


dim(filtered_allele1[["peaks"]]@counts)
```


```{r}
#normalisation
filtered_allele1<- RunTFIDF(filtered_allele1)
filtered_allele1 <- FindTopFeatures(filtered_allele1, min.cutoff = 'q0')
filtered_allele1 <- RunSVD(filtered_allele1) 
filtered_allele1 <- RunUMAP(filtered_allele1, reduction = "lsi", dims = 1:30)
```


```{r}
# Hierarchical clustering
hc <- hclust(dist(Embeddings(filtered_allele1, "umap")))
clusters <- cutree(hc, k = 2)  # Adjust k as needed

# K-means
km <- kmeans(Embeddings(filtered_allele1, "umap"), centers = 2)  # Adjust centers as needed
clusters<-km$cluster



filtered_allele1$hierarchical_clusters <- as.factor(clusters)


DimPlot(filtered_allele1, reduction = "umap", group.by = "hierarchical_clusters", label = TRUE, pt.size = 0.5) 
  


cluster_id <- "hierarchical_clusters"

markers <- FindMarkers(filtered_allele1, ident.1 = 1, ident.2 = 2, test.use = "wilcox", latent.vars = cluster_id)


head(markers)  



significant_markers1<- markers[markers$p_val_adj < 0.05, ]


num_significant_genes1<- nrow(significant_markers1)


print(num_significant_genes1)


gene_list1 <- rownames(significant_markers1)

```


```{r}





async_master_list_path <- "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/async_master_list.GRCm38.noX[6776].bed"




filtered_allele2_counts <-filtered_allele2[["peaks"]]@counts
peaks <- row.names(filtered_allele2_counts)

peaks.df <- as.data.frame(do.call(rbind, lapply(peaks, function(x) unlist(strsplit(x, "[:-]")))))
# Set column names
colnames(peaks.df) <- c("seqnames", "start", "end")
peaks.gr <- makeGRangesFromDataFrame(peaks.df)

asrt <- read.delim(async_master_list_path, header = F, col.names = c("seqnames", "start", "end", "ASRT"))
asrt$seqnames <- gsub("chr", "", asrt$seqnames)
asrt.gr <- makeGRangesFromDataFrame(asrt)
overlaps <- findOverlaps(peaks.gr, asrt.gr)
peaks.overlap.asrt <- subsetByOverlaps(peaks.gr, asrt.gr, type="any")

peaks.overlap.asrt.names = paste(peaks.overlap.asrt@seqnames, peaks.overlap.asrt@ranges, sep="-")
counts.asrt <- filtered_allele2_counts[row.names(filtered_allele2_counts) %in% peaks.overlap.asrt.names,]


rownames(counts.asrt) <- rownames(counts.asrt)


filtered_allele2 <- subset(filtered_allele2, features = rownames(counts.asrt))


filtered_allele2[["peaks"]]@counts <- counts.asrt


dim(filtered_allele2[["peaks"]]@counts)
```


```{r}
#normalisation
filtered_allele2<- RunTFIDF(filtered_allele2)
filtered_allele2 <- FindTopFeatures(filtered_allele2, min.cutoff = 'q0')
filtered_allele2 <- RunSVD(filtered_allele2) 
filtered_allele2 <- RunUMAP(filtered_allele2, reduction = "lsi", dims = 1:30)




```


```{r}
# Hierarchical clustering
hc <- hclust(dist(Embeddings(filtered_allele2, "umap")))
clusters <- cutree(hc, k = 2)  # Adjust k as needed

# K-means
km <- kmeans(Embeddings(filtered_allele2, "umap"), centers = 2)  # Adjust centers as needed
clusters<-km$cluster



filtered_allele2$hierarchical_clusters <- as.factor(clusters)


DimPlot(filtered_allele2, reduction = "umap", group.by = "hierarchical_clusters", label = TRUE, pt.size = 0.5) 
  


 
cluster_id <- "hierarchical_clusters"


markers<- FindMarkers(filtered_allele2, ident.1 = 1, ident.2 = 2, test.use = "wilcox", latent.vars = cluster_id)


head(markers)  
write.table(markers, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/Markersgenome2.bed",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)

significant_markers2<- markers[markers$p_val_adj < 0.05, ]


num_significant_genes2<- nrow(significant_markers2)


print(num_significant_genes2)


gene_list2 <- rownames(significant_markers2)

```


```{r}

coords <- strsplit(row.names(significant_markers1), "-")
bed <- do.call(rbind, lapply(coords, function(x) {
  c(paste0("chr", x[1]), as.numeric(x[2]), as.numeric(x[3]))
}))


bed <- as.data.frame(bed, stringsAsFactors = FALSE)
colnames(bed) <- c("chrom", "chromStart", "chromEnd")


bed$score <- significant_markers1$p_val_adj


bed$chromStart <- as.numeric(bed$chromStart)
bed$chromEnd <- as.numeric(bed$chromEnd)

write.table(bed, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/markers_genome1.bed",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
```


```{r}

coords <- strsplit(row.names(significant_markers2), "-")
bed <- do.call(rbind, lapply(coords, function(x) {
  c(paste0("chr", x[1]), as.numeric(x[2]), as.numeric(x[3]))
}))


bed <- as.data.frame(bed, stringsAsFactors = FALSE)
colnames(bed) <- c("chrom", "chromStart", "chromEnd")


bed$score <- significant_markers2$p_val_adj


bed$chromStart <- as.numeric(bed$chromStart)
bed$chromEnd <- as.numeric(bed$chromEnd)
# Écrire dans un fichier BED
write.table(bed, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/markers_genome2.bed",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
```


```{r}
library(ensembldb)
library(rtracklayer)

library(EnsDb.Mmusculus.v79)
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ensembldb")
BiocManager::install("AnnotationHub")
BiocManager::install("EnsDb.Mmusculus.v79")



ensdb <- EnsDb.Mmusculus.v79
gr <- genes(ensdb)
export(gr, format = "gff3", con = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/EnsDb.Mmusculus.v79.gff3")



```


```{r}
library("ChIPseeker")
library("EnsDb.Mmusculus.v79")
txdb <- EnsDb.Mmusculus.v79
library("org.Mm.eg.db")
library("clusterProfiler")




peaks1<- readPeakFile("C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome1/markers_genome1.bed")

seqlevels(peaks1) <- gsub("^chr", "", seqlevels(peaks1))


peakAnno <- annotatePeak(peaks1,tssRegion = c(1000,3000),TxDb=txdb,annoDb="org.Mm.eg.db")
plotAnnoPie(peakAnno)




anno<- peakAnno@anno

gene_names<-anno$SYMBOL
write.table(gene_names, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/allgenesmarker1.txt",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
```





```{r}
library("ChIPseeker")
library("EnsDb.Mmusculus.v79")
txdb <- EnsDb.Mmusculus.v79
library("org.Mm.eg.db")
library("clusterProfiler")



peaks2<- readPeakFile("C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/genome2/markers_genome2.bed")

seqlevels(peaks2) <- gsub("^chr", "", seqlevels(peaks2))


peakAnno <- annotatePeak(peaks2,tssRegion = c(1000,3000),TxDb=txdb,annoDb="org.Mm.eg.db")
plotAnnoPie(peakAnno)


anno<- peakAnno@anno

gene_names<-anno$SYMBOL
write.table(gene_names, file = "C:/Users/helper/Documents/yofith/fichiersprojetsbioinfo/allgenesmarker2.txt",sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
```

 



















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