scAnnotatR
Pretrained learning models for cell type prediction on single cell RNA-sequencing data
Bioconductor version: 3.23 · Package version: 1.18.0
The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scAnnotatR") Details
| Maintainer | Johannes Griss <johannes.griss@meduniwien.ac.at> |
| Author | Vy Nguyen [aut] (ORCID: <https://orcid.org/0000-0003-3436-3662>), Johannes Griss [cre] (ORCID: <https://orcid.org/0000-0003-2206-9511>) |
| License | MIT + file LICENSE |
| URL | https://github.com/grisslab/scAnnotatR |
| Bug Reports | https://github.com/grisslab/scAnnotatR/issues/new |
| Downloads rank | 262 |
| Source branch | RELEASE_3_23 |
| biocViews | Classification, GeneExpression, SingleCell, Software, SupportVectorMachine, Transcriptomics |
Documentation
- Introduction to scAnnotatR
- Training basic model classifying a cell type from scRNA-seq data
- Training model classifying a cell subtype from scRNA-seq data
Download
Dependencies
Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment
Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, AnnotationHub, utils
Reverse dependencies
Suggests Me (1): scAnnotatR.models