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 details |
|
---|---|
Author | Ana Carolina Leote [cre, aut] (<https://orcid.org/0000-0003-0879-328X>) |
Bioconductor views | GeneExpression Network Preprocessing Sequencing SingleCell Transcriptomics |
Maintainer | Ana Carolina Leote <anacarolinaleote@gmail.com> |
License | GPL-3 + file LICENSE |
Version | 1.0.0 |
Package repository | View on Bioconductor |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.