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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Dec 22, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

scDoc: correcting drop-out events in single-cell RNA-seq data.

Di Ran¹, Shanshan Zhang^2,3, Nicholas Lytal²

¹Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health.

Bioinformatics (Oxford, England)

|May 5, 2020

Summary

This summary is machine-generated.

We developed scDoc, a novel method to correct for "drop-out" events in single-cell RNA sequencing (scRNA-seq) data. This approach improves cell subpopulation identification and data visualization by leveraging information from similar cells.

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Last Updated: Dec 22, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

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Published on: September 13, 2018

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is vital for studying cellular heterogeneity and development.
A significant challenge in scRNA-seq is 'drop-out' events, caused by low mRNA input or gene expression stochasticity.
Accurate imputation of drop-out events is crucial for reliable scRNA-seq data analysis.

Purpose of the Study:

To introduce scDoc, a novel method for single-cell RNA-seq drop-out correction.
To address the limitations of existing methods in handling drop-out events.
To improve the accuracy of downstream analyses like data visualization and cell subpopulation identification.

Main Methods:

scDoc imputes drop-out events by utilizing gene expression information from highly similar cells.
The method uniquely incorporates drop-out information into cell-to-cell similarity estimation.
Performance was evaluated using simulated data and real-world scRNA-seq datasets.

Main Results:

scDoc effectively imputes drop-out events in scRNA-seq data.
The method demonstrates superior performance compared to existing imputation techniques.
Improvements were observed in data visualization, cell subpopulation identification, and differential expression detection.

Conclusions:

scDoc offers a robust solution for drop-out events in scRNA-seq.
The method enhances the reliability and interpretability of single-cell gene expression data.
scDoc provides valuable insights into cellular heterogeneity and development.