Package Recipe 'bioconductor-dreamlet'

recipe bioconductor-dreamlet

Cohort-scale differential expression analysis of single cell data using linear (mixed) models

Homepage:: https://bioconductor.org/packages/3.21/bioc/html/dreamlet.html
License:: Artistic-2.0
Recipe:: /bioconductor-dreamlet/meta.yaml

Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

package bioconductor-dreamlet¶

Versions:

1.8.0-0, 1.6.0-0

Depends:

on bioconductor-beachmat >=2.26.0,<2.27.0
on bioconductor-beachmat >=2.26.0,<2.27.0a0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0a0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-delayedarray >=0.36.0,<0.37.0
on bioconductor-delayedarray >=0.36.0,<0.37.0a0
on bioconductor-delayedmatrixstats >=1.32.0,<1.33.0
on bioconductor-delayedmatrixstats >=1.32.0,<1.33.0a0
on bioconductor-edger >=4.8.0,<4.9.0
on bioconductor-edger >=4.8.2,<4.9.0a0
on bioconductor-gseabase >=1.72.0,<1.73.0
on bioconductor-gseabase >=1.72.0,<1.73.0a0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-iranges >=2.44.0,<2.45.0a0
on bioconductor-limma >=3.66.0,<3.67.0
on bioconductor-limma >=3.66.0,<3.67.0a0
on bioconductor-matrixgenerics >=1.22.0,<1.23.0
on bioconductor-matrixgenerics >=1.22.0,<1.23.0a0
on bioconductor-s4arrays >=1.10.0,<1.11.0
on bioconductor-s4arrays >=1.10.1,<1.11.0a0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-s4vectors >=0.48.0,<0.49.0a0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0a0
on bioconductor-sparsearray >=1.10.0,<1.11.0
on bioconductor-sparsearray >=1.10.8,<1.11.0a0
on bioconductor-sparsematrixstats >=1.22.0,<1.23.0
on bioconductor-sparsematrixstats >=1.22.0,<1.23.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
on bioconductor-variancepartition >=1.40.0,<1.41.0
on bioconductor-variancepartition >=1.40.1,<1.41.0a0
on bioconductor-zenith >=1.12.0,<1.13.0
on bioconductor-zenith >=1.12.0,<1.13.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-ashr
on r-base >=4.5,<4.6.0a0
on r-broom
on r-data.table
on r-dplyr
on r-ggbeeswarm
on r-ggplot2
on r-ggrepel
on r-gtools
on r-irlba
on r-lme4 >=1.1-33
on r-mashr >=0.2.52
on r-mass
on r-matrix
on r-metafor
on r-purrr
on r-rcpp
on r-rdpack
on r-remacor
on r-reshape2
on r-rlang
on r-scattermore
on r-tidyr

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-dreamlet

to add into an existing workspace instead, run:

pixi add bioconductor-dreamlet

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-dreamlet

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-dreamlet

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-dreamlet:<tag>

(see bioconductor-dreamlet/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-dreamlet/README.html)