Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean-variance relationship of the log-counts-per-million using 'voom'. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source 'limma' package

Rethinking the measurement of energy poverty in Europe : a critical analysis of indicators and data

Energy poverty – which has been recognised via terms such as ‘fuel poverty’ and ‘energy vulnerability ’ – occurs when a household experiences inadequate levels of essential energy services in the home. Measuring energy poverty is challenging, as it is a culturally sensitive and private condition, which is temporally and spatially dynamic. This is compounded by the limited availability of appropriate data and indicators, and lack of consensus on how energy poverty should be conceptualised and measured. Statistical indicators of energy poverty are an important and necessary part of the research and policy landscape. They carry great political weight, and are often used to guide the targeting of energy poverty measures - due to their perceived objectivity - with important consequences for both the indoor and built environment of housing. Focussing on the European Union specifically, this paper critically assesses the available statistical options for monitoring energy poverty, whilst also presenting options for improving existing data. This is examined through the lens of vulnerability thinking, by considering the ways in which policies and institutions, the built fabric and everyday practices shape energy use, alongside the manner in which energy poor households experience and address the issue on a day-to-day basis