Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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[1] § Results ↔ Trekker_Spatial_Multiome_Cuttag_analysis.Rmd, lines 34–77 · score 0.90 · Allen Brain Atlas, ATAC seq, TSS enrichment, annotated Seurat, post QC, mouse
[2] § Protocol › Bioinformatics and data analysis ↔ Trekker_Spatial_Multiome_Cuttag_analysis.Rmd, lines 410–447 · score 0.70 · FindTransferAnchors, post QC, ABC, anatomy, PCA, predictions
[3] § Protocol › Bioinformatics and data analysis ↔ Trekker_Spatial_Multiome_Cuttag_analysis.Rmd, lines 347–378 · score 0.70 · FindMultiModalNeighbors, LSI, graph, weighted, modality, embeddings

Paper

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The authors' code

R Markdown · 447 lines · 17 KB · no license · 3 matches

---
title: "10x multiome (cut&tag H3K27ac) + curio trekker data"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
rmdformats::readthedown:
self_contained: true
thumbnails: false
lightbox: false
gallery: false
highlight: tango
code_folding: hide
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
# the 10x hdf5 file contains both data types.
```
```{r}
### load libraries
library(Seurat)
library(ggplot2)
library(googleCloudStorageR)
library(Signac)
library(EnsDb.Mmusculus.v79)
library(BSgenome.Mmusculus.UCSC.mm10)
library(dplyr)
library(scDblFinder)
options(stringsAsFactors = F)
```
# Methods
1. Perform quality control for both gene expression and ATAC data, processed data can be reterieved from GEO: GSE327406
2. Identify doublets
3. Data normalization, dimension reduction and joint clustering
4. Cell type annotation based on mapping to reference single-cell dataset from allen brain atlas
5. Examine spatial clusters
6. The final post QC and annotated Seurat object can be found in Zenodo: 19475899
# QC control {.tabset}
## Distribution of QC metrics
```{r}
inputdata.10x <- Seurat::Read10X_h5("./filtered_feature_bc_matrix.h5")
frag.file <- "./atac_fragments.tsv.gz"
# extract RNA and ATAC data
rna_counts <- inputdata.10x$`Gene Expression`
atac_counts <- inputdata.10x$Peaks
# Create Seurat object
seurat_obj <- CreateSeuratObject(counts = rna_counts)
seurat_obj[["percent.mt"]] <- PercentageFeatureSet(seurat_obj, pattern = "^mt-")
# Now add in the ATAC-seq data
# we'll only use peaks in standard chromosomes
grange.counts <- StringToGRanges(rownames(atac_counts), sep = c(":", "-"))
grange.use <- seqnames(grange.counts) %in% standardChromosomes(grange.counts)
atac_counts <- atac_counts[as.vector(grange.use), ]
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
seqlevelsStyle(annotations) <- 'UCSC'
genome(annotations) <- "mm10"
seqinfo_mouse <- seqinfo(BSgenome.Mmusculus.UCSC.mm10)
chrom_assay <- CreateChromatinAssay(
counts = atac_counts,
sep = c(":", "-"),
genome = seqinfo_mouse,
fragments = frag.file,
min.cells = 0,
annotation = annotations, verbose = FALSE
)
seurat_obj[["ATAC"]] <- chrom_assay
DefaultAssay(seurat_obj) <- "ATAC"
#seurat_obj <- NucleosomeSignal(seurat_obj,verbose = F)
seurat_obj <- TSSEnrichment(seurat_obj,verbose = F)
```
```{r, fig.height=10, fig.width=15}
VlnPlot(seurat_obj, features = c("nCount_ATAC", "nCount_RNA","nFeature_ATAC","nFeature_RNA","percent.mt","TSS.enrichment"), ncol = 3,
log = FALSE, pt.size = 0) + NoLegend()
```
## Distribution of QC metrics log scale
```{r, fig.height=10, fig.width=15}
VlnPlot(seurat_obj, features = c("nCount_ATAC", "nCount_RNA","nFeature_ATAC","nFeature_RNA","percent.mt","TSS.enrichment"), ncol = 3,
log = TRUE, pt.size = 0) + NoLegend()
```
## nCount vs nFeature RNA
```{r}
FeatureScatter(seurat_obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
```
## Density of TSS enrichment
```{r}
DensityScatter(seurat_obj, x = 'nCount_ATAC', y = 'TSS.enrichment', log_x = TRUE, quantiles = TRUE)
```
## nCount_ATAC vs nCount_RNA
```{r}
DensityScatter(seurat_obj, x = 'nCount_ATAC', y = 'nCount_RNA', log_x = TRUE, quantiles = TRUE, log_y = TRUE)
```
```{r}
seurat_obj$qc_status <- ifelse(seurat_obj$nCount_ATAC < 1*10^6 &
seurat_obj$nCount_RNA < 1*10^5 &
seurat_obj$nCount_ATAC > 100 &
seurat_obj$nCount_RNA > 1000 &
seurat_obj$nFeature_RNA > 200 &
seurat_obj$nFeature_RNA < 10284 &
seurat_obj$TSS.enrichment > 2, "pass","fail")
```
`r sum(seurat_obj$qc_status == "fail")` out of `r ncol(seurat_obj)` are predicted to be low quality cells
# {-}
# Doublet prediction based on GEX
## Doublet prediction
```{r}
DefaultAssay(seurat_obj) <- "RNA"
seurat_obj <- seurat_obj %>% NormalizeData(verbose = F) %>% FindVariableFeatures(verbose = F)
mt_genes <- grep("^mt-", VariableFeatures(seurat_obj), value = TRUE, ignore.case = TRUE)
ribo_genes <- grep("^Rp[sl]", VariableFeatures(seurat_obj), value = TRUE, ignore.case = TRUE)
VariableFeatures(seurat_obj) <- setdiff(VariableFeatures(seurat_obj), c(mt_genes,ribo_genes))
seurat_obj <- seurat_obj %>% ScaleData(verbose = F) %>% RunPCA(verbose = F)
seurat_obj <- FindNeighbors(object = seurat_obj, assay = "RNA", reduction = "pca", dims = 1:50, verbose = FALSE)
seurat_obj <- FindClusters(object = seurat_obj, algorithm = 1, verbose = FALSE, cluster.name = "seurat_clusters")
seurat_obj <- RunUMAP(seurat_obj, reduction = "pca", dims = 1:50, verbose = FALSE)
```
```{r}
set.seed(1234)
sce <- scDblFinder(GetAssayData(seurat_obj, assay = "RNA",layer = "count"), clusters=seurat_obj$seurat_clusters, verbose = F, dbr.sd = 1)
seurat_obj[["scDblFinder.score"]] <- sce$scDblFinder.score
seurat_obj[["scDblFinder.class"]] <- factor(ifelse(seurat_obj[["scDblFinder.score"]] >= 0.6, "doublet", "singlet"),levels = c("doublet","singlet"))
gp <- ggplot() + geom_point(aes(x = nCount_RNA, y = nFeature_RNA,shape = scDblFinder.class), data = [email hidden]) + theme_classic() + scale_shape_manual(values = c(1, 4)) + facet_wrap(~scDblFinder.class, ncol = 2)
gp
```
`r sum(seurat_obj$scDblFinder.class == "doublet")` out of `r ncol(seurat_obj)` are predicted to be doublets
## Pct of doublet cells by cluster
```{r, fig.width=15, fig.height=10}
# Summarize pct doublets per cluster
doublet_summary <- [email hidden] %>%
group_by(cluster = seurat_clusters) %>% # or Idents(seuratO)
summarize(
total_cells = n(),
doublet_cells = sum(scDblFinder.class=="doublet", na.rm = TRUE),
pct_doublet = 100 * doublet_cells / total_cells
)
# Bar plot
ggplot(doublet_summary, aes(x = cluster, y = pct_doublet, fill = cluster)) +
geom_bar(stat = "identity", color = "black") +
geom_text(aes(label = paste0(round(pct_doublet,1), "%")),
vjust = -0.5, size = 4) +
labs(title = "Percentage of Doublet Cells per Cluster",
x = "Cluster", y = "Doublets (%)") +
theme_classic() +
theme(legend.position = "none",
axis.text = element_text(size=12),
axis.title = element_text(size=14),
plot.title = element_text(size=16, face="bold", hjust=0.5))
```
## nFeature_RNA by cluster
```{r}
VlnPlot(seurat_obj, features = "nFeature_RNA", group.by = "seurat_clusters")
```
```{r}
VlnPlot(seurat_obj, features = "scDblFinder.score", group.by = "seurat_clusters")
```
# {-}
# Doublet prediction based on Cut&Tag
## Doublet prediction
```{r}
otherChroms <- GRanges(c("M","chrM","MT","X","Y","chrX","chrY"),IRanges(1L,width=10^8))
res <- amulet(frag.file, regionsToExclude=otherChroms,minFrags=100,verbose=FALSE)
seurat_obj$amulet_score <- res$q.value[match(colnames(seurat_obj),rownames(res))]
```
```{r}
seurat_obj$amulet_class <- factor(ifelse(is.na(seurat_obj$amulet_score) | seurat_obj$amulet_score > 0.01, 'singlet', 'doublet'),
levels = c('doublet', 'singlet')
)
gp <- ggplot() + geom_point(aes(x = nCount_ATAC, y = nFeature_ATAC,shape = amulet_class), data = [email hidden]) + theme_classic() + scale_shape_manual(values = c(1, 4)) + facet_wrap(~amulet_class, ncol = 2)
gp
```
## Pct of doublet cells by cluster
```{r, fig.width=15, fig.height=10}
# Summarize pct doublets per cluster
doublet_summary <- [email hidden] %>%
group_by(cluster = seurat_clusters) %>% # or Idents(seuratO)
summarize(
total_cells = n(),
doublet_cells = sum(amulet_class=="doublet", na.rm = TRUE),
pct_doublet = 100 * doublet_cells / total_cells
)
# Bar plot
ggplot(doublet_summary, aes(x = cluster, y = pct_doublet, fill = cluster)) +
geom_bar(stat = "identity", color = "black") +
geom_text(aes(label = paste0(round(pct_doublet,1), "%")),
vjust = -0.5, size = 4) +
labs(title = "Percentage of Doublet Cells per Cluster",
x = "Cluster", y = "Doublets (%)") +
theme_classic() +
theme(legend.position = "none",
axis.text = element_text(size=12),
axis.title = element_text(size=14),
plot.title = element_text(size=16, face="bold", hjust=0.5))
```
## Comparing with GEX prediction
```{r}
# Create contingency table
df_tab <- [email hidden] %>%
select(scDblFinder.class, amulet_class) %>%
table() %>%
as.data.frame()
# Rename columns
colnames(df_tab) <- c("scDblFinder", "AMULET", "Count")
# Heatmap
ggplot(df_tab, aes(x = scDblFinder, y = AMULET, fill = Count)) +
geom_tile(color = "white") +
geom_text(aes(label = Count), color = "black", size = 4) +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(title = "Comparison of Doublet Predictions", x = "scDblFinder", y = "AMULET") +
theme_minimal(base_size = 14)
```
# {-}
# UMAP by QC statur and combined doublet status
```{r, fig.width=15, fig.height=10}
seurat_obj$doublet_status_combined <- ifelse((seurat_obj$scDblFinder.class == 'doublet' | (seurat_obj$scDblFinder.score >= 0.3 & seurat_obj$amulet_class == "doublet") | (seurat_obj$seurat_clusters %in% c(0,32))), 'doublet', 'singlet')
DimPlot(seurat_obj, reduction = "umap", group.by = c("qc_status","doublet_status_combined"))
```
# QC metrics vs # of spatial location {.tabset}
## Distribution of QC metrics by spatial location
```{r}
cell_metrics <- read.delim("./coords_trekker_gex_cuttag.txt", header = TRUE, stringsAsFactors = F, sep = " ")
seurat_obj$number_clusters <- cell_metrics$number_clusters[match(colnames(seurat_obj),paste0(cell_metrics$cell_bc, "-1"))]
seurat_obj$number_clusters_class <- sapply(seurat_obj$number_clusters, function(i){
if(i < 4) i else ">=4"
})
seurat_obj$number_clusters_class <- factor(seurat_obj$number_clusters_class, levels = c("0","1","2","3",">=4"))
```
```{r, fig.height=10, fig.width=10}
VlnPlot(seurat_obj, features = c("nCount_ATAC", "nCount_RNA","nFeature_ATAC","nFeature_RNA","percent.mt", "TSS.enrichment"), ncol = 3, pt.size = 0,group.by = "number_clusters_class") & theme(legend.position = "right")
```
```{r}
kableExtra::kable(unclass(table(seurat_obj$number_clusters_class)))
```
## nCount by Spatial location
```{r}
ggplot(data = [email hidden]) + geom_point(aes(x= nCount_ATAC, y = nCount_RNA, color = number_clusters_class)) + theme_classic() + scale_x_log10() + scale_y_log10()
```
## Doublet status vs spatial location
```{r}
# Compute proportions
df_summary <- [email hidden] %>%
group_by(doublet_status_combined, number_clusters_class) %>%
summarise(count = n(), .groups = "drop") %>%
group_by(doublet_status_combined) %>%
mutate(prop = count / sum(count))
# Plot: stacked bar showing proportions
ggplot(df_summary, aes(x = doublet_status_combined, y = prop, fill = number_clusters_class)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
labs(x = "Doublet Status", y = "Proportion", fill = "Number of spatial locations") +
theme_minimal()
```
## SB_top_cluster vs spatial location
```{r}
cell_metrics$number_clusters_class <- sapply(cell_metrics$number_clusters, function(i){
if(i < 4) i else ">=4"
})
cell_metrics$number_clusters_class <- factor(cell_metrics$number_clusters_class, levels = c("0","1","2","3",">=4"))
ggplot() + geom_boxplot(aes(x = number_clusters_class, y = SB_top_cluster), data=cell_metrics)
```
SB_top_cluster: Number of spatial barcodes in the top cluster (cluster == 1).
## SB_UMI_top_cluster vs spatial location
```{r}
ggplot() + geom_boxplot(aes(x = number_clusters_class, y = SB_UMI_top_cluster), data=cell_metrics)
```
SB_UMI_top_cluster: Total UMI counts summed over all spatial barcodes for that nucleus
## proportion_SB_top_cluster vs spatial location
```{r}
ggplot() + geom_boxplot(aes(x = number_clusters_class, y = proportion_SB_top_cluster), data=cell_metrics)
```
proportion_SB_top_cluster: Fraction of SBs in the top cluster: SB_top_cluster / SB_total
## proportion_SB_UMI_top_cluster
```{r}
ggplot() + geom_boxplot(aes(x = number_clusters_class, y = proportion_SB_UMI_top_cluster), data=cell_metrics)
```
proportion_SB_UMI_top_cluster: Fraction of UMIs in the top cluster: SB_UMI_top_cluster / SB_UMI_total.
# {-}
# Unsupervised clustering {.tabset}
## UMAP post filter
```{r}
seurat_obj <- subset(seurat_obj, qc_status == "pass" & doublet_status_combined == "singlet")
DefaultAssay(seurat_obj) <- "RNA"
seurat_obj <- SCTransform(seurat_obj, verbose = FALSE, vars.to.regress = 'percent.mt')
mt_genes <- grep("^mt-",VariableFeatures(seurat_obj), value = T, ignore.case = T)
ribo_genes <- grep("^Rp[sl]", VariableFeatures(seurat_obj), value = TRUE, ignore.case = TRUE)
VariableFeatures(seurat_obj) <- setdiff(VariableFeatures(seurat_obj), c(mt_genes,ribo_genes))
seurat_obj <- seurat_obj %>% RunPCA(verbose = F)
sapply(1:30, function(i) cor(seurat_obj@reductions$[email hidden][,i],seurat_obj$nCount_RNA, method = "s"))
# do not use PC1 as it's highly correlated to nCount_RNA
seurat_obj <- seurat_obj %>% RunUMAP(dims = 2:50, reduction.name = 'umap.rna', reduction.key = 'rnaUMAP_',verbose = F)
DefaultAssay(seurat_obj) <- "ATAC"
seurat_obj <- FindTopFeatures(seurat_obj, min.cutoff = "q5",verbose = F)
seurat_obj <- RunTFIDF(seurat_obj,verbose = F)
seurat_obj <- RunSVD(seurat_obj,verbose = F)
seurat_obj <- RunUMAP(seurat_obj, reduction = 'lsi', dims = 2:50, reduction.name = "umap.atac", reduction.key = "atacUMAP_", verbose = F)
seurat_obj <- FindMultiModalNeighbors(seurat_obj, reduction.list = list("pca", "lsi"), dims.list = list(2:50, 2:50),modality.weight.name = "RNA.weight",verbose = F)
seurat_obj <- RunUMAP(seurat_obj, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_", verbose = F)
seurat_obj <- FindClusters(seurat_obj, graph.name = "wsnn", algorithm = 3, verbose = FALSE, resolution = 0.3)
```
```{r, fig.width=12, fig.height=8}
p1 <- DimPlot(seurat_obj, reduction = "umap.rna", group.by = "seurat_clusters", label = TRUE, label.size = 5, repel = TRUE) + ggtitle("RNA")
p2 <- DimPlot(seurat_obj, reduction = "umap.atac", group.by = "seurat_clusters", label = TRUE, label.size = 5, repel = TRUE) + ggtitle("ATAC")
p3 <- DimPlot(seurat_obj, reduction = "wnn.umap", group.by = "seurat_clusters", label = TRUE, label.size = 5, repel = TRUE) + ggtitle("WNN")
p1 + p2 + p3 & NoLegend() & theme(plot.title = element_text(hjust = 0.5))
```
## Spatial plot
```{r,fig.width=10, fig.height=6}
coord_mat <- cell_metrics[match(colnames(seurat_obj),paste0(cell_metrics$cell_bc,"-1")),c("x_um","y_um")]
rownames(coord_mat) <- colnames(seurat_obj)
colnames(coord_mat) <- c("SPATIAL_1","SPATIAL_2")
seurat_obj[["spatial"]] <- CreateDimReducObject(embeddings = as.matrix(coord_mat), key = "SPATIAL_", assay = "RNA")
seurat_obj_sel <- subset(seurat_obj, number_clusters >0)
p4 <- DimPlot(seurat_obj_sel, reduction = "spatial", group.by = "seurat_clusters")
p3 + p4 & NoLegend() & theme(plot.title = element_text(hjust = 0.5))
```
All nuclei from single-nuclei pipeline analysis (including those without spatial locations) were included in the UMAP. All positioned nuclei were included in the spatial plot (including those with 2 or more spatial locations).
Total number of cells post QC and positioned spatially: `r ncol(seurat_obj_sel)`
## Spatial plot by # of spatial locations
```{r, fig.width=6,fig.height=6}
gp <- DimPlot(seurat_obj_sel, reduction = "spatial", group.by = "number_clusters_class")
plotly::ggplotly(gp)
```
# {-}
# Cell type annotation
```{r}
DefaultAssay(seurat_obj) <- "RNA"
integrated_obj <- readRDS("./abc_integrated.rds")
for(i in names(integrated_obj@assays$RNA@layers)) {
inputs <- all_matrix_inputs(integrated_obj@assays$RNA@layers[[i]])
inputs[[1]]@dir <- "./on_disk_mat/"
all_matrix_inputs(integrated_obj@assays$RNA@layers[[i]]) <- inputs
}
seurat_obj <- seurat_obj %>% NormalizeData(verbose = F) %>% ScaleData(verbose = F) %>% FindVariableFeatures(verbose = F)
DefaultAssay(integrated_obj) <- "RNA"
anchor <- FindTransferAnchors(
reference = integrated_obj,
query = seurat_obj,
reduction = "pcaproject",
reference.reduction = "pca", verbose = FALSE)
refdata <- as.list(c("cell_type_class","anatomical_division_label"))
names(refdata) <- c("cell_type_class","anatomical_division_label")
seurat_obj <- TransferData(
anchorset = anchor,
reference = integrated_obj,
query = seurat_obj,
refdata = refdata,
prediction.assay = TRUE)
## Remove cell types that are not expected to be present based on anatomy of the tissue
seurat_obj <- subset(seurat_obj, cells = colnames(seurat_obj)[!seurat_obj$predicted.cell_type_class %in% c("15 HY Gnrh1 Glut", "16 HY MM Glut","21 MB Dopa","22 MB-HB Sero", "23 P Glut", "25 Pineal Glut","26 P GABA", "27 MY GABA","28 CB GABA","29 CB Glut","32 OEC")])
tmp <- unique(seurat_obj$predicted.cell_type_class)[order(as.integer(gsub(" .*$","",unique(seurat_obj$predicted.cell_type_class))))]
seurat_obj$cell_type_class <- factor(seurat_obj$cell_type_class, levels = gsub("[0-9]+ ","",tmp))
saveRDS(seurat_obj, "./seurat_obj_postQC.rds")
```

Trekker_Spatial_Multiome_Cuttag_analysis.Rmd at commit 696d29d, no license · at the source

Overview

Authors: Ting Zhang^1,2, Fatemeh S. Farassati^1,2, Alejandro Stark-Quiroz^1,2, Yuanhang Liu³, Nick M. Huynh^1,2, Yujiro Hayashi^1,2, Tamas Ordog^1,2, Jeong-Heon Lee^1,2,4

ORCID iDs: Nick M. Huynh, Yujiro Hayashi

Enteric Neuroscience Program and Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science
Gastroenterology Research Unit, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic College of Medicine and Science
Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science
Division of Experimental Pathology and Laboratory Medicine, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine and Science

Institutions: Mayo Clinic (United States)

Journal: Journal of visualized experiments : JoVE, issue 232, article 10.3791/71046

Dates: published online 12 June 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.3791/71046 · PMID 42371963 · PMCID PMC13450693 · OpenAlex W7164661319

Open access: green, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), cellular / molecular (subfield)

Methods: Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging

MeSH: Epigenomics*, Frozen Sections*, Gene Expression Profiling*, Single-Cell Analysis*, Transcriptome*, Animals, Brain, Multiomics, Single-Cell Gene Expression Analysis, Spatial Transcriptomics (* major topic)

Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: NIDDK NIH HHS (R01 DK126827, P30 DK084567, R01 DK131455, R01 DK121766, R01 DK142826)

Citations: not cited yet (Europe PMC); 27 references in the paper

Research resources: Anti-H3K27ac antibody RRID:AB_3712075, Seurat RRID:SCR_016341, Signac RRID:SCR_021158, scDblFinder RRID:SCR_022700, Cell Ranger Arc v2.0.2 RRID:SCR_023897, ABC atlas RRID:SCR_024440, 10X Genomics Chromium X instrument RRID:SCR_024537, KEYENCE Fluorescence Microscope RRID:SCR_025160, Nexcelom Cellometer K2 RRID:SCR_025529

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Liuy12/Mouse_Brain_Trekker_Multiome_Cuttag

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 696d29dbc73b97b51beba4db91152f0c25dcc4ef, 8 April 2026

Languages: R (1)

Size: 2 files, 1 script

Software Heritage: not archived

Found in: the text, “Bioinformatics and data analysis”

Holds: README, 1 notebook

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: ggplot2 (1 file), Plotly (1 file), Seurat (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

2 files

Trekker_Spatial_Multiome_Cuttag_analysis.Rmd, R, 447 lines, 3 matches
README.md, Text, 1 line

At the source: github.com/Liuy12/Mouse_Brain_Trekker_Multiome_Cuttag

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Version 2, 28 September 2026

Publisher: n/a → MyJOVE

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, issue, pages, dates, 8 authors, 10 MeSH terms, 1 funder, 27 references, 9 RRIDs.

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This paper

Zhang, T., Farassati, F. S., Stark-Quiroz, A., Liu, Y., Huynh, N. M., Hayashi, Y., Ordog, T., & Lee, J.-H. (2026). Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections. Journal of visualized experiments : JoVE, 10.3791/71046. https://doi.org/10.3791/71046

BibTeX

@article{zhang2026spatially, author = {Zhang, Ting and Farassati, Fatemeh S. and Stark-Quiroz, Alejandro and Liu, Yuanhang and Huynh, Nick M. and Hayashi, Yujiro and Ordog, Tamas and Lee, Jeong-Heon}, title = {{Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections}}, journal = {Journal of visualized experiments : JoVE}, year = {2026}, month = jun, number = {232}, pages = {10.3791/71046}, publisher = {MyJOVE}, issn = {1940-087X}, doi = {10.3791/71046}, url = {https://doi.org/10.3791/71046}, pmid = {42371963}, pmcid = {PMC13450693} }

RIS

TY - JOUR AU - Zhang, Ting AU - Farassati, Fatemeh S. AU - Stark-Quiroz, Alejandro AU - Liu, Yuanhang AU - Huynh, Nick M. AU - Hayashi, Yujiro AU - Ordog, Tamas AU - Lee, Jeong-Heon TI - Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections T2 - Journal of visualized experiments : JoVE J2 - J Vis Exp PY - 2026 DA - 2026/06/12 IS - 232 SP - 10.3791/71046 SN - 1940-087X PB - MyJOVE DO - 10.3791/71046 UR - https://doi.org/10.3791/71046 LA - en ER -

CSL-JSON

{ "id": "10.3791/71046", "type": "article-journal", "title": "Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections", "container-title": "Journal of visualized experiments : JoVE", "author": [ { "family": "Zhang", "given": "Ting" }, { "family": "Farassati", "given": "Fatemeh S." }, { "family": "Stark-Quiroz", "given": "Alejandro" }, { "family": "Liu", "given": "Yuanhang" }, { "family": "Huynh", "given": "Nick M." }, { "family": "Hayashi", "given": "Yujiro" }, { "family": "Ordog", "given": "Tamas" }, { "family": "Lee", "given": "Jeong-Heon" } ], "container-title-short": "J Vis Exp", "issue": "232", "page": "10.3791/71046", "DOI": "10.3791/71046", "PMID": "42371963", "PMCID": "PMC13450693", "ISSN": "1940-087X", "publisher": "MyJOVE", "URL": "https://doi.org/10.3791/71046", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 12 ] ] } }

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