recipe bioconductor-adimpute

Adaptive Dropout Imputer (ADImpute)

Homepage

https://bioconductor.org/packages/3.14/bioc/html/ADImpute.html

License

GPL-3 + file LICENSE

Recipe

/bioconductor-adimpute/meta.yaml

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

package bioconductor-adimpute

Versions

1.4.0-0,  1.2.0-0,  1.0.0-1,  1.0.0-0

Depends

• bioconductor-biocparallel >=1.28.0,<1.29.0

• bioconductor-s4vectors >=0.32.0,<0.33.0

• bioconductor-singlecellexperiment >=1.16.0,<1.17.0

• bioconductor-summarizedexperiment >=1.24.0,<1.25.0

• r-base >=4.1,<4.2.0a0

• r-checkmate

• r-data.table

• r-drimpute

• r-kernlab

• r-mass

• r-matrix

• r-rsvd

• r-saver

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-adimpute

and update with:

conda update bioconductor-adimpute

or use the docker container:

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

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

Download stats

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