| name | bioconductor-monocle |
| description | Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progre |
| when_to_use | Use when: Analyzing single-cell RNA-Seq experiments to study complex biological processes.; Ordering single cells in pseudotime to place them along a trajectory corresponding to a biological process such as cell differentiation.; Performing differential gene expression and clustering to identify important genes and cell states.; Visualizing data distributions or trajectories using plot().. Not for: For bulk RNA-Seq differential expression, use DESeq2 instead because Monocle is specifically designed for single-cell RNA-Seq experiments.; For Python-based single-cell workflows, use Scanpy instead because Monocle is an R package. |
| user-invocable | false |
monocle
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 2.40.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: Matrix, Biobase, ggplot2, VGAM, DDRTree
- Imports: igraph, BiocGenerics, HSMMSingleCell, plyr, cluster, combinat, fastICA, irlba, matrixStats, Rtsne, MASS, reshape2, leidenbase, limma, tibble, dplyr, pheatmap, stringr, proxy, slam, viridis, biocViews, RANN, Rcpp
- System requirements: URL
- Install:
BiocManager::install("monocle")
When to Use
- Analyzing single-cell RNA-Seq experiments to study complex biological processes.
- Ordering single cells in pseudotime to place them along a trajectory corresponding to a biological process such as cell differentiation.
- Performing differential gene expression and clustering to identify important genes and cell states.
- Visualizing data distributions or trajectories using
plot().
When NOT to Use
- For bulk RNA-Seq differential expression, use
DESeq2 instead because Monocle is specifically designed for single-cell RNA-Seq experiments.
- For Python-based single-cell workflows, use
Scanpy instead because Monocle is an R package.
Data Requirements
- Input Format: Single-cell gene expression data (e.g., RNA-Seq).
- Structure: Expression matrices representing unsynchronized individual cells executing a gene expression program.
Key Parameters
- warning (FALSE): Controls whether warnings are displayed during knitr chunk execution via
opts_chunk$set().
- dpi (600): Sets the resolution for generated plots via
opts_chunk$set().
- cache (FALSE): Controls whether to cache the knitr code chunks.
Best Practices
- Load required prerequisite packages like
Biobase, reshape2, and ggplot2 using library() before starting the analysis.
- Use
set.seed() to ensure reproducibility of the trajectory learning and clustering algorithms.
- Configure global chunk options using
opts_chunk$set() to ensure high-quality plot outputs.
Common Pitfalls
- Missing Dependencies: Failing to load required packages will cause errors; fix this by running
library(Biobase) and library(ggplot2) at the start of your script.
- Non-Reproducible Results: Running the unsupervised trajectory algorithms without a seed can lead to variable results across runs; fix this by setting a random seed with
set.seed().
Alternatives
monocle3: The completely redesigned successor to Monocle, optimized for large datasets and complex trajectories.slingshot: A flexible, cluster-based trajectory inference tool for single-cell data.scater: For upstream single-cell preprocessing, normalization, and quality control.
Citations
- Trapnell C, Cacchiarelli D, et al. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudo-temporal ordering of single cells. Nature Biotechnology, 32:381-386. PMID:24658644
- Qiu X, Hill A, et al. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature Methods, 14:309-315. PMID:28114287
References
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