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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.

CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads

Requirements

Installation

To install the latest version of splikit from GitHub:

# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")

Usage

Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).

R6 Class Interface (Recommended)

The SplikitObject class provides a clean, chainable interface with built-in validation:

library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData

Traditional Function Interface

The original function-based approach remains fully supported:

# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)

Key Features

  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions

Documentation

Full guides, examples, and tutorials are available at the Splikit webpage

License

This project is licensed under the MIT License – see the LICENSE.md file for details.

Acknowledgment

The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.

About

Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data.

Topics

cranrna-splicingsingle-cell-ge

Resources

Readme
Activity
Custom properties

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 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('^' + ".*" + '

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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 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
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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 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" + '

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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 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('^' + ".*" + '

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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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Splikit /ˈsplaɪ.kɪt/ is a comprehensive R toolkit for analyzing alternative splicing in single-cell RNA sequencing (scRNA-seq) data. It offers a streamlined workflow for transforming raw junction abundance data from tools such as STARsolo into actionable insights—detecting differential splicing events and enabling rich downstream analyses. Designed for both power and ease of use, Splikit integrates high-performance C++ implementations and memory-efficient data structures to handle large datasets, all through a clean and intuitive R interface.
CRAN statusR-CMD-checkDocumentationGitHub versionCRAN downloads
Requirements
Installation
To install the latest version of splikit from GitHub:
# Install devtools if you haven't already
install.packages("devtools")
# Install splikitdevtools::install_github("csglab/splikit")
Usage
Splikit provides two ways to work with your data: a modern R6 class interface (recommended) and traditional functions (for backward compatibility).
R6 Class Interface (Recommended)
The SplikitObject class provides a clean, chainable interface with built-in validation:
library(splikit)
# Load data and create SplikitObjectjunction_ab<- load_toy_SJ_object()
obj<- splikit(junction_ab=junction_ab, min_counts=1)
# Compute M2 exclusion matrix (uses fast C++ implementation)obj$makeM2(n_threads=4)
# Find highly variable splicing eventsHVE<-obj$findVariableEvents(min_row_sum=50, n_threads=4)
# View object summaryobj$summary()
# Access data directlym1_matrix<-obj$m1m2_matrix<-obj$m2event_data<-obj$eventData
Traditional Function Interface
The original function-based approach remains fully supported:
# Create m1 matrix from a junction abundance objectjunction_abundance_object<- load_toy_SJ_object()
m1_object<- make_m1(
junction_abundance_object,
min_counts=1,
verbose=FALSE
)
# Create m2 matrix from the m1 inclusion matrixm2_matrix<- make_m2(
m1_inclusion_matrix=m1_object$m1_inclusion_matrix,
eventdata=m1_object$eventdata,
n_threads=4# Uses C++ with OpenMP
)
# Perform feature selection for splicing eventsm1_matrix<-m1_object$m1_inclusion_matrixHVE_Info<- find_variable_events(m1_matrix, m2_matrix, n_threads=32)
Key Features
  • High-performance C++ backend: Core computations use RcppArmadillo with OpenMP parallelization
  • Memory-efficient sparse matrices: Handles large single-cell datasets
  • Comprehensive validation: Input checking with informative error messages
  • Flexible API: Choose between R6 methods or traditional functions
Documentation
Full guides, examples, and tutorials are available at the Splikit webpage
License
This project is licensed under the MIT License – see the LICENSE.md file for details.
Acknowledgment
The author is aware of the package’s limitations and potential breakpoints. It was developed under limited knowledge, time, and resources, and is provided with the hope that it will be useful. Feedback and contributions from the community are warmly welcomed. If you encounter any issues or have suggestions, please open an issue.
About
Splikit /ˈsplaɪ.kɪt/ is a toolkit designed for analyzing high-dimensional single-cell splicing data. 
Topics
cranrna-splicingsingle-cell-ge
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages