title: Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq.
abstract: 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.
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Miller
authors.forename: Vilas
authors.initials: V
authors.lastname: Menon
authors.forename: Jeff
authors.initials: J
authors.lastname: Goldy
authors.forename: Ajamete
authors.initials: A
authors.lastname: Kaykas
authors.forename: Chang-Kyu
authors.initials: CK
authors.lastname: Lee
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Smith
authors.forename: Elaine H
authors.initials: EH
authors.lastname: Shen
authors.forename: John W
authors.initials: JW
authors.lastname: Phillips
authors.forename: Ed S
authors.initials: ES
authors.lastname: Lein
authors.affiliation: Allen Institute for Brain Science, 551 N 34th Street, Seattle, WA 98103, USA. mikeh@alleninstitute.org.
authors.forename: Mike J
authors.initials: MJ
authors.lastname: Hawrylycz
mesh_terms: D001921:Brain / Q000378:metabolism*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D019579:Neocortex / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000592:standards; D059467:Transcriptome