RT Journal Article
SR Electronic
T1 Using Mixtures of Biological Samples as Genome-Scale Process Controls
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 015107
DO 10.1101/015107
A1 Jerod Parsons
A1 Sarah Munro
A1 P. Scott Pine
A1 Jennifer McDaniel
A1 Michele Mehaffey
A1 Marc Salit
YR 2015
UL http://biorxiv.org/content/early/2015/02/11/015107.abstract
AB Background Genome-scale “-omics” measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements.Results The parameters of a model fit to a measured -omic profile can be evaluated to assess bias and variability of the genome-scale measurement of a mixture. A linear model describes the behavior of expression measures of mixtures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the messenger RNA (mRNA) content of the individual components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine mRNA content.Conclusions Genome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The mRNA fraction accounts for differential enrichment of mRNA from varying total RNA samples. Spike-in controls can be utilized to measure this relationship between mRNA content and input total RNA. Analysis of mixtures can also be employed to determine the composition and proportions of an unknown sample, even when component-specific markers are not previously known, so long as pure components can be measured alongside the mixture.