dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the typically low numbers of replicates, given the cost of the technology, affects variance estimations. To circumvent the statistical limitations associated with the size of the datasets, the strictness of the threshold used to filter significant genes is increased as the number of replicates decreases  [17]. However, a stricter threshold (type I error) decreases the power of the test (type II error), thereby increasing the rate of false negatives and limiting the detection of DEGs to those that are most obvious. Many authors have significantly improved the performances of such statistical analyses by solving methodological issues  [18] [19] [20] , and several performance benchmarks have been implemented to rank these methods  [21] [22] [23]. However, the low positive and negative predictive power still remains a major issue  [23].
Second, downstream data analysis and interpretation rely heavily on the availability/quality of knowledge resources for the genome of the species studied. In particular, the annotation of the probes and the amount of genomic knowledge, including ontologies and pathways, determine downstream data contextualisation. Nowadays, the constant evolution of genomic resources and the implementation of public databases have considerably improved the potential to infer and describe gene and protein networks, and shed light on their effect on patterns observed. Though such tools have been extensively used and validated in human-related genome-wide studies, the annotation/contextualisation of datasets regarding less studied organisms is more questionable. As an example, the full genomes of five fish species have been sequenced (namely zebrafish Danio rerio, stickleback Gasterosteus aculeatus, tetraodon Tetraodon nigroviridis, medaka Oryzias latipes and Fugu Takifugu rubripes)  [24]  and the sequencing of the genome of other species is still in progress (e.
Among them, DAVID  [28]  performs a test derived from the Fisher Exact Test, called EASE. The enrichment p-value calculation (EASE-score) adapts the Fisher exact probability to the particularities of microarray data, with penalisation of the pvalue for categories represented by only a few genes  [29]. As highlighted by the growing number of dedicated software (i. e. DAVID/EASE  [28, 29] , GoMiner  [30] , FatiGO  [31] , GenMAPP  [32] ), this approach is now currently used in microarray studies, due to its extraordinary potential to enhance the whole dataset analysis. However, the relevancy of absolute p-values remains questionable, as we will point out in the discussion section. Though any of these methods alone can solve the statistic issue presented above when applied to experiments generating large lists of DEGs, we have described workflows  [33] [34] [35]  that progressively filter the output of the statistical analyses.
3. 4. 2. Microarray hybridisation and raw data production. The samples were hybridised on the microarray slides with incubation at 65°C for 17 hours in a hybridisation chamber (Agilent). The slides were scanned and pre-processed (signal background corrections) using Agilent's High-Resolution C Scanner. Raw data are available in S1 Dataset. 3. 4. 3. Statistical analysis. The data analyses were performed using the R statistical software version 2. 15. 3. available on the R-Project repository (http://cran. r-project. org) and a set of packages available in the Bioconductor repository (http://www. bioconductor. org). Brief descriptions of the different steps of the analyses are provided below. Detailed scripts are presented in S1 Script. The first steps were pre-processing and normalisation procedures, in which the expression values of the one-color microarray were first submitted to quantile-quantile normalisation using the normalize.
quantile function in the preprocessCore package. Normalised data are available in S2 Dataset. Detailed script is available in S1 Script. a. In a second step, an analysis of variance (ANOVA) was conducted on the normalized data. Variations in expression levels between replicate tanks (3 tanks per condition) were performed Table  1. Summary of the histological results obtained in a previous study  [37]. The results are presented as the percentage of gonad phenotypes observed in each concentration of EE2 tested. All the control fish displayed immature testes. Morphological disturbances were observed from the first concentration used (0. 01 μgEE2/L) and complete sex reversal was observed from the 0. 1 μgEE2/L concentration. Gonads morphologies were classified into 4 classes as follow: testicular (gonads displaying the histological features of a differentiated yet immature testis), ovotestis (phenotype characterized by the presence of oocytes within the testis), and ovarian-like (gonads displaying the characteristics of an immature ovary).
Several fish displayed an altered testicular morphology. by pair-wise comparisons in R using the lmFit function in the Limma package available in Bioconductor (S1 Script. b. ). We then performed a one-way ANOVA, with the EE2 concentration as the criterion, using the lmFit function of the Limma package (S1 Script. c). The third step consisted on post-hoc comparisons. A post-hoc evaluation was performed using Scheffe's method with the makeContrasts() and eBayes() functions provided in the Limma package (10 contrasts comparing all experimental conditions). The results were summarised using decideTests() and summary() (number of significant genes positively or negatively regulated for each evaluated contrast). The Benjamini-Hochberg procedure was used to adjust the p-values (correction for multiple tests)  [39].
Phenotype Following post-hoc comparisons, an analysis of intersections between selected contrasts was done. The set of common DEGs (S1 Script. e) between the top table of contrasts was defined using the intersect() function of the R package 'stat'. MAC OS 2011 (14. 2. 4) Excel software was used to select genes specific to a given intersection, by deleting those that were also differentially expressed in the other contrasts (see methodology for details). The next step was devoided to the annotation of the array. There is no consensus for annotation of the Agilent 60K array. Bioinformatics tools have specific requirements with regards to gene identifiers (IDs) and traditionally need homogenous and unique identifiers. In addition, the annotation of nucleotides sequences continues to improve and can substantially differ within several months (regular updates).
Therefore, the most recent annotation should be used to ensure the accuracy of the analysis. Ensemble gene IDs for Danio rerio (ENSDARG) were used to submit queries to the DAVID pathway analysis interface. The most recent gene symbols were used to analyse the data with EASE. The latest release of the trout INRA-Sigenae program (http://www. sigenae. org/, January 2013) was used to annotate the data using the UniProt  [40]  and e!Ensembl  [24]  databases. When all DEGs with accurate annotation were obtained, a step of data visualisation was performed. We used a set of bioinformatics tools for the downstream analysis of differential expression to examine the biological context specific to the different levels of organisation (gene expression, pathways, ontologies), guided by the significance of the sets of DEGs tested.
First, hierarchical clustering was performed using Cluster 3. 0 (C clustering library 1. 49) on the DEG highlighted by the statistical gene analysis (ANOVA) (available on http://bonsai. hgc. jp/m dehoon/software/cluster/software. htm). The parameterisation of the hierarchical clustering was defined (i) to use the log-level procedure, (ii) to center the genes and arrays on zero, (iii) to cluster both genes and arrays, and (iv) to use the centroid linkage (average linkage) as the distance metric. The Java TreeView software (version 1. 1. 6r2) [41] , available on http://jtreeview. sourceforge. net/ was used to visualise the results. A gene list was submitted to the tools available on the DAVID web interface (Database for annotation, Visualisation and Integrated Discovery)(http://david. abcc. ncifcrf.
gov/home. jsp) [28, 42] , to identify and illustrate pathways defined in KEGG (Kyoto Encyclopedia of Genes and Genomes) as describes previously  [33]. The background population was defined based on the genes targeted by the Agilent rainbow trout 60K array. A gene list was submitted to the EASE software (Expression Analysis Systematic Explorer), version 2. 0 available on the DAVID website  [29]. EASE is standalone software covering a wider range of gene identifiers (as compared to DAVID tools), and is able to handle the heterogeneous Swissprot annotation (orthologs from several species). Gene symbols were entered into EASE to proceed with the analysis of over-represented categories (the basic analysis was run on a non-redundant fully annotated data table). The quality of the analysis was illustrated in R with volcano-plots (p-value vs.
fold-change). The raw data and a normalised expression file are available through the accession number GSE58519 at the GeneOmnibus public data repository (http://www. ncbi. nlm. nih. gov/geo/). 3. 4. 4. Validation of microarray data by real-time quantitative PCR experiment. Six genes among the oligonucleotides spotted on the chip, known as markers of early testicular development (sox9a2, dmrt1, cyp11b) or for their altered expression following estrogenic treatment (vtg, esr1a, esr2b) were selected for further analysis by real-time PCR, in order to validate the gene expression patterns obtained through the microarray approach. Real-time PCR analyses were performed as previously described  [37]. Briefly, RNA was collected, purified, quantified, and stored as described in "RNA extraction" above (section 4.
Methodology In this section, we describe the specific development of the microarray analysis workflow to fit the particularities of the data collected. The workflow here, which was adapted from a workflow developed by our team on human Affymetrix platforms  [33, 34]  and successfully adapted to another study  [35]  upon the same platform, was designed to handle a more complex experimental design (number of conditions) and the downstream annotation/pathway analysis which required that the specificities of fish species and the availability of genomic knowledge resources be taken into account. Progressive filtering procedures were applied to the raw dataset obtained from a microarray study to restrict the size of the gene lists and to enrich them in true positives and biologically relevant DEGs (Differentially Expressed Genes).
We then started with the gProcessedSignal data Agilent files. Normalisation is an important step that balances several replicates appropriately to allow for meaningful biological comparisons of expression levels. Several methods have been developed to normalise the data. We selected the quantile normalisation procedure that has been used extensively by the scientific community to scale datasets generated with one-color microarray platforms  [47]. This method defines a shared distribution of mean intensity values computed from the quantiles of array-specific distributions of intensity values  [48]  and has been reported to ensure robust downstream statistical comparisons between arrays  [47, 49]. ii. The use of visualisation methodologies that sort the DEGs by expression profile, such as clustering methods  [25]  (see results), greatly helps reorder the dataset and focus on interesting groups of genes, but leaves the unsolved problem of low predictive power or even increases the risk of misinterpretation of the results.
Moreover, in our analysis, the number of DEGs was often too high to successfully use this procedure, as the clusters identified displayed so many genes that it was impossible to treat them manually. iii. As described above in the introduction, statistical analysis of microarray data is a non-trivial task and caution is required when performing this critical step. Most of the top-ranked methodologies involve optimisation of the variance estimation by sharing information across genes (i. e. the Shrinkage t  [19] , Windows t  [50] , Regularized t, Moderated t  [51] , etc. ). In our case, the experimental design involved more than 2 factor levels (5 concentrations). To handle this design, we selected the Limma R package  [51]  that implements generalised linear models using an empirical Bayes model to assess moderated t and F statistics.
Limma can be used to analyse both single and dual color microarray experiments. In addition, our previous benchmarks performed on a simulated dataset containing real data (biological variance) illustrated the quality of the results provided by Limma, which ranked second among 8 methodologies tested  [23]. To increase the predictive power of the analysis, we conducted experiments with n = 6 in each condition. However, for n = 6 and for a fold change of -0. 46 ± 3. 86, approximately 80% true positives (i. e. 20% false negatives) were found in gene lists contaminated by 50% false positives  [23]. Several filters were applied with varying stringency to focus on the more interesting genes (among the thousands of DEGs from the statistical output). The first filter operated on all DEGs revealed by the ANOVA was to apply Scheffe's post hoc pairwise comparisons between the experimental conditions.
v. The next step performed to further enrich the list of DEGs with true positives used overrepresentation analysis methods (ORA). These methods identify sets that are the most significantly observed in the top list of differentially expressed genes, compared with any other gene-set definition. Such sets of genes are defined based on criteria of interest for annotation and interpretation of the results (Ontology, Pathways, Transcription factor targets. ). We previously used DAVID  [28]  to analyse the representation of pathways on cancer datasets and reported the discovery of the implication of several pathogen recognition pathways  [33, 34]  and the spliceosome  [35]  in the metastatic phenotype under hypoxia. Enrichment analysis tools start with definition of the appropriate sets of genes (pathways, ontology,.
Hierarchical clustering on the whole dataset From the statistical analysis, 29,250 probes (20,718 non-redundant) appeared as differentially expressed (DEGs) in at least one tested condition (p < 0. 05). In order to visualize these DEGs, a hierarchical clustering of the selected genes has been performed in Fig 1. It is interesting to note that EE2 concentration (columns) is associated with specific patterns of gene expression. By cutting the y-axis under the second level, we outlined three clusters: (i) the first cluster included the controls and the 0. 01 μgEE2/L concentration; (ii) a second cluster grouped 4 samples of the 0. 1 μgEE2/L concentration and (iii) a third contained two 0. 1 μgEE2/L samples and all replicates at the higher concentrations (1 and 10 μgEE2/L). At the gene level (rows), two major clusters appeared with genes under or over expressed between the low (0.
First, we only focused on genes for which the mean signal was up to the median in at least one experimental group. Second, to focus on transcripts with relevant regulation, we fixed a threshold of 1. 5 for the fold change between tested samples. Last, we selected highly significant genes (p-value < 0. 01). The resulting lists of DEGs built from both contrasts were called "Stringent intersection" and included 1,208 DEGs. "Specific intersection" focused on DEGs that were specific to the CT1-CT2 intersection, which were expected to be specifically associated with the development of intersex features at the morphological level. We thus discarded those DEGs that were differentially expressed in the other contrasts (CT3 and CT4): 300 DEGs belong to this group. Volcano plots are provided in Fig  3  for the three groups of genes defined above (total, stringent and specific intersections) for each contrast to illustrate the selection applied to the dataset.
ENSDARG annotation refers to the ensembl database identifiers for Danio rerio genes. were accurately annotated with an ENSDARG ID, and DAVID recognised 2,159 of them. The background was made up of 16,977 ensembl IDs. A total of 90 pathways containing at least 2 genes were identified from the remaining 2,159 genes (S1 Supporting Information). Among these pathways, 11 were detected with a significant enrichment in the list of DEGs submitted, with an EASE score below the threshold of 0. 05 (Table  2 ). Only one pathway (the cell cycle) could be detected when the threshold was set to adjusted p-values, after the very conservative multiple testing Benjamini correction (see Table  2 ). Most of these enriched pathways involved Metabolism, and especially that of Carbohydrates (Glycolysis, Propanoate), Lipids (Fatty acid and Glycerolipid) and Amino acids (Valine, leucine and isoleucine degradation).
The two sets of candidate biomarkers displayed different patterns of expression. The first group includes genes with peak expression in the CT1 and CT2 contrasts, and weak or no expression in CT3 Table  3. Results obtained for the ontology enrichment analysis approach. Significantly enriched GO terms (EASE score < 0. 01) retrieved by EASE software for the "Stringent intersection" group in this analysis  (1, 208  gene IDs restricted to a set of 868 well annotated differentially expressed genes recognized by Gene Symbol identifiers in DAVID). This group represents the common genes differentially expressed following chronic steroid modulation of immature gonads in rainbow trout at 0. 01 and 0. 1 μgEE2/L. They were retrieved from the intersection between two contrasts (CT1 and CT2) represented by a majority of fish displaying intersex gonads, after a more stringent filtration of the dataset.
The strength of this methodology is that it follows a step-by-step procedure, from general to more specific, depending on the bioinformatics tool used and using adapted lists of DEGs. Based on previous considerations, we choose CT1 and CT2 contrast intersection for the downstream analyses, using more stringent filter criteria which creates two groups of genes, named "Total intersection" and "Stringent intersection". The use of DEGs common to these two contrasts focuses on a smaller group of genes representing potential genetic signatures of the intersex stage. The improvement of the dataset obtained between the "Total" and "Stringent" intersection groups of DEGs can be visualised in the Volcano plots (Fig 3AB  -3CD ), with a majority of the stringent DEGs being located on the edges of both the CT1 (Fig 3C  )  and CT2  (Fig 3D ) graphs.
Monte Carlo samples Monte Carlo simulation samples are used to correct the data for acceptance and efficiency and for the V + jets processes. They are also used to estimate the background to the Z + jets signal. The Z + jets signal is generated with the MADGRAPH (version 5. 1. 3. 30) [17]  program. The leading-order multiparton matrix element (ME) calculation includes up to four partons (gluons and quarks) in the final state. The showering and hadronization of the partons, as well as the underlying event, are modeled by PYTHIA (version 6. 4. 26)  [18]  with the Z2 * tune  [19]. The k T MLM matching scheme  [20]  with a matching parameter of 20 GeV is applied to avoid a double counting of final states arising in the ME calculation and the parton shower (PS). The events are generated with the CTEQ6L1  [21]  parton distribution functions (PDF) and rescaled using a global next-to-next-to-leading-order (NNLO) K-factor to match the inclusive cross section calculated with FEWZ 3.
1  [22]. Backgrounds to Z + jets are generated using MADGRAPH with the same configuration as the signal events. These include top quark-antiquark pairs (tt) and EW backgrounds, such as W + jets and diboson processes (WZ, ZZ, WW). The Z + jets signal and tt background processes are also generated with SHERPA (version 1. 4. 2)  [23] , using the CT10 PDF  [24]. The cross section for the signal is also rescaled using a global next-to-next-to-leading-order (NNLO) K-factor. For the tt background, the NNLO calculation provided by Ref. [25]  is used to calculate the NNLO K-factor. The background yields from EW processes are rescaled using the MCFM  [26]  NLO cross sections. In addition to these general purpose MC signal data sets, we use an NLO perturbative QCD calculation of Z + jets from the BLACKHAT Collaboration  [9] , which is available for a Z boson accompanied by up to three jets.
The function H (δ) is chosen such that it vanishes as δ → 0. In particular, H (δ) = E γ T 1 -cos δ 1 -cos δ 0 n. The Frixione cone in effect only adds contributions from partons which are within δ 0 of the photon. In the BLACKHAT samples, = 0. 025, δ 0 = 0. 4, and n = 2. These were selected because a Frixione cone with these choices mimics the selections in the true on-shell photon 4 Event selection and object reconstruction definition at particle level. Photon distributions using Frixione cone requirements are found to agree with those using cone isolation to within 1-2%  [32]. The simulation of the CMS detector is based on the GEANT4 package  [33]. The simulated events used for the detector level MC estimations are reconstructed following the same procedures used for the data. For our run, the average number of inelastic proton-proton collisions occurring per LHC bunch crossing was 21.
For photon candidates, a match with a corresponding trigger object is required. For this analysis, only isolated high-p T photons located inside the barrel region of the detector (|η| < 1. 4) are considered. We concentrate on photons inside the barrel region because the data size of the templates, described in Section 5. 2, allows for a precise purity determination in this region. Around 40% of the photons convert into e + e -pairs inside the tracker material. Conversion track candidates are fitted from a combination of ECAL seeded tracks and Gaussian sum filter  [38]  electron tracks originate from a common vertex. The track pair is then matched to energy clusters in ECAL to identify a converted photon candidate. The final photon candidates are checked for possible overlap with electron candidates by looking for electron track seeds in the pixel detector or by using the characteristics of the track pair for converted photons.
The jets are clustered by four-momentum summation. The reconstructed PF candidates are calibrated separately to account for the nonlinear and nonuniform response of the CMS hadron calorimeter, especially for neutral hadrons. Charged hadrons and photons are well measured in the silicon tracker and the ECAL, and therefore need only minimal corrections. Thus, the resulting jets require only small additional momentum adjustments. Jet energy corrections are obtained using GEANT4 simulated events generated with PYTHIA6. The energy contributions due to the presence of additional proton-proton interactions are subtracted from each jet using the measured pileup unit density in the event and the jet area  [36]. The η dependent corrections are adjusted using exclusive dijet events, while the p T -dependent corrections are adjusted using exclusive Z + 1-jet and γ + 1-jet events in the data  [41].
The rate of events gained with respect to the baseline selection is largely dominated by diboson events. These additional data events are compared to estimations from MADGRAPH, finding an agreement within roughly 10% for all jet multiplicity phase space selections. This comparison is done in the range p T < 300 GeV. The same selection criteria from data are used at the particle level: leading leptons are required to have p T > 20 GeV and |η| < 2. 4, while jets are required to have p T > 30 GeV within the region of |η| < 2. 4. The particle level jets in simulation are obtained by clustering the generated stable particles (after hadronization and including neutrinos) using the anti-k T algorithm with distance parameter of R = 0. 5. Electrons and muons have different energy losses due to final state radiation at particle level.