dataesr/scientific-paragraphs-categorization huggingface dataset:
Operation DEVEA was built as a Shiny application  4  in R 3 (V. 4. 1. 1). Shiny is a package that facilitates the development of web application from R. It is particularly indicated for building interactive and user-friendly software wrapper. The tool is hosted on a remote web server (http://shiny. imib. es/devea), freely accessible. Apart from DEVEA's public web server, the application can be used on a local computer (see the supplementary material for a detailed procedure as extended data here https://github. com/MiriamRiquelmeP/DEVEA/blob/main/Supplemental-Information. txt). Its source code is available on GitHub (https://github. com/MiriamRiquelmeP/DEVEA), under the terms of the Apache license 2. 0. DEVEA has been tested in Linux and Windows 10 operating systems locally, and has also been launched remotely with different browsers (Google Chrome, Mozilla Firefox and Internet Explorer).
Locally running the application shares all of the same characteristics as the Shiny web application. DEVEA relies on several existing R packages to carry out all the functionalities proposed (see supplementary material for the complete list). For instance, in order to handle the calculation of differentially expressed genes, the analysis is largely based on DESeq2 package. 8 The annotation is managed by the AnnotationDbi package,  9  collecting all the dedicated annotation databases for the different species (currently Homo sapiens, Mus musculus and Rattus norvegicus) for robust name conversion. For the enrichment calculation and visuals, the R packages topGO, 10 fgsea (Fast Gene Set Enrichment Analysis)  11  and clusterProfiler  12  together with other basic dependencies, such as ggplot2  13  and plotly 14 has been used.
Getting started To start working with DEVEA, the adequate module to perform the analysis has to be chosen from DEVEA's main lobby interface. The decision depends on the input data format. The user has to choose 'Go DESeq DEVEA' if their input is a CM + SI or a DO, whereas the 'Go Simple DEVEA' mode must be selected in the case of a GL + SV or a simple GL input files. See Figure  3  for a visual screenshot of the lobby.
Data upload and statistical design specification Within the appropriate interface according to the data type, the first tab available corresponds to the 'Input data' section. The user has to upload their own data in one of the different accepted formats and types (see them on Data requirement section) in their dedicated spaces (see Figure  4A ). For all input data, a field to specify the custom dataset to use as a background universe is available as well (see Figure  4B ). If necessary for the user, some demo data representing the 4 different input types, can be found on GitHub https://github. com/MiriamRiquelmeP/DEVEA/Data. The nature of the demo data and how it was generated is described in the Use case section. When a CM + SI or a DO are used as input data, it is important to indicate the statistical design or contrast for the expected comparison.
Differential expression analysis (DEA) and data view The first key performance of the application consists in extracting the descriptive information based on the feature expression and the statistical contrast representing the differential expression analysis part. In CM + SI data type a new DeseqDataSet object is calculated from the files and information provided by the user. In DO or GL + SI input modes, the application retrieves the important values already included in the objects. All transformations, normalization and measurements applied to the data at this step are performed with functions included in DESeq2 R package. It should be noted that DEA calculation is not possible with the simple GL input mode, due to the lack of expression values and statistical details. Following comments will not apply to this object.
The calculations and the statistical results are accessible in the 'Preview dataset' section tab. The user can explore at this step of the analysis the number of features considered as differentially expressed and their direction, and establish the descriptive statistics thresholds to consider them DEGs. By default, DEVEA uses prefixed log fold change|lfc|> 0. 5 and p-value < 0. 05 thresholds, that can be adapted by the user at any moment and thus modulate the list of DEGs. Moreover, the information uploaded and the descriptive statistics will be used to establish and control some interactive parts of the plots. For example, the color of defined up-regulated or down-regulated genes can be chosen (Figure  5A ). For the first two input objects (CM + SI and DO), a complete table of results, named 'Statistical -Expression values', is displayed showing useful information such as base means across samples, log2 fold changes, standard errors, raw and adjusted p-values for the specific design selected.
A second table is also shown with the DEA analysis details called 'Samples information -Coldata' (Figure  5B ). For the GL + SV mode, raw data are displayed in a table. The user can monitor gene name conversion, explore values interactively and sort, filter and download them at any time. It is important to stress that, as different elements can be extracted from the distinct input objects depending on their complexity, the number of graphs available for each of them will vary (Figures  1  and 6 ). Using the raw expression values that are available only in CM + SI and DO input modes, the user can explore data in a Principal Component Analysis plot (PCA) with the top 500 variant features, to show clusters of samples based on their similarity selecting the principal components of interest; a box or violin plot for gene expression distribution across the dataset; a heatmap representation of the top variant genes, regulated by the user; and a dot plot with the expression of the top 6 variant genes or a selection of individual genes of interest (Figure  7 ).
Enrichment analysis (EA) and visualization The last stage of the analysis with DEVEA is EA. This is a method to identify classes of defined categories that are overrepresented in the list of DEGs. These categories may be associated with disease phenotypes, biological pathways, or cellular functions. DEVEA uses the differentially expressed and significant features to retrieve the over-represented terms from several well known databases. This major block of the DEVEA analysis can be carried out from all data input types. It consists of an extensive EA after the selection of appropriate statistical values for defining DEGs from CM + SI, DO and GL + SV, or using all components included in the simple GL input. It collects significant terms from KEGG (Kyoto Encyclopedia of Genes and Genomes  18  ) and GO (Gene Ontology 19 ) Biological process, Molecular function and Cellular component databases.
Furthermore, a GSEA-type analysis (Gene Set Enrichment Analysis  20  ) can be performed from different databases for the whole set of features. In the case of CM + SI, DO and GL + SV input modes, KEGG and GO analyses are performed for all DEGs together, and for the subset of up-and down-regulated genes in separated tabs. GSEA analysis is always performed on the complete set of genes and uses the statistical values associated with the features. With the simple GL data input, enrichment can only be performed for the whole set of genes for KEGG and GO analysis and no GSEA analysis will be possible, due to the lack of statistical information. In the EA performed by DEVEA, the main results are shown as interactive tables containing detailed information on the enrichment from each database. In KEGG and GO categories, the tables display columns for the name of the significant pathways or terms, their (adjusted) p-values and additional descriptive information such as total number of genes associated or the DEGs participating in the pathway.
The user can also display the feature names that match in the pathway from the + symbol. Below each table, additional plots can be created by selecting rows of interest (showed in green on the Figure  9A ). The plots are interactive and reactive. They can be changed at any time by selecting new lines in the table. The user can visualize results as word cloud, circle plot, bars plot, chord plot, dot plot, heatmap or net plot representing different elements from the tables. For GSEA-type analysis, the results are displayed as a table containing the significant enriched pathways from the selected databases. In the table, important GSEA calculation parameters are available and below a typical GSEA plot is shown from the lines selected in the table (see Figure  9B ). As a special feature offered by DEVEA, a custom gene list can be loaded to be used as the background gene universe for EA (Figure  4B ).
For example, the user can use a background list containing only expressed genes in this experiment. It will control for experimental context and may enable the results to define not only the nature of the samples but also representative functions. This is especially important to consider in microarray studies when a limited number of probes is used, or in studies of specific cells or tissue with a restricted set of detectable genes. For this EA, internal ENTREZ ID gene code is used to associate gene names in Symbol or ENSEMBL with the enrichment annotation in KEGG, GO and GSEA libraries. An exhaustive conversion across different functions is conducted to retrieve all possible terms, since they are not registered in all conversion databases similarly. Furthermore, not all genes are curated or annotated and therefore some will be left out of the EA (the portion of genes is indicated in the application below the 'preview table' as shown in Figure  5B ).
Use case To demonstrate the usefulness of DEVEA, a RNA-seq dataset generated by our team and published recently  21  was investigated using the application. The study aimed to characterize the role of JAK2/STAT3 signaling in astrocytes in the context of Huntington's disease (HD). HD is a rare genetic neurodegenerative disease leading to severe motor, cognitive and psychiatric symptoms, with no curative treatment available. 22 Astrocytes, an heterogeneous group of star-shaped glial cells perform key functions in the brain, they provide nutrients to neurons, regulate synaptic transmission, and contribute to nervous tissue repair following injury. 23 Astrocytes become reactive in the brain of HD patients and their impact on HD progression is still unclear. 24 The study used a genetic mouse model of HD.
Treated mice were injected with an adeno-associated viral (AAV) vector targeting astrocytes and encoding a constitutive form of the JAK2 kinase (JAK2T875N) to activate the JAK2-STAT3 pathway. Control mice were injected with a similar AAV expressing GFP. Astrocytes were isolated and sequenced by RNA-seq from Illumina platform. Reads were aligned on the mouse genome (ENSEMBL GRCm38 release 96) and a count matrix was generated. Data were adapted and integrated as different input objects in DEVEA to test all functionalities. For instance, Figure  7A  shows that control (GFP, N=5, in green) and treated (JAK, N=6, in red) samples are clearly separated on a PCA plot, with a better separation achieved on PC2 (representing 27% of the total variance). Figure  7C -D also show two types of clustering profile, which group samples from the same group together and display genes with higher variability.
The final number of features is 18260 + 2 custom genes representing the two transgenes JAK2T875N and GFP). The SI file contains all relevant information on sample characteristics; (2) a complete DO built from the same CM and SI files with a design based on the comparison of the two different AAVs (design = AAV); (3) a GL + SV file manually created according to the DESeq2 results. The file is generated with 3 columns that contains the gene names, FC and P-Value for the top 500 most significant genes and (4) a unique GL containing only the 268 DEGs from the comparison of interest, reported from the DESeq2 analysis with adjusted P-Value < 0. 1 statistical threshold and no threshold for the FC (for further details on the input data, see the Data requirement section). With this user case, no errors were detected throughout the analysis.
In particular, DEVEA could further develop data management functionalities, by extending the capacity of dealing with batch effect or data pre-processing and filtering. One of the possible drawbacks is the low number of available species for the EA compared with other tools or the potential mismatches when converting gene names. To counter this, numerous graphics are displayed with whom the user can interact and perform a complete EA. The EA can be generated from all DEGs, either split by the direction of expression change, or merged into a single list. The custom global report is a unique feature to DEVEA. This might be very handy to share results with collaborators, because the user can easily insert comments, and transfer the fullyinteractive HTML report. Last, DEVEA appears slightly more flexible in terms of the application hosting and running, since it is possible to run it online (DEVEA web server), or offline (from R).
Conclusion and future directions The DEVEA application is developed to improve the set of existing software to perform DEA, data visualization and annotation or EA from transcriptomics data. DEVEA meets the need for applications that give sufficient usage autonomy, without compromising the complexity and accuracy of the results. It provides an interactive and user-friendly interface accessible for users without bioinformatics training, with high diversity of analysis components. Researchers can explore their data, carry statistical DEA and subsequent EA from distinct well-known databases without losing possible customization in real-time. It is a wide wrapper of functions in a single tool, avoiding the use of different tools/websites to run the distinct steps of the transcriptomics analysis and to reach this level of advanced ready-to-publish visuals, tables and results.
One of the main strength is the incorporation of several input data type options. The possibility to include a custom background make DEVEA suitable for analysis in which correction of some experimental bias could lead to better results in the EA part. Another key advantage is that DEVEA let the user extract their results individually or in a still interactive way through a custom HTML format file. To further develop DEVEA analyses, we plan to later offer additional pre-treatment options (remove batch effect, filter genes by expression, etc. ). More species can also be integrated. Other improvements include transcription factor enrichment analysis and use of complete datasets from different omics such as proteomics spectral counts matrix, microarray expression matrix, etc. In conclusion, a purpose of DEVEA is to promote a dialogue between biologists and bioinformaticians, particularly to produce suitable data and to understand the validity of the data needed to create the best downstream results.
Comments for manuscript improvement: The preparation of some input data is still difficult for biologists who lack bioinformatics skills. For example, getting the raw read counts requires bioinformatics skills to map the RNA-seq data to a reference genome or transcriptome and generate read counts by using quantification tools such as Salmon, Kallisto and RSEM. The author should propose an easyto-access method and provide step-by-step tutorials for biologists to generate the read counts from raw RNA-seq data, for example, using the Galaxy platform.
○ In the example data link: https://github. com/MiriamRiquelmeP/DEVEA/tree/main/data, it would be better to include a readme file with descriptions of the example datasets. ○ "Supplemental-Information. txt: Running DEVEA locally on a machine": I would recommend the author build DEVEA as an R package. Then, instead of "Download the compressed folder from the DEVEA GitHub repository", users can install and run DEVEA on a local machine as an R package. ○ Page 3: "Despite the great progress to facilitate transcriptomics computational analyses, some improvements are still possible for these tools. " Although comparisons were given in the Related work section, the author should give brief descriptions of these tools and summarise their limitations as literature reviews in the introduction. The author also should address what are the possible improvements and why they are needed.
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Transcriptomics method development, automated pipeline development, Shiny App development, bioinformatics analysis, RNA-seq data analysis, differential gene expression analysis, differential alternative splicing analysis, gene regulatory network analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
DESeq2 also provides suggestions on how to correct the batch effects in the user manual. + We can distinguish between modeling the batch effect using DESeq2 within DEVEA and removing the batch effect before importing the data into DEVEA. An advantage of using as input data a DO generated previously in R, is that the user can include Batch in the design formula as "design = ~Batch + Condition". Once the final contrast is specified as Condition_level1_vs_level2, the batch effect will then be 'accounted for' (the statistical inferences will be adjusted for Batch) with a differential expression analysis testing for Condition. Batch is essentially treated in DEVEA as a covariate in the regression model, just as we do in association studies. If the user wants to effectively remove the batch effect for downstream analyses, it can be performed outside DEVEA on the variance-stabilized or rlog expression values using limma::removeBatchEffect() or ComBat-seq tool, before uploading the data to the application again.
This could be considered for future versions of DEVEA as well. We have added a section in the 'Data upload and statistical design specification' section of the manuscript. + -DEVEA only allows a pair-wise comparison between control and treatment at a time, which is not flexible for complex experimental design, such as datasets with a great number of conditions to compare, datasets of time-series data and development series, and datasets with multiple groups and each group include addition level of conditions to compare. + Multiple experimental conditions can be included in the design of the DESeq2 object (DO). DEVEA will offer the possibility to consider any of them foreach analysis. For instance, when "design = ~ConditionA + ConditionB + ConditionC", the tool will propose as possible contrast 1.
ConditionA_level1_vs_level2, 2. ConditionB_level1_vs_level2 and 3. ConditionC_level1_vs_level2. The user can select from DEVEA interface, which specific contrast to explore. In addition, for DO and CM + SI, when there are more than two levels from the same condition, DEVEA will propose all the one-vs-one combinations. Therefore, if the "design = ~ ConditionWith4levels", the possible contrasts would be 1. Condition_level1_vs_level4, 2. Condition_level2_vs_level4, and 3. Condition_level3_vs_level4, level4 being the basal condition for every contrast. For CM + SI, these levels are selected alphabetically. In a DO, the user can use the function relevel() from DESeq2 in R to specify the basal level. We have changed the text in the manuscript to indicate these possibilities, in the 'Data upload and statistical design specification' from the 'Implementation' section.
-It would be impressive to enable enrichment analysis for the genes lacking annotations. For example, getting the gene annotation by blasting the sequence against some databases. It will be especially useful for the analysis of newly assembled genes and transcripts. + We agree that such an initiative would provide a genuinely useful service, especially for recently sequenced organisms who do not have yet a good and comprehensive annotation. However, this task is far from being trivial and requires a substantial amount of software development and is therefore currently beyond the scope of the current DEVEA implementation. For DEVEA, we currently aim to exploit available packages and resources for visualization, differential expression analysis and enrichment from well-known and described species.
Comments for manuscript improvement: -The preparation of some input data is still difficult for biologists who lack bioinformatics skills. For example, getting the raw read counts requires bioinformatics skills to map the RNA-seq data to a reference genome or transcriptome and generate read counts by using quantification tools such as Salmon, Kallisto and RSEM. The author should propose an easy-to-access method and provide step-by-step tutorials for biologists to generate the read counts from raw RNA-seq data, for example, using the Galaxy platform. + It is true that data preparation can be quite complicated for non-specialists. Following the remark of the reviewer, we have added references and web links for two complete pipelines and tutorials for Galaxy on our DEVEA GitHub page (https://github.
com/MiriamRiquelmeP/DEVEA/blob/main/Galaxy_tutorials. md). We have also included a brief explanation of them in the 'Use case' section. - + We have noticed that the HTML file that displays the tutorial is significantly heavy. As a result, some browsers do not render it properly and take some time to put each element in its place. In order to view the tutorial, as well as the complete workflow of DEVEA itself, we strongly recommend the use of Mozilla Firefox Web Browser. This part has been clarified in the 'Operation' section. -In the example data link: https://github. com/MiriamRiquelmeP/DEVEA/tree/main/data, it would be better to include a readme file with descriptions of the example datasets. + A README. md file has been included on GitHub data folder, with all the test data inside (for Mus musculus and for Arabidopsis thaliana).
-"Supplemental-Information. txt: Running DEVEA locally on a machine": I would recommend the author build DEVEA as an R package. Then, instead of "Download the compressed folder from the DEVEA GitHub repository", users can install and run DEVEA on a local machine as an R package. -Page 11: "Reads were aligned on the mouse genome (ENSEMBL GRCm38 release 96) and a count matrix was generated. " For reproducibility, the tool for read mapping and count matrix generation should be referred to and cited. + The software tools we used for this task and further details are now indicated in the main text. + -Figure  7 : the font size of labels and some details of the figures are too small. Would it be better to change the multiple plot layout to 3 rows x 2 columns? + We have tried to reformat the figure layout to 3 rows x 2 columns, but the result was really not satisfying, so we preferred to keep the original layout.
The paper has quite a few typos. The author should carefully check the entire manuscript again. Here, I have picked out some of them: Page 3: "key steps of a complete RNA-seq analyses pipeline" should be "key steps of a complete RNA-seq analysis pipeline" Page 3: "detailed bellow in the circular notes" should be "detailed below in the circular notes". Page 5: "the simple gene list (GL) can be build from" should be "the simple gene list (GL) can be built from" Page 6: "the program extract the conditions and calculates" should be "the program extracts the conditions and calculates" Page 11: "To demonstrate the usefulness of DEVEA, a RNA-seq" should be "To demonstrate the usefulness of DEVEA, an RNA-seq" Page 12: "which offers significantly less options since" should be "which offers significantly fewer options since" + We thank the reviewer for pointing out those typos.