SIMLR
Bioc currentSingle-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Release Lineage
Entered 3.4 · Oct 18, 2016
Current · Requires R 4.6
Description
Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
97 4 exported
Complexity
3.7 avg / 19 max
Call network
97 nodes / 56 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
4,253
Files
50
Compiled share
49.2%
Has compiled src
Yes
Language breakdown
API
Exported functions
4
Internal functions
3
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.01
testthat edition
–
CI present
Yes
CI type
["github-actions"]
PR gated
Yes
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.1.0
System requirements
–
C++ standard
–
License
file LICENSE
License flags
SPDX valid, not OSI
History
Versions
20
First release
2016-11-08
Latest release
2026-04-28
Avg cadence
181 days
Cold removal rate
0%
Dep drift
4
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
- Luca De Sano maintainer author
- Serafim Batzoglou contributor
- Daniele Ramazzotti author
- Bo Wang author