Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on samples subgroups

Background: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. Results: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. Conclusion: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays

Stomach cancer incidence in Southern Portugal 1998-2006:a spatio-temporal analysis

Understanding geographical differences in health, particularly in small areas, became a major concern of epidemiologists. Geographical association studies and, more recently, several spatial disease mapping studies have emerged due to the development of new spatial statistical tools. Among other diseases, these methods are being applied to analyze cancer data. However, in this kind of studies, it is of utmost importance to also investigate the influence of temporal variability and that is why spatio-temporal studies became so popular. The aim of this study is to investigate spatial and temporal trends for the incidence of this type of cancer. This retrospective population-based study is based on data on all stomach cancers registered by the Southern Portuguese Cancer Registry (ROR Sul) between 1998 and 2006. Because several studies have underlined the important role of socioeconomic status in cancer risk, information on this variable has also been taken into account. Bayesian hierarchical models were applied to model stomach incidence at a county level and resulting relative risks were used to build risk maps for cancer incidence. Age-Period-Cohort models were also applied.N/

COVID-19 : nothing is normal in this pandemic

Funding: This work was partially support by CEAUL (funded by FCT – Fundação para a Ciência e a Tecnologia, Portugal, through the project UIDB/00006/2020).This manuscript brings attention to inaccurate epidemiological concepts that emerged during the COVID-19 pandemic. In social media and scientific journals, some wrong references were given to a "normal epidemic curve" and also to a "log-normal curve/distribution". For many years, textbooks and courses of reputable institutions and scientific journals have disseminated misleading concepts. For example, calling histogram to plots of epidemic curves or using epidemic data to introduce the concept of a Gaussian distribution, ignoring its temporal indexing. Although an epidemic curve may look like a Gaussian curve and be eventually modelled by a Gauss function, it is not a normal distribution or a log-normal, as some authors claim. A pandemic produces highly-complex data and to tackle it effectively statistical and mathematical modelling need to go beyond the "one-size-fits-all solution". Classical textbooks need to be updated since pandemics happen and epidemiology needs to provide reliable information to policy recommendations and actions.Publisher PDFPeer reviewe

Religious affiliation modulates weekly cycles of cropland burning in Sub-Saharan Africa

Research ArticleVegetation burning is a common land management practice in Africa, where fire is used for hunting, livestock husbandry, pest control, food gathering, cropland fertilization, and wildfire prevention. Given such strong anthropogenic control of fire, we tested the hypotheses that fire activity displays weekly cycles, and that the week day with the fewest fires depends on regionally predominant religious affiliation.We also analyzed the effect of land use (anthrome) on weekly fire cycle significance. Fire density (fire counts.km-2) observed per week day in each region was modeled using a negative binomial regression model, with fire counts as response variable, region area as offset and a structured random effect to account for spatial dependence. Anthrome (settled, cropland, natural, rangeland), religion (Christian, Muslim, mixed) week day, and their 2-way and 3-way interactions were used as independent variables. Models were also built separately for each anthrome, relating regional fire density with week day and religious affiliation. Analysis revealed a significant interaction between religion and week day, i.e. regions with different religious affiliation (Christian, Muslim) display distinct weekly cycles of burning. However, the religion vs. week day interaction only is significant for croplands, i.e. fire activity in African croplands is significantly lower on Sunday in Christian regions and on Friday in Muslim regions. Magnitude of fire activity does not differ significantly among week days in rangelands and in natural areas, where fire use is under less strict control than in croplands. These findings can contribute towards improved specification of ignition patterns in regional/global vegetation fire models, and may lead to more accurate meteorological and chemical weather forecastinginfo:eu-repo/semantics/publishedVersio

Análise preditiva: uma pequena introdução

Um dos problemas mais importantes da Estatística, pelo menos no que respeita às aplicações é sem dúvida o de predizer o futuro com base no resultado de experiência passada. Estranhamente no entanto, este problema não tem merecido, da parte dos Estatísticos, a atenção que se adivinharia da sua importância. Desenvolvimentos de metodologia Estatística têm-se centrado essencialmente em aspectos paramétricos e de modelação, sendo o problema da predição relegado para segundo plano, considerando talvez como mero “aparte”. O objectivo deste trabalho é precisamente o de fazer um pouco a história do problema da previsão estatística, apresentar a abordagem Bayesiana através de exemplos simples, fazendo um paralelo com soluções clássicas recentes

Quantification of annual wildfire risk; A spatio-temporal point process approach.

Policy responses for local and global firemanagement depend heavily on the proper understanding of the fire extent as well as its spatio-temporal variation across any given study area. Annual fire risk maps are important tools for such policy responses, supporting strategic decisions such as location-allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence without addressing the loss component. In this paper, we study the spatio-temporal point patterns of wildfires and model them by a log Gaussian Cox processes. Themean of predictive distribution of randomintensity function is used in the narrow sense, as the annual fire risk map for next year

Bayesian hierarchical models: an analysis of Portugal road accident data

In this work Bayesian hierarchical models are applied to road accident data at a county level, in Portugal, from 2000 to 2007. The objective of the study is to build model-based risk maps for road accidents at county level and to perform an analysis of association between road accidents and potential risk factors, through the inclusion of ecological covariates in the model

Mean MODIS active fire regional density (fires.km^-2), 2003–2011.

<p>a) Monday; b) Tuesday; c) Wednesday; d) Thursday; e) Friday; f) Saturday; g) Sunday.</p

Deviance Information Criterion (DIC) scores for all negative binomial models fitted.

<p>R–Religion; W–Week day; A–Anthrome; ICAR–Intrinsic conditional autoregressive term.</p><p>The best model (lowest DIC, bold) includes all single variables, the two-way interactions between religion and week day, between religion and anthrome, and the spatial term.</p