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Related Experiment Video

Updated: Dec 7, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Increased comparability between RNA-Seq and microarray data by utilization of gene sets.

Frans M van der Kloet¹, Jeroen Buurmans¹, Martijs J Jonker¹

¹Swammerdam Institute for Life Sciences, University of Amsterdam.

Plos Computational Biology

|September 30, 2020

Summary

This summary is machine-generated.

This study presents a novel method to integrate transcriptomics data from microarray and RNA-sequencing (RNA-Seq) platforms. By converting raw data into gene set enrichment scores, researchers can improve data comparability and enable robust analysis of merged datasets.

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Last Updated: Dec 7, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Transcriptomics
Bioinformatics
Genomics

Background:

Transcriptomics measures messenger RNA (mRNA) to assess gene expression.
Microarray and RNA-Sequencing (RNA-Seq) are primary mRNA quantification platforms.
Inconsistencies between microarray and RNA-Seq data hinder integrated analysis.

Purpose of the Study:

To develop a robust method for increasing the comparability of microarray and RNA-Seq data.
To enable the analysis of merged datasets from both transcriptomics platforms.
To validate the method's effectiveness in biological applications.

Main Methods:

Transformed high-dimensional transcriptomics data into a lower-dimensional dataset using gene set enrichment scores.
Calculated enrichment scores based on gene set collections for all samples.
Compared data similarity using raw data and enrichment scores.
Validated the method using predictive models for breast cancer subtypes.

Main Results:

The data transformation into enrichment scores is biologically relevant and filters noise.
Enrichment scores significantly increase the concordance between microarray and RNA-Seq data.
Predictive models demonstrated successful validation using enrichment scores from both platforms.

Conclusions:

Transforming transcriptomics data into gene set enrichment scores enhances platform comparability.
This approach is a significant step towards integrating data from microarray and RNA-Seq.
Further research into gene set composition, size, and number is recommended.