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About Spaniel

Spaniel is an R package designed to visualise results of Spatial Transcriptomics experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:


BiocManager::install('Spaniel')

Spaniel - with 10X import option

This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.
This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:


devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data

This vignette will show how to load the results of 10X Visium spatial transcriptomics experiment which has been run through the Space Ranger pipeline. The data is distributed as part of the Space Ranger software package which can be downloaded here:

https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome

The output from from the "spaceranger testrun" is used as an example here.

Import the expression data

Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.

pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object

The pixel coordinates are added to the colData of the SCE object shown below:

colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:

metadata(sce)$ImgDims

The image is stored as a rasterised grob.

metadata(sce)$Grob

Quality Control

Assessing the number of genes and number of counts per spot is a useful quality control step. Spaniel allows QC metrics to be viewed on top of the histological image so that any quality issues can be pinpointed. Spots within the tissue region which have a low number of genes or counts may be due to experimental problems which should be addressed. Conversely spots which lie outside of the tissue and have a high number of counts or large number of genes may indicate that there is background contamination.

Visualisation

The plotting function allows the use of a binary filter to visualise which spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.

NOTE: The parameters are set for subset of counts used in this dataset.
The filter thresholds will be experiment specific and should be adjusted as necessary.


filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.

sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.


sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

About
Spatial Transcriptomics Analysis
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Readme
MIT license
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3 watching
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3 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

About
Spatial Transcriptomics Analysis
Resources
Readme
MIT license
Activity
Stars
7 stars
Watchers
3 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

About
Spatial Transcriptomics Analysis
Resources
Readme
MIT license
Activity
Stars
7 stars
Watchers
3 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

About
Spatial Transcriptomics Analysis
Resources
Readme
MIT license
Activity
Stars
7 stars
Watchers
3 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

About
Spatial Transcriptomics Analysis
Resources
Readme
MIT license
Activity
Stars
7 stars
Watchers
3 watching
Forks
3 forks
Report repository
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Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
About Spaniel
Spaniel is an R package designed to visualise results of Spatial Transcriptomics
experiments. The current stable version of Spaniel (version 1.1.0) is available from Bioconductor:

BiocManager::install('Spaniel')

Spaniel - with 10X import option
This vignette refers to a development version of Spaniel (version 1.2) designed to import data from a 10X Genomics Visium experiment.

This version will be tested and pushed to Bioconductor. In the meantime, if you would like to test the features described in this vignette you can install a development Spaniel (version 1.2) using the following command:

devtools::install_github("RachelQueen1/Spaniel", ref = "Development" )

library(Spaniel)
library(DropletUtils)
library(scater)

Data
This vignette will show how to load the results of 10X Visium spatial
transcriptomics experiment which has been run through the Space Ranger pipeline.
The data is distributed as part of the Space Ranger software package which can
be downloaded here:
https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome
The output from from the "spaceranger testrun" is used as an example here.
Import the expression data
Spaniel can be load the output directly from SpaceRanger output using the createVisiumSCE function. This which imports the gene expression data, spatial barcodes, and image dimensions into the SingleCellExperiment object.
pathToTenXOuts <- file.path(system.file(package = "Spaniel"), "extData/outs")
sce <- createVisiumSCE(tenXDir=pathToTenXOuts, resolution="Low")

SCE Object
The pixel coordinates are added to the colData of the SCE object shown below:
colData(sce)[, c("Barcode", "pixel_x", "pixel_y")]

The image dimensions are added to the metadata of the SCE object:
metadata(sce)$ImgDims

The image is stored as a rasterised grob.
metadata(sce)$Grob

Quality Control
Assessing the number of genes and number of counts per spot is a useful quality
control step. Spaniel allows QC metrics to be viewed on top of the
histological image so that any quality issues can be pinpointed.
Spots within the tissue region which have a low number of genes or counts may
be due to experimental problems which should be addressed. Conversely spots
which lie outside of the tissue and have a high number of counts or large
number of genes may indicate that there is background contamination.
Visualisation
The plotting function allows the use of a binary filter to visualise which
spots pass filtering thresholds. We create a filter to show spots at 1 gene is detected. Spots where no genes are detected will be removed from the remainder of the analysis.
NOTE: The parameters are set for subset of counts used in this dataset.

The filter thresholds will be experiment specific and should be adjusted as
necessary.

filter <- sce$detected > 0
spanielPlot(object = sce,
plotType = "NoGenes", showFilter = filter, techType = "Visium", ptSizeMax = 3)

Spots where no genes are detected can be removed from the remainder of the analysis.
sce <- sce[, filter]

The filtered data can then be normalised using the the "normalize" function from scater and the expression of selected genes can be viewed on the histological image.

sce <- logNormCounts(sce)
gene <- "ENSMUSG00000024843"
p2 <- spanielPlot(object = sce,
plotType = "Gene", gene = gene,
showFilter = NULL, techType = "Visium", ptSizeMax = 3)
p2

About
Spatial Transcriptomics Analysis
Resources
Readme
MIT license
Activity
Stars
7 stars
Watchers
3 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages