ArticlePLoS computational biology2020
Increased comparability between RNA-Seq and microarray data by utilization of gene sets.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
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- Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer.Current issues in molecular biology · 2024Article
- Omic horizon expression: a database of gene expression based on RNA sequencing data.BMC genomics · 2023Article
- Data Mining of Microarray Datasets in Translational Neuroscience.Brain sciences · 2023Review
- DEG (differentially expressed gene) or not DEG that is the question: Should we compare between datasets or not?Journal of molecular and cellular cardiology plus · 2023Article
- hCoCena: horizontal integration and analysis of transcriptomics datasets.Bioinformatics (Oxford, England) · 2022Article
- Fucose as a potential therapeutic molecule against the immune-mediated inflammation in IgA nepharopathy: An unrevealed link.Frontiers in immunology · 2022Article
- Loss of Gene Information: Discrepancies between RNA Sequencing, cDNA Microarray, and qRT-PCR.International journal of molecular sciences · 2021Article
- Transfer transcriptomic signatures for infectious diseases.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of transcriptomics uses and measures mRNA as a proxy of gene expression. There are currently two major platforms in use for quantifying mRNA, microarray and RNA-Seq. Many comparative studies have shown that their results are not always consistent. In this study we aim to find a robust method to increase comparability of both platforms enabling data analysis of merged data from both platforms. We transformed high dimensional transcriptomics data from two different platforms into a lower dimensional, and biologically relevant dataset by calculating enrichment scores based on gene set collections for all samples. We compared the similarity between data from both platforms based on the raw data and on the enrichment scores. We show that the performed data transforms the data in a biologically relevant way and filters out noise which leads to increased platform concordance. We validate the procedure using predictive models built with microarray based enrichment scores to predict subtypes of breast cancer using enrichment scores based on sequenced data. Although microarray and RNA-Seq expression levels might appear different, transforming them into biologically relevant gene set enrichment scores significantly increases their correlation, which is a step forward in data integration of the two platforms. The gene set collections were shown to contain biologically relevant gene sets. More in-depth investigation on the effect of the composition, size, and number of gene sets that are used for the transformation is suggested for future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.