Bioconductor Monocle

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

• Homepage: https://bioconductor.org/packages/monocle
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/monocle/inst/doc/monocle-vignette.pdf

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