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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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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

OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data.

Ning Leng¹, Jeea Choi², Li-Fang Chu¹

¹Morgridge Institute for Research.

Bioinformatics (Oxford, England)

|January 9, 2016

Summary

This summary is machine-generated.

A new artifact, the ordering effect (OE), impacts single-cell RNA sequencing (scRNA-seq) data from Fluidigm C1. OEFinder software identifies these OE genes to prevent biased analysis in scRNA-seq studies.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing cellular heterogeneity.
An ordering effect (OE) artifact has been identified in scRNA-seq datasets generated by the Fluidigm C1 platform.
This OE artifact manifests as significantly increased gene expression in cells captured from specific plate output IDs, potentially biasing downstream analyses.

Purpose of the Study:

To develop a statistical method and software tool for identifying the ordering effect (OE) artifact in scRNA-seq data.
To provide researchers with a reliable way to detect and mitigate OE-related biases in their Fluidigm C1 datasets.
To ensure the accuracy and integrity of scRNA-seq data analysis.

Main Methods:

Development of a statistical method to detect the ordering effect (OE) artifact.
Creation of OEFinder, an R package with a graphical user interface.
Implementation of OEFinder for identifying OE genes in scRNA-seq datasets.

Main Results:

OEFinder successfully identifies a sorted list of ordering effect (OE) genes.
The software enables users to check for potential artifacts in Fluidigm C1 scRNA-seq data.
This facilitates the removal of OE-affected genes, improving data reliability.

Conclusions:

The OEFinder tool effectively addresses the ordering effect (OE) artifact in Fluidigm C1 scRNA-seq data.
Accurate identification of OE genes prevents biased results in downstream analyses.
OEFinder enhances the reliability of scRNA-seq data generated using the Fluidigm C1 platform.