Quality Control Metrics for Extraction-Free Targeted RNA-Seq Under a Compositional Framework

Dominic LaRoche, Dean Billheimer, Kurt Michels, Bonnie LaFleur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rapid rise in the use of RNA sequencing technology (RNA-seq) for scientific discovery has led to its consideration as a clinical diagnostic tool. However, as a new technology the analytical accuracy and reproducibility of RNA-seq must be established before it can realize its full clinical utility (SEQC/MAQC-III Consortium, 2014; VanKeuren-Jensen et al. 2014). We respond to the need for reliable diagnostics, quality control metrics and improved reproducibility of RNA-seq data by recognizing and capitalizing on the relative frequency nature of RNA-Seq data. Problems with sample quality, library preparation, or sequencing may result in a low number of reads allocated to a given sample within a sequencing run. We propose a method, based on outlier detection of Centered Log-Ratio (CLR) transformed counts, for objectively identifying problematic samples based on the total number of reads allocated to the sample. Normalization and standardization methods for RNA-Seq generally assume that the total number of reads assigned to a sample does not affect the observed relative frequencies of probes within an assay. This assumpion, known as Compositional Invariance, is an important property for RNA-Seq data which enables the comparison of samples with differing read depths. Violations of the invariance property can lead to spurious differential expression results, even after normalization. We develop a diagnostic method to identify violations of the Compositional Invariance property. Batch effects arising from differing laboratory conditions or operator differences have been identified as a problem in high-throughput measurement systems (Leek et al. in Genome Biol 15, R29 [14]; Chen et al. in PLoS One 6 [10]). Batch effects are typically identified with a hierarchical clustering (HC) method or principal components analysis (PCA). For both methods, the multivariate distance between the samples is visualized, either in a biplot for PCA or a dendrogram for HC, to check for the existence of clusters of samples related to batch. We show that CLR transformed RNA-Seq data is appropriate for evaluation in a PCA biplot and improves batch effect detection over current methods. As RNA-Seq makes the transition from the research laboratory to the clinic there is a need for robust quality control metrics. The realization that RNA-Seq data are compositional opens the door to the existing body of theory and methods developed by Aitchison (The statistical analysis of compositional data, Chapman & Hall Ltd., 1986) and others. We show that the properties of compositional data can be leveraged to develop new metrics and improve existing methods.

Original languageEnglish (US)
Title of host publicationPharmaceutical Statistics - MBSW 39, 2016
EditorsRay Liu, Yi Tsong
PublisherSpringer New York LLC
Pages299-314
Number of pages16
ISBN (Print)9783319673851
DOIs
StatePublished - Jan 1 2019
Event39th Annual Midwest Biopharmaceutical Statistics Workshop, MBSW 2016 - Muncie, United States
Duration: May 16 2016May 18 2016

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume218
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference39th Annual Midwest Biopharmaceutical Statistics Workshop, MBSW 2016
CountryUnited States
CityMuncie
Period5/16/165/18/16

Keywords

  • Composition
  • Next generation sequencing
  • Normalization
  • Quality control
  • RNA-Seq
  • Relative abundance

ASJC Scopus subject areas

  • Mathematics(all)

Fingerprint Dive into the research topics of 'Quality Control Metrics for Extraction-Free Targeted RNA-Seq Under a Compositional Framework'. Together they form a unique fingerprint.

  • Cite this

    LaRoche, D., Billheimer, D., Michels, K., & LaFleur, B. (2019). Quality Control Metrics for Extraction-Free Targeted RNA-Seq Under a Compositional Framework. In R. Liu, & Y. Tsong (Eds.), Pharmaceutical Statistics - MBSW 39, 2016 (pp. 299-314). (Springer Proceedings in Mathematics and Statistics; Vol. 218). Springer New York LLC. https://doi.org/10.1007/978-3-319-67386-8_21