Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq
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BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking. RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform. CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.
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Probe selection for the human neurogenetics analysis followed Miller et al. to analyze probes that most reliably align to RNA-sequencing values, resulting in 20,736 unique gene probes.
“Probe selection was conducted according to the analysis of Miller et al. (52) to analyze probes that most reliably align to RNA-sequencing values, resulting in 20,736 unique gene probes.”
From Oxytocin under opioid antagonism leads to supralinear enhancement of social attention (2017) · cites this paper as [52]
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Taheri, T., Farahani, A., Liu, Z.-Q., Ceballos, E. G., Harroud, A., Dagher, A., & Mišić, B. (2026) · Science Advances · doi:10.1126/sciadv.aeg8117
- Authors
- Miller, J. A., Menon, V., Goldy, J., Kaykas, A., Lee, C.-K., Smith, K. A., Shen, E. H., Phillips, J. W., Lein, E. S., & Hawrylycz, M. J.
- Affiliations
- Allen Institute for Brain Science (US)
- Corresponding author
- Hawrylycz, Mike J
- ORCID
- Venue
- BMC Genomics
- Published
- 2014-02-24
- Volume
- 15
- Issue
- 1
- Pages
- 154-154
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC4007560
- OpenAlex
- W1995809178
- Cited by
- 48
- References
- 43
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 7 · 2020: 7 · 2019: 5 · 2018: 9 · 2017: 3 · 2016: 2 · 2015: 3 · 2014: 2
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services
- University of Wisconsin-Madison
- Health Resources and Services Administration
- U.S. Public Health Service (1C76HF15069-01-00, 1C76HF19619-01-00)
- University of California, Irvine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- RNA-seq, Allen Human Brain Atlas, off-target effects, reproducibility, human cell lines, Agilent microarrays
- MeSH terms
- Brain, Humans, Reproducibility of Results, Gene Expression, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Neocortex, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Transcriptome
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