BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.
RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance.
CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months.

How individuals respond to environmental change determines the strength and direction of biological processes like recruitment and growth that underpin population productivity. Ascertaining the relative importance of environmental factors can, however, be difficult given the numerous mechanisms through which they affect individuals. This is especially true in dynamic and complex estuarine environments. Here, we develop long-term otolith-based indices of recruitment and growth for estuary perch Percalates colonorum (Bemm River, Australia), to explore the importance of intrinsic (individual, demographic) and extrinsic (hydrologic, climatic, density-dependent) factors in driving estuarine fish productivity. Analyses involved a novel zero-inflated specification of catch curve regression and mixed effects modelling. The 39 years of recruitment and 46 years of growth data, spanning a period of environmental change including severe drought, displayed considerable inter-annual variation. Recruitment success was strongly related to high freshwater inflows during the spawning season, suggesting that these conditions act as spawning cues for adults and potentially provide favourable conditions for larvae. Individuals displayed age-dependent growth, with highest rates observed at younger ages in years characterized by warm temperatures, and to a lesser degree, greater magnitude base inflow conditions. We detected systematic among-year-class growth differences, but these were not attributable to year class strength, suggesting that environmental conditions experienced by individuals as juveniles can have long-lasting effects of greater importance to population productivity than density-dependent growth responses. The primacy of temperature in driving growth variation highlights that under-appreciated climatic variation can affect estuarine fish productivity through direct physiological and indirect food web mechanisms. We predict that climatic warming will promote individual growth in southerly populations of P. colonorum but concurrently limit recruitment due to forecast reductions in spawning season river discharge. Disparate trait responses are likely in other fishes as they respond to multiple and changing environmental drivers, making predictions of future population productivity challenging.