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spaceRAT

Lifecycle: experimental

Installation

Dependencies

# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)

Install from GitHub

remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)

View vignettes

browseVignettes(package="spaceRAT")

Usage

It takes two steps to perform ranked analysis of transcriptome:

  1. build a scaffold space
  2. project your new samples onto the scaffold.

Example code will be provided here to illustrate each. Additional information can be found in the vignettes.

Build a scaffold space

There are two ways to get a scaffold space. You can either obtain the prebuilt scaffolds, or build a scaffold space of your own, by providing a count matrix, a phenotype table, and a column name of the phenotype table to buildScaffold().

Build a scaffold with example data:

library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")

Project new samples

Get a list of available prebuilt scaffolds with:

library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"

Project a sample of interest into a custom built or prebuilt scaffold:

# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
About
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
About
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1 fork
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, '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
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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
About
No description, website, or topics provided.
Resources
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Stars
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Watchers
2 watching
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1 fork
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, '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" + '

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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
About
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, '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('^' + ".*" + '

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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
2 watching
Forks
1 fork
Report repository
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Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
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Skip to content
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spaceRAT 
Lifecycle: experimental
Installation
Dependencies
# Install Bioconductor dependenciesif (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment", "S4Vectors", "BiocStyle"))
# If you run into Matrix/irlba issues, # try reinstalling both from source and restart your R session:
install.packages("Matrix", type="source")
install.packages("irlba", type="source")
# Install spaceRATScaffolds from githubremotes::install_github("shdam/spaceRATScaffolds", build_vignettes=TRUE)
Install from GitHub
remotes::install_github("shdam/spaceRAT", build_vignettes=TRUE)
View vignettes
browseVignettes(package="spaceRAT")
Usage
It takes two steps to perform ranked analysis of transcriptome:
  1. build a scaffold space
  2. project your new samples onto the scaffold.
Example code will be provided here to illustrate each. Additional
information can be found in the vignettes.
Build a scaffold space
There are two ways to get a scaffold space. You can either obtain the
prebuilt scaffolds, or build a scaffold space of your own, by providing
a count matrix, a phenotype table, and a column name of the phenotype
table to buildScaffold().
Build a scaffold with example data:
library(spaceRAT)
#> Loading required package: spaceRATScaffolds
data("DMAP_exprs", "DMAP_pData", package="spaceRATScaffolds")
scaffold<- buildScaffold(
DMAP_exprs, pheno=DMAP_pData,
colname="cell_types", data="exprs", pca_scale=TRUE)
#> Preprocessing complete.#> Finding differentially expressed genes#> Reducing dimensions.#> Scaffold is built.
plotScaffold(
scaffold, title="Scaffold Plot",
dimred="PCA", dims= c(1,2), plot_mode="dot")
Project new samples
Get a list of available prebuilt scaffolds with:
library("spaceRATScaffolds")
listScaffolds()
#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!#> [1] "TCGA.v2" "DMAP.v1" "GTEx.v1" "TCGA.v1"
Project a sample of interest into a custom built or prebuilt scaffold:
# Load count data
data("ilaria_counts", package="spaceRATScaffolds")
# Load custom or prebuilt scaffoldscaffold<- buildScaffold("DMAP") # omitting '.vX' gets the latest version#> ℹ Successfully fetched list of published records - page 1#> ✔ Successfully fetched list of published records!#> ✔ Successfully fetched record for DOI '10.5281/zenodo.10842509'!# Project sample
projectSample(
sample=ilaria_counts,
scaffold=scaffold, plot_mode="dot",
dims= c(1,2),
dimred="PCA",
sample_name="New Samples",
title="Samples projected into DMAP scaffold in PCA space")
#> Preprocessing complete.#> 6 genes are added to count matrix with imputed expression level 0.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages