dataesr/scientific-paragraphs-categorization huggingface dataset:
Then we compare these two methods to the results of independent per-study analyses and a negative binomial generalized linear model (GLM) with a fixed study effect as implemented in the DESeq Bioconductor package  [1]. All methods are compared on real data from two related studies on human melanoma cell lines, as well as in an extensive set of simulations varying the inter-study variability, number of studies, and biological replicates per study. Finally, we note that our focus is on RNA-seq data arising from two or more studies in which all experimental conditions under consideration are included in every study (with potentially different numbers of biological replicates); differential analyses among conditions that are not studied in the same experiment are typically limited, or even compromised, due to the confounding of condition and study effects.
Additional considerations for p-value combination We note that the implementation of the previously described p-value combination techniques requires two additional considerations to be taken into account. First, a crucial underlying assumption for the statistics defined in Equations (  2 ) and (  3 ) is that p-values for all genes arising from the per-study differential analyses are uniformly distributed under the null hypothesis H 0,gs defined in Equation (  1 ). This assumption is, however, not always satisfied for RNA-seq data; in particular, a peak is often observed for p-values close to 1 due to the discretization of p-values for very low counts. To circumvent this first difficulty, we propose to filter the weakly expressed genes in each study using the HTSFilter Bioconductor package  [16] , as described in the Supplementary Materials.
We note that in so doing, it is possible for a gene to be filtered from one study and not from another. As will be seen in the following, this approach appears to effectively filter those genes contributing to a peak of large p-values, resulting in p-values that appear to be roughly uniformly distributed under the null hypothesis. Second, for the two p-value combination methods described above, unlike microarray data, under-and over-expressed genes are analyzed together for RNA-seq data. As such, some care must be taken to identify genes exhibiting conflicting expression patterns (i. e. , under-expression when comparing one condition to another in one study, and over-expression for the same comparison in another study). We suggest that genes exhibiting differential expression conflicts among studies be identified post hoc, and removed from the list of differentially expressed genes; this step to remove genes with conflicting differential expression from the final list of differentially expressed genes may be performed automatically within the associated R package metaRNASeq.
Parameters for simulations To fix realistic values for the parameters {w gc , φ gs , σ}, we first performed individual per-study differential analyses by fitting a negative binomial model with the default methods and parameters of the DESeq package (see Section 2. 1) on the unfiltered human data presented in Section 3. 1. The per-study false discovery rate was subsequently controlled at the α = 0. 05 level  [3]. For the genes identified as differentially expressed in both studies, w g1 and w g2 were fixed to be the values of the empirical means (after normalization for library size differences) for each condition across studies. For the remaining genes, we set w g1 = w g2 = w g to be the overall empirical mean (after normalization for library size differences) for gene g across both conditions and studies.
Methods and criteria for comparison In addition to the intersection of independent per-study analyses (where genes were declared to be differentially expressed if identified in more than half of the studies with no differential conflict), the Fisher and inverse normal p-value combination techniques, and the global analysis with fixed study effect, we also considered a global analysis with no study effect. For each simulation setting and level of inter-study variability σ, 300 independent datasets were simulated, and the filtering method of  [16]  was applied, either independently to each study (for the independent per-study analyses and p-value combination techniques) or to the full set of data (for the global analysis). For each method, performance was assessed using the sensitivity, false discovery rate (FDR) and area under the receiver operating characteristic (ROC) curve (AUC).
In addition, we also considered Figure  3 : Receiver Operating Characteristic (ROC) curves, averaged over 300 datasets. Each plot represents the results of a particular setting, with columns corresponding (from left to right) to simulations including 2 studies, 3 studies, and 5 studies, and rows corresponding (from top to bottom) to simulations with inter-study variability set to σ = 0. 15 and σ = 0. 50 (low inter-study variability to lareg inter-study variability). Within each plot: Fisher (red lines), global analysis with no fixed study effect (DESeq (no study), blue lines), and global analysis with a fixed study effect (DESeq (study), orange lines). The dotted black line represents the diagonal. a criterion to assess the "value added" for the p-value combination methods with respect to the global analysis, and vice versa: the proportion of true positives among those uniquely identified by a given method (e.
Conclusions The aim of this paper was to present and compare different strategies for the differential metaanalysis of RNA-seq data arising from multiple, related studies. As expected, naive analyses such as the overlap of lists of differentially expressed genes found by individual studies or a global analysis not accounting for a study effect perform very poorly. On the other hand, the two proposed meta-analysis methods seem to have very similar performances. For low inter-study variability, the results are very close to those of a global GLM analysis including a study effect. When the interstudy variability increases, however, the gains in performance in terms of AUC, sensitivity, and proportion of true positives among uniquely identified genes for the meta-analysis techniques are   Error bars represent one standard deviation, and numbers in parentheses represent the mean total number of unique discoveries for DESeq with study effect as compared to Fisher and vice versa, respectively.
significant as compared to the global analysis, particularly for the analysis of data from more than two studies. We note that both of the proposed p-value combination methods are implemented in an R package called metaRNASeq, available on the R-Forge. Our focus in this work is on differential analyses between two experimental conditions, but can readily be extended to multi-group comparisons. However, as previously noted, the methods presented here are intended for the analysis of data in which all experimental conditions under consideration are included in every study, thus avoiding problems due to the confounding of condition and study effects. As with all meta-analyses, the p-value combination techniques presented here must overcome differences in experimental objectives, design, and populations of interest, as well as differences in sequencing technology, library preparation, and laboratory-specific effects.
Background: Association mapping studies offer great promise to identify polymorphisms associated with phenotypes and for understanding the genetic basis of quantitative trait variation. To date, almost all association mapping studies based on structured plant populations examined the main effects of genetic factors on the trait but did not deal with interactions between genetic factors and environment. In this paper, we propose a methodological prospect of mixed linear models to analyze genotype by environment interaction effects using association mapping designs. First, we simulated datasets to assess the power of linear mixed models to detect interaction effects. This simulation was based on two association panels composed of 90 inbreds (pearl millet) and 277 inbreds (maize). Results: Based on the simulation approach, we reported the impact of effect size, environmental variation, allele frequency, trait heritability, and sample size on the power to detect the main effects of genetic loci and diverse effect of interactions implying these loci.
Interaction effects specified in the model included SNP by environment interaction, ancestry by environment interaction, SNP by ancestry interaction and three way interactions. The method was finally used on real datasets from field experiments conducted on the two considered panels. We showed two types of interactions effects contributing to genotype by environment interactions in maize: SNP by environment interaction and ancestry by environment interaction. This last interaction suggests differential response at the population level in function of the environment. Conclusions: Our results suggested the suitability of mixed models for the detection of diverse interaction effects. The need of samples larger than that commonly used in current plant association studies is strongly emphasized to ensure rigorous model selection and powerful interaction assessment.
Basic scheme for the simulation of association mapping data A basic model of genotype-phenotype association consists in simulating datasets characterized by the main effect of a single locus on the trait. Such an effect could be added on real phenotypic scores from a structured panel, so that the generated phenotype is linked to a real genetic background. With this scheme, there is no longer any need to simulate background matrices. So let us consider a panel of n individuals. We denote p i the original (real) phenotype of an individual i. Considering a binary causative polymorphism (for instance presence/absence of a SNP), a simulated phenotype y i , can be assigned to each individual i as follows: y i ¼ p i þ S i r σ G þ ε i ; ð1Þ where S is a random variable with possible values "1" and "0", standing respectively for the presence or the absence of the causative allele; σ G is the standard deviation of the original trait across the whole panel; r is the genetic effect ratio (or effect ratio, i.
05 to 2 (9 values); q was set at 0. 05, 0. 25 and 0. 5, respectively; h 2 was set at 0. 25 and 0. 75, respectively. For each combination of parameters, a thousand samples were generated for pearl millet panel and a hundred samples for maize, as in equation 2. The size of each individual sample was the same as in the original panel (n = 90 for pearl millet and n = 277 for maize). Among the populations comprising each panel, the population with the highest average ancestry (q 0 = 0. 28 for pearl millet and q 0 = 0. 49 for maize, respectively) was defined as P 0 , to maximize sample size. We wrote scripts with the software R (version 2. 7. 2) to implement all the simulation schemes used.
Estimation of model components and assessment of power The data simulating gene by environment interaction (Equation  2 ) were fitted using model 2. The model with two and three way interactions (model 3) was used to fit the data including the corresponding interactions (Equation  3 ). Model components were estimated using the REML method  [43]. The fixed effects were significantly tested using the incremental Wald test procedure implemented in the ASReml package  [43]. The power of each model was estimated for each parameter combination as the proportion of significant tests (P < 0. 05) out of the total number of tests.
The maize and the pearl millet panels Results of independent field experiments were available for two panels (maize and pearl millet). Maize data came from Cornell University (Ithaca, New York). Pearl millet data came from the Institut de Recherche pour le Développement (IRD, France and Niger). For pearl millet, the number of days from sowing to the female flowering stage was scored on a panel of 90 inbred lines. Nine field trials were carried out at different sowing dates throughout the rainy season, over a period of four years (2005 to 2008). For this dataset, we use the term trial to refer to the replicate of the field experiment at a given sowing date. All trials were conducted at the same location (Sadoré, Niger). Three of these trials  (2005) (2006)  and the experimental design are described in detail in a previous paper  [17].
The maize panel contained 277 inbred lines. Flowering phenotype (days to silk) was recorded in eight environments in the United States  [34]. Hereafter, we use the term trial to refer to the maize experiment in a given year of experimentation at a given location. In this study, only seven trials were used, due to missing data in the eighth trial. For each inbred, the best linear unbiased predictor (BLUP) from each trial was used. Ancestry estimates were also available from a previous study which clustered maize inbreds into three populations: NonStiffStalk (NS), StiffStalk (SS) and Tropical (TS) populations  [46]. Kinship coefficients were based on a set of previously described background markers  [46]  and calculated using the method implemented in the package EMMA  [47]. The genotype score for a MITE insertion at the Vgt1 locus was also available from a previous study  [34].
Statistical analyses A common set of five competing models were defined to fit each of the maize and pearl millet datasets (Table  2 ). All five models were nested and differed from each other by one or more terms. For each panel, we compared the result of model selection using the Wald test (WLD)  [48]. We also assessed the possibility to use information criteria (Additional file 1) and we finally used the Corrected Akaike Information Criterion (AICC)  [49]  and the Bayesian Information Criterion (BIC)  [50]. AICC is an efficient information criterion and BIC is a consistent information criterion. For WLD, a larger model was considered better if at least one of the specific terms added in this model was significant (P < 0. 05). To be more conservative for candidate gene association, we systematically set main effects and interactions involving environment or population background prior to effects involving candidate genes.
Results Power of the mixed model with a gene by environment interaction We used the simulated data to measure the power of mixed linear model to detect gene main effect and gene by environment interaction (Figure  2 , Additional file 1: Figure  S1 ). The power increased with the genetic effect ratio r and with the allele frequency in both maize and pearl millet samples. The power was highest with allele frequencies of 0. 5 and 0. 25 and with high heritability (h 2 = 0. 75). Also, the power to detect the interaction was more sensitive to the coefficient λ; this was expected because λ measures the increase in the effect of the SNP from the first environment to the second. The power was very low for the detection of gene by environment interactions when low heritability was combined with low allele frequency and/or low effect size.
The global pattern of power variation with the parameters (r, λ, q) was consistent for both datasets (maize and pearl millet), but the maize sample performed globally better than the pearl millet sample in terms of absolute power. So in the maize sample, environmental interactions underlined by common alleles with relatively strong effects were still detectable at h 2 = 0. 25 and also the detection of rare alleles (q = 0. 05) was better than that in pearl millet (Additional file 1: Figure  S1 ). Finally, we calculated the simulated effect size resulting from the combination of parameters (Additional file 2: Table  S1 ). For instance, SNP by environment interaction effects of 3. 05 days and 4. 44 days were detected for the flowering trait with a power of about 95% in both maize and pearl millet (for h 2 = 0.
As allele frequency is a proportion (i. e. a numeric value between 0 and 1), this combination is necessarily lower than any of the original variable frequencies. Furthermore, these simulations suggest that Pearl millet (n=90) Maize (n=277) S S x E Q x S Q x S x E Figure 3 Power of the mixed linear model to detect phenotypic effects including three way interactions. The data were simulated using pearl millet and maize panels (see text for details). The set of simulated effects consists of SNP main effect (S), SNP by environment interaction (S × E), ancestry by SNP interaction (Q × S) and three way interaction between ancestry, SNP and environment (Q × S × E). The power to detect each effect is plotted for heritability h 2 = 0. 75 and the allele frequency q = 0. 5, according to the effect ratio r, and the parameter λ which influences variations in the effect with the environment.
Finally, we observed that the absolute level of power was improved in the simulations based on maize data compared to pearl millet. Although other properties of these two panels differed (e. g. population structure, trait variance), the improvement in power was in great part due to the effect of the larger sample size in maize (three times larger than the pearl millet sample). But despite this difference in the absolute value of power estimates, it is important to note that the pattern of power variation, as a function of allele frequency, heritability or effect size, was consistent for the two panels. To explore more parameters spaces in terms of genetic background configuration, allele frequency or sample size, the use of simulated panels based on theoretically defined parameters (rather than real panels) will be more suited.
But this is not implemented in the current study. Statistical limits to the analysis of complex interactions using small panels were revealed in when we used very low allele frequency for the smaller dataset (pearl millet data). The number of inbred baring the allele is then too low and the reliability of model components defined on the basis of rare combinations is statistically questionable. Taking all the simulation results together, it appears that no single parameter determines the power of the model and that the effects of all parameters might balance each other. For instance, a good allele frequency does not necessarily imply good power when heritability is insufficient, and reciprocally. In this connection, the power of the MLM framework for a particular set of data needs to be discussed with respect to the combination of data characteristics, instead of focusing on only one or two parameters.
Statistical evidence in maize for two types of interactions The result of model selection in maize data was very close between methods. This good consistency between the methods for this relatively large sample (n = 277) is actually expected in the light of our simulation study (Additional file 3). Two types of biologically important interactions are highlighted by these results. First, we found significant interactions between maize ancestry and environment. This suggests that differences in the background of the three maize populations led to a differential reaction to environment variation. The effect of the interaction between population structure and the environment suggests that these populations respond differently to different environments. The analysis population structure, we actually analyzed an unknown number of loci co-varying with the population structure.
Conclusion We used mixed linear model to discuss the statistical perspective of using association mapping panels to study interaction effects in plants. First, this study highlighted the need of large sample size to ensure best model selection and high power. Most of the association panels of plants species to date are composed of a fairly small number of individuals, so larger panels are to be recommended in the future to deal better with G × E interactions. Second, we showed a strong impact of data parameters on the power, with a drastic loss of power when allele frequency, heritability, or effect size, are low. This shortcoming reinforces the need of complementary methods to deal with rare alleles or alleles with small effects. However, we illustrated cases of success of the current mixed model framework by identifying, in the maize association mapping panel, two types of biologically interesting effects of interaction between genetic factors and environment.
Acknowledgement This study was supported by a travel grant from the Agropolis Fondation to AAS; by a grant from the National Research Agency of France (ANR-07-JCJC-0116 to YV); and is partly funded by the Agropolis Fondation "Agropolis Resource Center for Crop Adaptation and Diversity" (ARCAD) project. AAS was supported by IRD Ph. D. fellowship and performed part of this research in Cornell University (February-April 2009). We thank Edward S. Buckler, Peter Bradbury, Elhan Ersoz and Zhiwu Zhang for discussion and help during modelling and statistical analyses. We acknowledge Alain Charcosset, Jacques David and Laurène Gay for discussion about the preliminary versions of this paper. We thank Jean-Louis Pham for advice during project design.
Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and wholecohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union's Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases.
& common infrastructures and data elements for rare disease patient registries; & common standards and catalogue for rare disease biobanks; and & best ethical practices and a proposal for a regulatory framework for linking medical and personal data related to rare disease. RD-Connect will initially incorporate data generated by two associated projects: EURenOmics, which uses multiple approaches to focus on causes, diagnostics, biomarkers, and disease models for rare kidney disorders such as steroidresistant nephrotic syndrome and tubulopathies; and NeurOmics, which uses novel molecular approaches to improve diagnosis and treatment of rare neurodegenerative and neuromuscular disorders such as Huntington's disease and muscular dystrophies. RD-Connect unites existing infrastructures and integrates the latest tools to create a comprehensive platform for biobanking, data analysis, and patient registry for researchers across the world.
While such integrative efforts are ongoing within some medical centers, individual efforts often remain largely siloed. This is a critical problem in rare disease studies, where a given center may see only a small number of patients with a certain disease. Linking such data sets across centers and across diseases is thus an essential step. Outside the rare disease field, a number of major research infrastructures, including the International Cancer Genome Consortium and the International Human Epigenome Consortium, have shown the feasibility of robust tools for large-scale data and sample sharing across multiple research projects. 5 o address this issue, major medical research funders have come together in a global effort to foster collaboration in rare disease research. The International Rare Diseases Research Consortium (IRDiRC) was launched in 2011 and now has 35 members worldwide, including the European Commission as well as key national funders, such as several institutes in the US National Institutes of Health.