An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation

(E-pub Ahead of Print)

Author(s): Zarrin Basharat*, Sania Majeed, Humaira Saleem, Ishtiaq Ahmad Khan, Azra Yasmin

Journal Name: Current Genomics

Become EABM
Become Reviewer
Call for Editor


Single cell RNA-Seq technology enables assessment of RNA expression in individual cells. This makes it popular in experimental biology for gleaning specifications of novel cell types as well as inferring heterogeneity. Experimental data conventionally contains zero counts or dropout events for many single cell transcripts. Such missing data hampers the accurate analysis using standard workflows, designed for massive RNA-Seq datasets. Imputation for single cell datasets is done to infer the missing values. This was traditionally done with ad-hoc code but later customized pipelines, workflows and specialized softwares appeared for the purpose. This made it easy to benchmark and cluster things in an organized manner. In this review, we have assembled a catalog of available RNA-Seq single cell imputation algorithms/workflows and associated softwares for the scientific community performing single-cell RNA-Seq data analysis. Continued development of imputation methods, especially using deep learning approaches would be necessary for eradicating associated pitfalls and addressing challenges associated with future large scale and heterogeneous datasets.

Keywords: Single cell, RNA-Seq, imputation, algorithms

Rights & PermissionsPrintExport Cite as

Article Details

Published on: 15 July, 2020
(E-pub Ahead of Print)
DOI: 10.2174/1389202921999200716104916
Price: $95

Article Metrics

PDF: 243