Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge due to the vast amounts of data and the large variety of pre-processing and filtering steps employed before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip Human Exon 1.0 ST Array

Early life predictors of intelligence in young adulthood and middle age

BACKGROUND:Studies on early predictors of intelligence often focus on single or few predictors and often on childhood intelligence. This study compared the contributions of a broad selection of potential early predictors of intelligence at different adult ages. METHODS:Information on predictors was recorded prospectively in the Copenhagen Perinatal Cohort during pregnancy, at delivery, and at 1- and 3-year examinations for children born between 1959-61. Adult intelligence was assessed at three independent follow-ups using three different tests of intelligence: Børge Priens Prøve, Wechsler Adult Intelligence Scale, and Intelligenz-Struktur-Test 2000R. From a total of 4697 cohort members, three non-overlapping samples were derived. RESULTS:The included predictors explained between 22.2-24.3% of the variance in adult IQ, with parental socioeconomic status and sex explaining 16.2-17.0%. Other consistent predictors were head circumference at birth, increase in head circumference head during the first three years, and 3-year milestones. Head circumference was the most important anthropometric measure compared to measures of weight and length. CONCLUSION:Besides social status and sex, the strongest and most consistent early predictors of adult intelligence were physical or behavioural characteristics that to some extent reflect brain-and cognitive development

Unaccounted uncertainty from qPCR efficiency estimates entails uncontrolled false positive rates

BACKGROUND: Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments. The most commonly used correction strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the ΔΔC(q). Currently, it is recommended to determine the AE with high precision as this plug-in approach does not account for the AE uncertainty, implicitly assuming an infinitely precise AE estimate. Determining the AE with such precision, however, requires tedious laboratory work and vast amounts of biological material. Violation of the assumption leads to overly optimistic standard errors of the ΔΔC(q), confidence intervals, and p-values which ultimately increase the type I error rate beyond the expected significance level. As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level. RESULTS: We suggest and benchmark different methods to obtain the standard error of the efficiency adjusted ΔΔC(q) using the statistical delta method, Monte Carlo integration, or bootstrapping. Our suggested methods are founded in a linear mixed effects model (LMM) framework, but the problem and ideas apply in all qPCR experiments. The methods and impact of the AE uncertainty are illustrated in three qPCR applications and a simulation study. In addition, we validate findings suggesting that MGST1 is differentially expressed between high and low abundance culture initiating cells in multiple myeloma and that microRNA-127 is differentially expressed between testicular and nodal lymphomas. CONCLUSIONS: We conclude, that the commonly used efficiency corrected quantities disregard the uncertainty of the AE, which can drastically impact the standard error and lead to increased false positive rates. Our suggestions show that it is possible to easily perform statistical inference of ΔΔC(q), whilst properly accounting for the AE uncertainty and better controlling the false positive rate