anacarolinaleote/ADImpute: Adaptive Dropout Imputer (ADImpute)

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.

Getting started

Package details

Bioconductor views GeneExpression Network Preprocessing Sequencing SingleCell Transcriptomics
LicenseGPL-3 + file LICENSE
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
anacarolinaleote/ADImpute documentation built on May 18, 2021, 10:11 p.m.