SingleR
Clone of the Bioconductor repository for the SingleR package.
Science Score: 46.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: nature.com -
✓Committers with academic emails
5 of 14 committers (35.7%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.3%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Clone of the Bioconductor repository for the SingleR package.
Basic Info
- Host: GitHub
- Owner: SingleR-inc
- License: gpl-3.0
- Language: R
- Default Branch: master
- Homepage: https://bioconductor.org/packages/devel/bioc/html/SingleR.html
- Size: 1.41 MB
Statistics
- Stars: 192
- Watchers: 11
- Forks: 19
- Open Issues: 16
- Releases: 0
Topics
Metadata Files
README.md
SingleR - Single-cell Recognition
Current build status
- release 
- development 
Recent advances in single cell RNA-seq (scRNA-seq) have enabled an unprecedented level of granularity in characterizing gene expression changes in disease models. Multiple single cell analysis methodologies have been developed to detect gene expression changes and to cluster cells by similarity of gene expression. However, the classification of clusters by cell type relies heavily on known marker genes, and the annotation of clusters is performed manually. This strategy suffers from subjectivity and limits adequate differentiation of closely related cell subsets. Here, we present SingleR, a novel computational method for unbiased cell type recognition of scRNA-seq. SingleR leverages reference transcriptomic datasets of pure cell types to infer the cell of origin of each of the single cells independently.
For more informations please refer to the manuscript: Aran, Looney, Liu et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology (2019)
This repository contains a simplified, more performant version of SingleR.
The original repository containing the legacy version can be found here.
This version does not support the browser application that accompanied the original version.
Installation
This is the development version of the R/Bioconductor package SingleR. It may contain unstable or untested new features. If you are looking for the release version of this package please go to its official Bioconductor landing page and follow the instructions there to install it.
If you were really looking for this development version, then you can install it via:
r
install.packages("BiocManager")
BiocManager::install("SingleR", version = "devel")
Alternatively, you can install it from GitHub using the devtools package.
r
install.packages("devtools")
library(devtools)
install_github("SingleR-inc/SingleR")
Usage
The SingleR() function annotates each cell in a test dataset given a reference dataset with known labels. Documentation and basic examples can be accessed with ?SingleR.
Both basic and advanced examples can be found in the SingleR book.
Usage with Seurat/SingleCellExperiment objects
SingleR() is made to be workflow/package agnostic - if you can get a matrix of normalized counts, you can use it.
SingleCellExperiment objects can be used directly.
Seurat objects can be converted to SingleCellExperiment objects via Seurat's as.SingleCellExperiment() function or their normalized counts can be retrieved via GetAssayData or FetchData.
SingleR results labels can be easily added back to the metadata of these objects as well:
```R seurat.obj[["SingleR.labels"]] <- singler.results$labels
Or if method="cluster" was used:
seurat.obj[["SingleR.cluster.labels"]] <- singler.results$labels[match(seurat.obj[[]][["my.input.clusters"]], rownames(singler.results))] ```
Scalability
SingleR performs well on large numbers of cells - annotating 100k cells with fine-grain labels typically takes under an hour using a single processing core.
Using broad labels can reduce the time to under 15 minutes, though run times will vary between datasets and the reference dataset used.
Contributors
SingleR was originally developed by Dvir Aran. This refactor was initiated by Aaron Lun, with additional contributions from Daniel Bunis, Friederike Dündar, and Jared Andrews.
Issues and pull requests are welcome.
Owner
- Name: SingleR-inc
- Login: SingleR-inc
- Kind: organization
- Repositories: 1
- Profile: https://github.com/SingleR-inc
Code and resources for the SingleR method
GitHub Events
Total
- Issues event: 31
- Watch event: 14
- Delete event: 1
- Issue comment event: 38
- Push event: 28
- Pull request event: 2
- Fork event: 1
- Create event: 1
Last Year
- Issues event: 31
- Watch event: 14
- Delete event: 1
- Issue comment event: 38
- Push event: 28
- Pull request event: 2
- Fork event: 1
- Create event: 1
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| LTLA | i****s@g****m | 393 |
| Daniel Bunis | D****s@u****u | 117 |
| dviraran | d****n@u****u | 105 |
| Jared Andrews | j****7@g****m | 41 |
| Friederike Duendar | f****7@m****u | 28 |
| Nitesh Turaga | n****a@g****m | 12 |
| J Wokaty | j****y@s****u | 10 |
| Shen,Yang (RES) BIP-DE-B | y****n@b****m | 3 |
| Alex Pickering | a****g@g****m | 2 |
| A Wokaty | a****y@s****u | 2 |
| Daniel Bunis | d****s@d****n | 2 |
| Peter Hickey | p****y@g****m | 1 |
| zhihua-chen | 1****n | 1 |
| CHANGHEE LEE | c****e@C****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 96
- Total pull requests: 11
- Average time to close issues: 3 months
- Average time to close pull requests: 26 days
- Total issue authors: 73
- Total pull request authors: 4
- Average comments per issue: 2.64
- Average comments per pull request: 0.91
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 20
- Pull requests: 4
- Average time to close issues: 8 days
- Average time to close pull requests: 1 day
- Issue authors: 13
- Pull request authors: 2
- Average comments per issue: 2.15
- Average comments per pull request: 0.0
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- LTLA (9)
- mauritsunkel (3)
- ycl6 (3)
- lkqnaruto (3)
- nickhir (2)
- sivaraja23 (2)
- antoine4ucsd (2)
- feanaros (2)
- LeaLe88 (2)
- zznx (1)
- jianhaizhang (1)
- bbimber (1)
- wenxiang688 (1)
- robertzeibich (1)
- rustylakeparadox (1)
Pull Request Authors
- LTLA (8)
- dtm2451 (3)
- PeteHaitch (1)
- dmkv1 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- bioconductor 299,999 total
- Total dependent packages: 5
- Total dependent repositories: 0
- Total versions: 6
- Total maintainers: 1
bioconductor.org: SingleR
Reference-Based Single-Cell RNA-Seq Annotation
- Homepage: https://github.com/SingleR-inc/SingleR
- Documentation: https://bioconductor.org/packages/release/bioc/vignettes/SingleR/inst/doc/SingleR.pdf
- License: GPL-3
-
Latest release: 2.10.0
published 10 months ago
Rankings
Maintainers (1)
Dependencies
- SummarizedExperiment * depends
- BiocParallel * imports
- BiocSingular * imports
- DelayedArray * imports
- DelayedMatrixStats * imports
- Matrix * imports
- Rcpp * imports
- S4Vectors * imports
- beachmat * imports
- methods * imports
- parallel * imports
- stats * imports
- utils * imports
- BiocGenerics * suggests
- BiocStyle * suggests
- SingleCellExperiment * suggests
- celldex * suggests
- ggplot2 * suggests
- grDevices * suggests
- gridExtra * suggests
- knitr * suggests
- pheatmap * suggests
- rmarkdown * suggests
- scRNAseq * suggests
- scater * suggests
- scran * suggests
- scuttle * suggests
- testthat * suggests
- viridis * suggests