Guilherme J. M. Rosa

Reassessing design and analysis of two-colour microarray experiments using mixed effects models

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.

Noninvasive Estimation of Black Bear Abundance Incorporating Genotyping Errors and Harvested Bear

Abstract Estimating black bear (Ursus americanus) population size is a difficult but important requirement when justifying harvest quotas and managing populations. Advancements in genetic techniques provide a means to identify individual bears using DNA contained in tissue and hair samples, thereby permitting estimates of population abundance based on established mark–capture–recapture methodology. We expand on previous noninvasive population-estimation work by geographically extending sampling areas (36,848 km2) to include the entire Northern Lower Peninsula (NLP) of Michigan, USA. We selected sampling locations randomly within biologically relevant bear habitat and used barbed wire hair snares to collect hair samples. Unlike previous noninvasive studies, we used tissue samples from harvested bears as an additional sampling occasion to increase recapture probabilities. We developed subsampling protocols to account for both spatial and temporal variance in sample distribution and variation in sample quality using recently published quality control protocols using 5 microsatellite loci. We quantified genotyping errors using samples from harvested bears and estimated abundance using statistical models that accounted for genotyping error. We estimated the population of yearling and adult black bears in the NLP to be 1,882 bears (95% CI = 1,389–2,551 bears). The derived population estimate with a 15% coefficient of variation was used by wildlife managers to examine the sustainability of harvest over a large geographic area.

On reference designs for microarray experiments.

We compare four variants of the reference design for microarray experiments in terms of their relative efficiency. A common reference sample across arrays is the most extensively used variation in practice, but independent samples from a reference group have also been considered in previous works. The relative efficiency of these designs depends of the number of treatments and the ratio between biological and technical variances. Here, we propose another alternative of reference structure, denoted by blocked reference design (BRD), in which each set (replication) of the treated samples is co-hybridized to an independent experimental unit of the control (reference) group. We provide efficiency curves for each pair of designs under different scenarios of variance ratio and number of treatments groups. The results show that the BRD is more efficient and less expensive than the traditional reference designs. Among the situations where the BRD is likely to be preferable we list time course experiments with a baseline and drug experiments with a placebo group.

Subsampling Hair Samples Affects Accuracy and Precision of DNA-Based Population Estimates

Abstract Variance in population estimates is affected by the number of samples that are chosen to genotype when multiple samples are available during a sampling period. Using genetic data obtained from noninvasive hair-snags used to sample black bears (Ursus americanus) in the Northern Lower Peninsula of Michigan, USA, we developed a bootstrapping simulation to determine how precision of population estimates varied based on the number of samples genotyped. Improvements in precision of population estimates were not monotonic over all samples sizes available for genotyping. Estimates of cost, both financially and in terms of bias associated with increasing genotyping error and benefits in terms of greater estimate precision, will vary by species and field conditions and should be determined empirically.