:orphan: .. only available via index, not via toctree .. title:: Package Recipe 'bioconductor-dreamlet' .. highlight: bash bioconductor-dreamlet ===================== .. conda:recipe:: bioconductor-dreamlet :replaces_section_title: :noindex: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs :homepage: https://bioconductor.org/packages/3.22/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. .. conda:package:: bioconductor-dreamlet |downloads_bioconductor-dreamlet| |docker_bioconductor-dreamlet| :versions: ``1.8.0-0``,  ``1.6.0-0`` :depends on bioconductor-beachmat: ``>=2.26.0,<2.27.0`` :depends on bioconductor-beachmat: ``>=2.26.0,<2.27.0a0`` :depends on bioconductor-biocgenerics: ``>=0.56.0,<0.57.0`` :depends on bioconductor-biocgenerics: ``>=0.56.0,<0.57.0a0`` :depends on bioconductor-biocparallel: ``>=1.44.0,<1.45.0`` :depends on bioconductor-biocparallel: ``>=1.44.0,<1.45.0a0`` :depends on bioconductor-delayedarray: ``>=0.36.0,<0.37.0`` :depends on bioconductor-delayedarray: ``>=0.36.0,<0.37.0a0`` :depends on bioconductor-delayedmatrixstats: ``>=1.32.0,<1.33.0`` :depends on bioconductor-delayedmatrixstats: ``>=1.32.0,<1.33.0a0`` :depends on bioconductor-edger: ``>=4.8.0,<4.9.0`` :depends on bioconductor-edger: ``>=4.8.2,<4.9.0a0`` :depends on bioconductor-gseabase: ``>=1.72.0,<1.73.0`` :depends on bioconductor-gseabase: ``>=1.72.0,<1.73.0a0`` :depends on bioconductor-iranges: ``>=2.44.0,<2.45.0`` :depends on bioconductor-iranges: ``>=2.44.0,<2.45.0a0`` :depends on bioconductor-limma: ``>=3.66.0,<3.67.0`` :depends on bioconductor-limma: ``>=3.66.0,<3.67.0a0`` :depends on bioconductor-matrixgenerics: ``>=1.22.0,<1.23.0`` :depends on bioconductor-matrixgenerics: ``>=1.22.0,<1.23.0a0`` :depends on bioconductor-s4arrays: ``>=1.10.0,<1.11.0`` :depends on bioconductor-s4arrays: ``>=1.10.1,<1.11.0a0`` :depends on bioconductor-s4vectors: ``>=0.48.0,<0.49.0`` :depends on bioconductor-s4vectors: ``>=0.48.0,<0.49.0a0`` :depends on bioconductor-singlecellexperiment: ``>=1.32.0,<1.33.0`` :depends on bioconductor-singlecellexperiment: ``>=1.32.0,<1.33.0a0`` :depends on bioconductor-sparsearray: ``>=1.10.0,<1.11.0`` :depends on bioconductor-sparsearray: ``>=1.10.8,<1.11.0a0`` :depends on bioconductor-sparsematrixstats: ``>=1.22.0,<1.23.0`` :depends on bioconductor-sparsematrixstats: ``>=1.22.0,<1.23.0a0`` :depends on bioconductor-summarizedexperiment: ``>=1.40.0,<1.41.0`` :depends on bioconductor-summarizedexperiment: ``>=1.40.0,<1.41.0a0`` :depends on bioconductor-variancepartition: ``>=1.40.0,<1.41.0`` :depends on bioconductor-variancepartition: ``>=1.40.1,<1.41.0a0`` :depends on bioconductor-zenith: ``>=1.12.0,<1.13.0`` :depends on bioconductor-zenith: ``>=1.12.0,<1.13.0a0`` :depends on libblas: ``>=3.9.0,<4.0a0`` :depends on libgcc: ``>=14`` :depends on liblapack: ``>=3.9.0,<4.0a0`` :depends on liblzma: ``>=5.8.2,<6.0a0`` :depends on libstdcxx: ``>=14`` :depends on libzlib: ``>=1.3.1,<2.0a0`` :depends on r-ashr: :depends on r-base: ``>=4.5,<4.6.0a0`` :depends on r-broom: :depends on r-data.table: :depends on r-dplyr: :depends on r-ggbeeswarm: :depends on r-ggplot2: :depends on r-ggrepel: :depends on r-gtools: :depends on r-irlba: :depends on r-lme4: ``>=1.1-33`` :depends on r-mashr: ``>=0.2.52`` :depends on r-mass: :depends on r-matrix: :depends on r-metafor: :depends on r-purrr: :depends on r-rcpp: :depends on r-rdpack: :depends on r-remacor: :depends on r-reshape2: :depends on r-rlang: :depends on r-scattermore: :depends 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 :ref:`bioconda_setup`). 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 :ref:`bioconda_setup`), 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 :ref:`bioconda_setup`), 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: (see `bioconductor-dreamlet/tags`_ for valid values for ````). 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. .. _conda: https://conda.io .. _pixi: https://pixi.sh .. |downloads_bioconductor-dreamlet| image:: https://img.shields.io/conda/dn/bioconda/bioconductor-dreamlet.svg?style=flat :target: https://anaconda.org/bioconda/bioconductor-dreamlet :alt: (downloads) .. |docker_bioconductor-dreamlet| image:: https://quay.io/repository/biocontainers/bioconductor-dreamlet/status :target: https://quay.io/repository/biocontainers/bioconductor-dreamlet .. _`bioconductor-dreamlet/tags`: https://quay.io/repository/biocontainers/bioconductor-dreamlet?tab=tags .. raw:: html Download stats ----------------- .. raw:: html :file: ../../templates/package_dashboard.html Link to this page ----------------- Render an |install-with-bioconda| 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) .. |install-with-bioconda| image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat :target: http://bioconda.github.io/recipes/bioconductor-dreamlet/README.html