Face up to false positives

Valuable lessons-learned in transcriptomics experimentation

“…There are many transcriptomics experiments out there that have yielded sub-par results due to an incomplete experimental design, missing, for instance, essential controls due to budgetary reasons. 3,4 In these cases, it would be better to set up the experiments on a smaller scale or using a different technique that has a solid design and lies within budgetary limits. Conversely, given the growing understanding of the role of miRNA in gene expression regulation in combination with good and affordable NGS technology for small RNA-seq, it seems good science to investigate both mRNA and miRNA transcriptomes in parallel within one experiment.…”

“…It is extremely important at this early design stage to foresee, as much as possible, the eventual required data analyses, as these regularly influence the experiment design. [2][3][4] One key lesson, related to statistics, concerns sample pooling. For decades, biologists intuitively have pooled their samples.…”

“…Much as we appreciate the desire for independence in each life-sciences researcher, we advocate a strict multidisciplinary approach when it comes to omics experimentation. 2,3,17 One of the most prevalent hazards in data analysis is that biologists and/or bioinformaticians get lost in the transcriptomics data swamp. The sheer amount of data invariably leads to (apparent) remarkable observations.…”

“…Even though the reasons, such as expensive experimentation, or pressure to finish a PhD study in time, can be very persuasive, bioinformatics analysis of poor data invariably turns out to be an extremely time-consuming effort that rarely has a satisfying or scientifically sound outcome. 3,4 Data analysis is still the domain of bioinformatics experts. Currently, we experience a trend in which simple-to-operate software tools allow biologists to analyze omics data themselves.…”

Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations

“…The growing feature dimensionality of today's problems warrants caution in controlling for false positives [6]. A number of techniques have been put forth for addressing this critical concern in the context of sparse regression [7].…”