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
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("monocle")plot().DESeq2 instead because Monocle is specifically designed for single-cell RNA-Seq experiments.Scanpy instead because Monocle is an R package.opts_chunk$set().opts_chunk$set().Biobase, reshape2, and ggplot2 using library() before starting the analysis.set.seed() to ensure reproducibility of the trajectory learning and clustering algorithms.opts_chunk$set() to ensure high-quality plot outputs.library(Biobase) and library(ggplot2) at the start of your script.set.seed().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.This skill is the knowledge layer — when, why, and how to use monocle. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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