- recipe bioconductor-dino
Normalization of Single-Cell mRNA Sequencing Data
Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.
- package bioconductor-dino¶
- depends bioconductor-biocparallel:
- depends bioconductor-biocsingular:
- depends bioconductor-s4vectors:
- depends bioconductor-scran:
- depends bioconductor-singlecellexperiment:
- depends bioconductor-summarizedexperiment:
- depends r-base:
- depends r-matrix:
- depends r-matrixstats:
- depends r-seurat:
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install bioconductor-dino and update with:: mamba update bioconductor-dino
To create a new environment, run:
mamba create --name myenvname bioconductor-dino
myenvnamebeing a reasonable name for the environment (see e.g. the mamba docs for details and further options).
Alternatively, use the docker container:
docker pull quay.io/biocontainers/bioconductor-dino:<tag> (see `bioconductor-dino/tags`_ for valid values for ``<tag>``)