dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation and results DIANE is an R Shiny  [22, 23]  application available as an online web service, as well as a package for local use. To perform relevant bioinformatic and bio-statistical work, different existing CRAN and Bioconductor packages as well as novel functions are brought together. Its development was carried out via the golem  [24]  framework, allowing a modular and robust package-driven design for complex production-grade Shiny applications. Each main feature or analysis step is programmed as a shiny module, making use of the appropriate server-side functions. In the case of local use, those functions are exported by the package so they can be called from any R script to be part of an automated pipeline or more user-specific analyses. We also provide a Dockerfile  [25]  and instructions so that interested users can deploy DIANE to their own team servers.
Organism and gene annotation Several model organisms are included in DIANE to allow for a fast and effortless annotation and pathway analysis. For now, automatically recognized model organisms are Arabidopsis thaliana, Homo sapiens, Mus musculus, Drosophilia melanogaster, Ceanorhabditis elegans, and Escherichia coli. DIANE takes advantage of the unified annotation data for those organisms offered by the corresponding Bioconductor organisms database packages  [26] [27] [28] [29] [30] [31]. Other plant species are annotated such as white lupin, and users can easily upload their custom files to describe any other organism whenever it is needed or possible along the pipeline. Organism specific information needed can be common gene names and descriptions, gene -GO terms associations, or known transcriptional regulators.
GO enrichment analysis Among a list of DEG, it is of great interest to look for enriched biological processes, molecular functions, of cellular components. This functionality is brought to DIANE by the clusterProfiler R package  [38] , that employs Fischer-exact tests on hypergeometric distribution to determine which GO terms are significantly more represented. Results can be obtained as a downloadable data table, a dotplot of enriched GO terms with associated gene counts and p-values, or as en enrichment map linking co-occurring GO terms.
Gene clustering Method In order to identify co-expressed genes among a list of DEGs, DIANE enables gene expression profiles clustering using the statistical framework for inferring mixture models through an Expectation-Maximisation (EM) algorithm introduced by  [9, 10]. We chose to use the approach implemented in the Bioconductor Coseq package  [11]. Coseq makes it possible to apply transformation to expression values prior to fitting either Gaussian or Poisson multivariate distributions to gene clusters. A penalized model selection criterion is then used to determine the best number of clusters in the data. With DIANE, users simply have to select which DEG should be clustered among previously realized DEA, the experimental conditions to use for clustering, as well as the range of number of clusters to test.
Exploring the clusters Once clustering was performed, a new tab enables a detailed exploration of the created clusters. It includes interactive profiles visualization, downloadable gene data table, GO enrichment analysis. In addition, if the experimental design file was uploaded, Poisson generalized linear models are fitted to the chosen cluster in order to characterize the effect of each factor on gene expression. To validate and extend the work done around our demonstration dataset, we performed clustering analysis similarly to what was done in the original paper  [21]. We considered all genes from the seven DEA computed between control and perturbation treatments, with a 0. 05 FDR threshold and an absolute LFC above 2. Figure  4  presents the clusters of interest as given by the Poisson Mixtures estimation.
They provide a gene partitioning representative of all behaviors in the dataset. In particular, we found that the 3 biggest clusters  (2, 3, 6)  were composed of heat responsive genes. Among those clusters, statistically enriched GO terms are in majority Fig. 4  Clustering of combinatorial RNA-seq data with DIANE. Clusters of interest generated by Coseq in DIANE. Gene expression profiles are defined as the normalized expression divided by the mean normalized expression across all conditions. Graphical results of ontology enrichment analysis are presented for clusters 3 and 5. Highlighted ontologies are relevant categories in line with previously published findings  [21]. Ontology enrichment plots show detected GO terms (under 0. 05 in Fischer's exact tests), color-coded by their adjusted p-value, and shifted in the x-axis depending on the number of genes matching this ontology linked to heat and protein conformation.
They provide performance metrics for 27 methods based on regression techniques, mutual information metrics, correlation or Bayesian framework among other methods. The performance metrics gathered by DREAM5  [45]  (i. e Area Under Precision and Recall curves or overall scores), as well as more recent efforts to compare new methods on those gold standards (i. e F-meausres, ROC curves) are useful resources to help making a choice. For example, existing methods to learn GRN structures are WGCNA  [12] , ARACNE, CLR, TIGRESS, GENIE3 (see  [45]  for an exhaustive and referenced list of methods), or also SORDER  [46]  or CMI2NI  [47]. In DIANE, the package chosen for GRN reconstruction is GENIE3  [16] , a machine learning procedure that was among the best performers of the DREAM challenges. GENIE3 uses Random Forests  [20]  which is a machine learning method based on the inference of a collection of regression trees.
Empirical p-values are computed for the selected regulatory weights. To assess weather the importance value of a pair is significant or not, the rfPermute package  [57]  fits Random Forests and repeatedly shuffles the target gene expression profile so that the null distribution of each regulator influence is estimated. Hence, the empirical p-value of a regulator-gene pair is given by the extremeness of its importance as compared to the estimated null distribution. For a faster and more exploratory-oriented network inference, it is possible to skip edges testing (this step and the following). FDR correction for multiple testing  [58]  is applied to the p-values, and only the edges above an FDR threshold are kept to form the final network. After edges statistical testing, graphics that show the p-values distribution and the final number of edges depending on the FDR choice See Additional file 1 for more details on the statistical procedure and implementation.
Benchmark of the proposed approach We benchmark this novel procedure designed to keep the most significant interactions from a complete GRN. As GENIE3's performance was already assessed in several comparative studies, we focus here only on the edges testing strategy, that we compare to a more naive approach, hard thresholding. To do so, we applied our edges selection strategy to GENIE3 edges ranking on two different datasets, for which robust regulator-gene validation information is available. The first expression dataset is the RNA-Seq experiment on Arabidopsis thaliana we present in this article. We inferred a GRN of heat responsive genes in all experimental conditions (1497 genes from C versus H DEA, LFC ≥ 1. 5, FDR ≤ 0. 05, containing 118 regulators). To validate the inferred connections, we made use of connecTF  [59] , a recent database containing regulatory interactions in Arabidopsis thaliana obtained from in vitro and in vivo binding experiments, as well as in planta regulation experiments.
We specifically chose to use the interactions in connecTF obtained from CHIP-Seq and TARGET experiments that represent the most robust data in order to validate connections. The second dataset is an experiment on Escherichia coli, generated by the authors of the "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles"  [60]. We restricted ourselves to a subset of this compendium of experimental conditions corresponding to a single combinatorial experiment. In the latter, bacteria were exposed to a control treatment or to norfloaxacin for different amounts of time, for a total of 24 experimental conditions. The 4345 genes of the organism provided in the dataset, containing 154 transcription factors, are used for GRN inference followed by edges testing.
In order to validate the connections of the networks generated in DIANE, we used RegulonDB  [61] , a database of regulatory interactions built from classic molecular biology experiments and more recently high throughput genomics such as CHIP-Seq and gSELEX. For each organism, we compared the validity of network predictions between two strategies. The first one corresponds to a network obtained by applying a hard threshold to GENIE3's weighted regulatory associations, to achieve a desired network connectivity density. The second strategy corresponds to that same network, but after removing the edges deemed spurious by our empirical testing procedure for edges selection. By doing so, we aim at determining weather refining edges with our testing procedure leads to networks of higher quality. The performance metric we chose to assess our method's performance is the precision.
The results are gathered in Fig. 6a  and b. They demonstrate that a significant increase of precision can be achieved on both datasets when choosing stringent adjusted p-values for edges removal, independently of prior density. This finding supports that p-values obtained from permutations on Random Forest importance metrics can allow more confidence in the inferred edges than hard thresholding GENIE3's fully connected network. Figure  6a  and b also illustrate the order of magnitude of the number of connections removed by the testing strategy. After using our empirical testing procedure for edges removal, we stored the number of remaining edges. We then applied hard-thresholding to GENIE3's ranking in order to create networks containing those same number edges. We observed that the precision of such networks was not as high as with our empirical testing.
A B Fig. 6  Benchmark of the proposed testing method on the E. coli and A. thaliana datasets. Boxplots compare the distributions of precision between hard-thresholding (green) and hard-thresholding followed by the removal of non significant edges as predicted by the testing procedure (purple). The 20 replicates for each configuration provide an estimation of the precision dispersion caused by randomness in GENIE3 and testing by permutations. For each organism, we investigate two appropriate connectivity densities, and three adjusted p-value thresholds (FDR). procedure. This reveals that our adjusted p-values bring more information than GENIE3's ranking only, even with a hard-thresholding resulting in the same number of final interactions. Figure  6b  shows computation times required to perform statistical testing on A.
thaliana dataset, as permitted by DIANE's online interface. DIANE's online version is hosted on a Debian 9. 13 server with a 256Go RAM, and 2 Intel(R) Xeon(R) Gold 6130 2. 10GHz CPUs. The parallel computing for online use allows up to 16 CPU cores (computation time reported in Fig. 6b  uses 16 cores). Altogether, this benchmarking analysis demonstrates an added-value in terms of network precision when edges selection is performed on the basis of p-values rather than by hard thresholding, for a limited time of computation.
The experimenter is then free to visualize the results in the network as color-coded communities, while exploring module-specific expression profiles and GO enrichment analyses. At last, it is possible to download edges and node information as csv dataframes, to be further investigated or opened in popular network visualization tools such as Cytoscape. We used the GRN features of DIANE in order to infer a GRN of the response to heat under osmotic stress, environmental conditions that plants are supposed to face more frequently under climate change circumstances. The input list of genes is obtained in DIANE, by calculating DEG between simple osmotic stress and the double heatosmotic perturbation (M versus HM, FDR < 0. 01, LFC > 2). 640 DEG are detected, among which 363 are upregulated, 277 are down-regulated, and 45 are transcriptional regulators.
Conclusions To summarise this work, we presented an online graphical user interface to easily conduct in-depth analyses on gene expression data from multi-factorial experiments, including gene expression profile clustering and GRN inference. It can be downloaded and installed seamlessly as any R package to run the pipeline locally or from R scripts. Given that all other graphical interface tools found in the literature are (i) more oriented toward coexpression rather than regulation and (ii) do not provide recent advanced methodological frameworks for pathway reconstruction, our application positions itself as a tool of first choice to explore regulatory mechanisms. The demonstration of DIANE on its companion dataset allowed to better understand the effect of combined heat, osmotic and salinity perturbations on Arabidopsis thaliana, consistently with the original analysis  [21].
First, combining the results of several inference methods has proven to be as a robust and powerful approach on validated datasets  [45, 52]. Second, our strategy is particularly well-suited for multi-factorial and perturbation designs, but is not optimal for time series RNA-Seq. Other inference methods specific to time series RNA-Seq data  [69]  could be available in DIANE, to bring closer to causality in the inferred transcriptionnal interactions. Lastly, it would be valuable to further functional features in DIANE, notably in order to integrate external information, such as interaction databases, or data from TF binding or chromatin accessibility experiments.
Availability of data and materials The RNA-Seq experiment we included to DIANE for demonstration purposes corresponds to the GEO accession GSE146206 and can be found at https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE146206. The code and benchmark scripts of DIANE are available in the github repositories https://github. com/OceaneCsn/DIANE and https://github. com/ OceaneCsn/Benchmarking_DIANE. DIANE largely relies on the CRAN https://cran. r-project. org/ and Bioconductor https://bioconductor. org/ packages repositories. The datasets queried to retrieve validated regulatory interactions are connecTF https://connectf. org/ and RegulonDB http://regulondb. ccg. unam. mx/. The expression data used to infer regulatory networks on Escherichia coli were taken from the Many Microbe Microarrays Database at http://m3d.
To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar. Theia's and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A).
This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with the WASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.
MAJA is based on the Multi-temporal Atmospheric Correction and Cloud Screening software (MACCS)  (Hagolle et al. , 2015)  and includes a few modules from the AT-COR software  (Richter et al. , 2006). Compared to classical processors based on multi-spectral relations to detect clouds and estimate aerosol content, MAJA also involves multi-temporal criteria, which assume that land surface reflectance tends to change slowly as compared to the atmospheric effects due to clouds and aerosols. To our knowledge, MAJA is the only Level-2A processor using multi-temporal criteria, it is therefore using more information to detect clouds and aerosols than the other methods. But even with accurate surface reflectance and cloud masks, Sentinel-2 Level-2A data usage is still complicated and requires specific technical skills which are not possessed by all users.
The presence of clouds causes spatial and temporal data gaps in the time series that have to be handled by automatic processing, and the swath of Sentinel-2 satellites is limited. It is therefore impossible to observe a large region in one overpass, and users have to rely on data acquired on different dates with different cloud covers. Merging different acquisitions may cause artifacts at the boundaries of swaths or around the data gaps due to clouds. Overcoming this difficulty is the role of the Level-3A product (L3A), which provides a monthly synthesis of cloud free surface reflectance. Since a few years, Theia and EOC have been distributing Level-3A products to users based on the Weighted Average Synthesis Processor (WASP), which calculates a weighted average of cloud free surface reflectances after a directional correction to normalize data acquired from different swaths.
MAJA and WASP processors are being used by CNES and DLR, within the Theia  (Theia, 2020)  and EOC data centers (EOC, 2020) respectively, to produce in near real time Sentinel-2 Level-2A and Level-3A products. These products have been delivered to more than 2500 users so far, and cover about a surface of 10 Million km 2 for THEIA and 0. 4 Million km 2 for EOC (coverage of Germany). On average, each product generated has been downloaded 1. 3 times, counting only real single users and not redistribution platforms. MAJA can also be applied to other satellites such as LANDSAT  (Loveland, Dwyer, 2012) , with a 16 day revisit, or VENµS with a two day revisit  (Ferrier et al. , 2010). Validation is a key element to strengthen trust on an ARD product. CNES made significant efforts to obtain reference data and in-situ measurements, and participated to the atmospheric correction intercomparison international experiments (ACIX)  (Doxani et al.
MAJA uses this feature only for VENµS sensor. Due to this parallax, it is necessary to dilate the cloud masks, but Sen2cor does not implement a dilation because it would also dilate the frequent false detection of clouds over buildings. In order to compare similar cloud masks, the statistics provided below include a buffer of 240 m for the reference and evaluated cloud masks. The overall accuracy per image is provided on Figure  2. The average overall accuracy for the whole data set ranges from 91% for MAJA, 90% for FMask and 84% for Sen2cor. Even if these percentages are quite high, it has to be noted that an overall accuracy of 84% corresponds to 16% of errors, which is a large amount. For more detailed analysis, please refer to  (Baetens et al. , 2019).
Surface reflectances The actual output of Level-2A processors are surface reflectances, which is the quantity that should be validated. Nevertheless, the available sites with daily measurements of surface reflectances which can be expressed in the same observation geometry as Sentinel-2 satellites are very scarce. The ACIX-2 experiment only relied on two sites whose data are collected by CNES, La Crau, France, and Gobabeb, Mauritania. These two sites have been set-up to validate the absolute calibration of optical sensors, and they were chosen to minimize all error sources. As a result these sites are very uniform and are situated in regions with a generally low AOD. They also have quite high reflectances, especially Gobabeb, which makes them less sensi-tive to the atmosphere. In 2021, CESBIO will implement a new site in Lamasquere, in the South of France, to validate surface reflectances in more difficult conditions, with lower surface reflectances and less uniform landscapes.
CONCLUSIONS To enable an easier uptake of remote sensing data by users, space agencies and data centers have developped Analysis Ready Data that spare users the burden of pre-processing the data. These tasks are one of the missions of the French and German land data centers, Theia and EOC. Both centers provide users with high quality ARD products generated with MAJA and WASP processors. A large scale validation effort is made to collect in situ data and assess the quality of these products. These validation results show that the detection of valid pixels (without clouds and cloud shadows) is accurate to 91%, AOD is measured with an uncertainty of 0. 14 when taking into account heavy AOD cases. It is better than 0. 1 if AOD cases above 0. 6 are discarded. Surface reflectance errors are in the order of magnitude of 0.
The Sea Ice Evaluation Tool (SITool) described in this paper is a performance metrics and diagnostics tool developed to evaluate the skill of Arctic and Antarctic model reconstructions of sea ice concentration, extent, edge location, drift, thickness, and snow depth. It is a Python-based software and consists of well-documented functions used to derive various sea ice metrics and diagnostics. Here, SITool version 1. 0 (v1. 0) is introduced and documented, and is then used to evaluate the performance of global sea ice reconstructions from nine models that provided sea ice output under the experimental protocols of the Coupled Model Intercomparison Project phase 6 (CMIP6) Ocean Model Intercomparison Project with two different atmospheric forcing datasets: the Coordinated Ocean-ice Reference Experiments version 2 (CORE-II) and the updated Japanese 55-year atmospheric reanalysis (JRA55-do).
The simulated sea ice areal properties are also improved in the model with higher spatial resolution. For the cross-metric analysis, there is no link between the performance in one variable and the performance in another. SITool is an open-access version-controlled software that can run on a wide range of CMIP6-compliant sea ice outputs. The current version of SITool (v1. 0) is primarily developed to evaluate atmosphere-forced simulations and it could be eventually extended to fully coupled models.
These sea ice metrics give a detailed view of sea ice state and highlight major deficiencies in the sea ice simulation. SITool is written in the open-source language Python and distributed under the Nucleus for European Modelling of the Ocean (NEMO) standard tools. SITool is provided with the reference code and documentation to make sure the final results are traceable and reproducible. Here, SITool version 1. 0 (v1. 0) is applied to evaluate the performance of Arctic and Antarctic historical sea ice simulations under the experimental protocols of the CMIP6 Ocean Model Intercomparison Project (OMIP,  Griffies et al. , 2016). OMIP provides global ocean-sea ice model simulations with a prescribed atmospheric forcing, which gives us the opportunity to intercompare sea ice model performance under fully controlled conditions.