These studies facilitate the discovery of genes that identify certain subtypes of cells, or that mark intermediate states during a biological process as well as bifurcate between two alternative cellular fates. Introduction The monocle package provides a how to interpret utility functions for analyzing single cell gene expression experiments.
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This vignette provides an overview of a single cell RNA-Seq analysis workflow with Monocle. Monocle was originally developed to analyze dynamic biological processes such as cell differentiation, although it also supports other experimental settings. Monocle 2 includes new and improved algorithms for classifying and counting cells, performing differential expression analysis between subpopulations of cells, and reconstructing cellular trajectories. Monocle 2 has also been re-engineered to work well with very large single-cell RNA-Seq experiments containing tens of thousands of cells or more. In development, disease, and throughout life, cells transition from one state to another. Monocle helps you discover these transitions.
Installing the latest Beta release The latest stable release of Monocle is available through Bioconductor, and we recommend you use that. Enter the following commands at the R console: install. Sometimes we add features that require you install certain additional packages. You may see errors when you try the above command. Please do not email technical questions to Monocle contributors directly. Of the ways you could do this, we recommend you try this one first.
Of the ways you could do this, this way might work better than the one we usually recommend. Workflow steps at a glance Below, you can see snippets of code that highlight the main steps of Monocle. Click on the section headers to jump to the detailed sections describing each one. Order cells in pseudotime along a trajectory Now, put your cells in order by how much progress they’ve made through whatever process you’re studying, such as differentiation, reprogramming, or an immune response.