- recipe bioconductor-adimpute
Adaptive Dropout Imputer (ADImpute)
- Homepage:
https://bioconductor.org/packages/3.16/bioc/html/ADImpute.html
- License:
GPL-3 + file LICENSE
- Recipe:
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:
1.8.0-0
,1.4.0-0
,1.2.0-0
,1.0.0-1
,1.0.0-0
- Depends:
bioconductor-biocparallel
>=1.32.0,<1.33.0
bioconductor-s4vectors
>=0.36.0,<0.37.0
bioconductor-singlecellexperiment
>=1.20.0,<1.21.0
bioconductor-summarizedexperiment
>=1.28.0,<1.29.0
r-base
>=4.2,<4.3.0a0
- Required By:
Installation
With an activated Bioconda channel (see 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¶
Link to this page¶
Render an badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-adimpute/README.html)