Package: splikit 2.3.2

Arsham Mikaeili Namini

splikit: Analysing RNA Splicing in Single-Cell RNA Sequencing Data

Provides analysis of high-dimensional single-cell splicing data. Offers a framework to extract and work with ratio-based data structures derived from single-cell RNA sequencing experiments. Provides both a modern 'R6' object-oriented interface and direct matrix manipulation functions. Core functionalities are implemented in 'C++' via 'Rcpp' to ensure high performance and scalability on large datasets.

Authors:Arsham Mikaeili Namini [aut, cre]

splikit_2.3.2.tar.gz
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manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
splikit/json (API)

# Install 'splikit' in R:
install.packages('splikit', repos = c('https://csglab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/csglab/splikit/issues

Pkgdown/docs site:https://csglab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

rna-splicingsingle-cell-geopenblascppopenmp

5.29 score 1 stars 6 scripts 545 downloads 18 exports 6 dependencies

Last updated from:e74c468ede. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK174
linux-devel-x86_64OK183
source / vignettesOK313
linux-release-arm64OK188
linux-release-x86_64OK208
macos-release-arm64OK171
macos-release-x86_64OK418
macos-oldrel-arm64OK233
macos-oldrel-x86_64OK377
windows-develOK179
windows-releaseOK198
windows-oldrelOK173
wasm-releaseOK138

Exports:find_variable_eventsfind_variable_genesget_pseudo_correlationget_rowVarget_silhouette_meanload_toy_M1_M2_objectload_toy_SJ_objectmake_eventdata_plusmake_gene_countmake_junction_abmake_m1make_m2make_velo_countplot_exclusive_junctionsplot_exclusive_junctions_eventplot_exclusive_junctions_pdfsplikitSplikitObject

Dependencies:data.tablelatticeMatrixR6RcppRcppArmadillo

Methods Overview
Local junction variants (LJVs) | Variable-event selection | Variable-gene selection | Event-covariate association | Implementation

Last update: 2026-06-09
Started: 2026-05-09

Splikit Manual for Single-Cell Splicing Analysis
Table of Contents | Introduction | What Does Splikit Do? | How splikit get these data? | Installation | Prerequisites | Installation from Source | Verify Installation | Core Concepts | Splicing Junction Events | Matrix Representations | Coordinate Groups | Variable Event Detection | Getting Started | Quick Start Workflow | R6 Object-Oriented Interface | Creating a SplikitObject | Complete Workflow with Method Chaining | Step-by-Step Workflow | Subsetting SplikitObjects | Working with Existing Matrices | Accessing SplikitObject Fields | R6 vs Function-Based Interface Comparison | Performance Features | Main Workflow Functions | make_junction_ab() | make_m1() | make_m2() | Additional Data Processing Functions | make_gene_count() | make_velo_count() | Feature Selection Functions | find_variable_events() | find_variable_genes() | Utility Functions | get_pseudo_correlation() | get_rowVar() | get_silhouette_mean() | Complete Workflow Example | Scenario: Analyzing Cell Type-Specific Splicing | Advanced Workflow: Multi-sample Comparison | Advanced Usage | Performance Optimization | Parallel Processing | Memory Management | Integration with Other Packages | Seurat Integration | SingleCellExperiment Integration | Custom Analysis Functions | Differential Splicing Analysis | Troubleshooting | Common Issues and Solutions | Issue 1: Memory Errors with Large Datasets | Issue 2: STARsolo Directory Structure Problems | Issue 3: Barcode Mismatch Issues | Issue 4: Low Event Detection | Performance Benchmarking | Function Reference | Core Workflow Functions | Data Loading Functions | Technical Functions | Conclusion

Last update: 2026-06-09
Started: 2025-08-10

STARsolo Processing Guide for Splicing Analysis
Splicing-Pipeline | 1. Mapping using STARsolo

Last update: 2025-08-10
Started: 2025-08-10