Data Normalization

In the past 5 years, RNA-Seq approaches, based on high-throughput sequencing technologies, are becoming an essential tool in transcriptomics studies. It is now commonly accepted that a normalization preprocessing step can significantly improve the quality of the analysis, in particular, ...

Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts, and other sources of unwanted variation are present ...

UCSD researchers have assessed the performance of seven normalization methods for single cell RNA-seq using data generated from dilution of RNA samples. Their analyses showed that methods considering spike-in ERCC RNA molecules significantly outperformed those not considering ERCCs. This work ...

DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical ...

Various methods had been introduced for normalization and comparison of RNA-seq count data. However, they lacked objectivity because they based on ad hoc assumptions that were never verified their appropriateness. Here, researchers from Akita Prefectural University introduced a method that assumes ...

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning ...

A team led by researchers at the National Heart Lung and Blood Institute sequenced over 700 individuals from the Drosophila Genetic Reference Panel with the goal of identifying the optimal analysis approach for the detection of differential gene expression among ...

Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. ...

High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational ...

from RPubs – by Prasanth A S In order to explain the different methods of normalization and their problems with large array of data, which are assumed to have: most genes are not differentially expressed across conditions. the distribution of ...