dataesr/scientific-paragraphs-categorization huggingface dataset:
After collating the 16 input datasets, we next followed a stepwise process to construct a module repertoire and identify co-expression networks (Figure  1 ). We used the module construction algorithm that we have previously implemented (the code is provided in Supplementary File 1). This approach followed four main steps: 1) clustering for each individual dataset; 2) co-clustering, where the number of instances that two genes were included in the same cluster was recorded, with the values ranging from 0 to 16 (i. e. co-clustering observed in none or all of the datasets, respectively); 3) constructing a weighted co-expression network, where the edges between the genes represent at least one co-clustering event in one of the input datasets and the weight is assigned based on the total number of co-clustering events; (supplementary Figure  1 ); and 4) mining the resulting network to identify highly inter-connected sub-networks that form the modules.
Development of module-level analysis workflows and visualizations A key characteristic of the gene sets collected via the process described above is that, by construction, changes in abundance of the corresponding transcripts within a given module will tend to be coordinated. As such, it should be possible to use these modules as a "framework" to: 1) identify functional convergences among the genes that comprise each set, and 2) summarize C. Gene co-clustering events were recorded in a table, where the entries indicate the number of datasets in which co-clustering was observed for a given gene pair. D. The co-clustering table served as the input to a weighted co-clustering graph (see also Supplementary Figure  1 ), where the nodes represent genes and the edges represent co-clustering events.
E. The largest, most highly weighted sub-networks among a large network (here constituting 15,132 nodes) were identified mathematically and assigned a module ID. The genes constituting this module were removed from the selection pool and the process was repeated, resulting in the selection of 382 modules constituted by 14,168 transcripts. changes in overall transcript abundance related to pathological processes or therapeutic interventions. We determined the gene composition of each of the 382 modules (supplementary file 2), The average number of genes per module as 37. 1, the median as 26. 5 and the range as 12-169. Functional profiling and enrichment results were generated using multiple tools (GSAn, Literature Lab, IPA, DAVID, KEGG, Biocarta, OMIM, and GOTERM). We also determined the extent of overlap with the previous modular repertoire and a set of modules constituted at Emory University and our previous generation of modules  (9).
For this we then performed a second tier of clustering to group the 382 modules into 38 "aggregates", with each row on the grid displaying the modules corresponding to one such aggregate. Segregation into distinct aggregates was based on similarities in abundance levels observed across the collection of 16 datasets. By this approach, we derived two levels of granularity (i. e. modulelevel vs. module aggregate-level) with the number of variables for interpretation constrained to a more manageable number. The overall result is that changes in expression levels for each row on the fingerprint grid will tend to be coordinated, which was not the case of prior iterations of such A. The modules were arranged onto the grid as follows: the master set of 382 modules was partitioned into 38 clusters (or aggregates) based on similarities among their module activity profiles across the sixteen reference datasets (A1-A38).
It is possible to go a step further and "aggregate" the changes observed by row, thereby reducing the dimensions for a given dataset even further. In this case, we reduced the dataset from 382 modules to 27 "aggregates" (Figure  3A ). Users can decide taking this extra step depends on the desired level of resolution: module-level or aggregate-level. For example, mapping changes at the aggregate level, the simplest framework possible, is optimal when looking at signatures in a broader context. But at the same time our earlier work showed that distinct interferon modules are biologically and clinically meaningful, also it can also be indicated to work instead at the modulelevel  (11). We generated fingerprint grid plots for each of the sixteen diseases or physiological states (Supplementary file 3); six module fingerprints are shown in Figure  3  as an illustration.
(Figure  4 ; Links for each aggregate are listed in Table  3. ; A demonstration video is available: https://youtu. be/8ajESET2mqI) Below, we describe briefly how we conducted our annotations (see also Experimental Procedures). Step 1 -Functional profiling: We conducted gene ontology profiling for each of the 382 modules using DAVID  (12) , GOTERM, and GSAn  (13). GSAn interactive reports were uploaded to a custom web portal (https://ayllonbe. github. io/modulesV3/index. html). We also performed pathway enrichment analyses using KEGG, Biocarta and Ingenuity Pathway, as well as literature term enrichment with Literature Lab. We synthesized and compiled the analyses (Supplementary   3. For a demonstration video, please visit: https://youtu. be/8ajESET2mqI.
Reference transcriptional profiling data Functional enrichment Demo video: https://youtu. be/8ajESET2mqI File 2) and used them to identify convergences and attribute functional titles to the different modules. Functional titles could not be attributed in all cases, due to a lack of convergence or poor enrichment in one or several of the analyses. Step 2 -Expression patterns in reference transcriptome datasets: We used transcriptome datasets as a reference to improve characterization and biological interpretation of the module framework. Two different datasets were used. The first was contributed by Novershtern et al. and comprised the transcriptome profiles of 38 human hematopoietic cell populations  (14). The second was contributed by Speake et al. and comprised the RNAseq profiles of six circulating leukocyte populations from patients with various immune-associated diseases  (15).
These percentages are equivalent to values derived from group comparisons, except that they are derived for each individual sample. The sepsis cohort comprised in the reference dataset collection was used to illustrate how this approach can be employed for assessment of inter-individual variability for a given pathology (Figure  5 ). Changes in transcript abundance across was found to be highly consistent across patients for some module aggregates. This was for instance the case of aggregate A1 (broadly associated with lymphocytic cells/responses), with consistent decreases in transcript abundance observed across patients. Conversely, consistent increases were observed for modules comprising aggregate A35 (broadly associated with inflammatory neutrophil responses). In this case differences were observed in the intensity of the response.
Other module aggregates, however, showed more mixed responses, which was the case of A37 (erythroid cells), A33 (functional association to be defined) or A28 (interferon response). A web application was developed as a resource to explore the inter-individual differences for a given disease, module aggregates or a combination of aggregates (https://drinchai. shinyapps. io/dc_gen3_module_analysis/#; video: https://youtu. be/y__7xKJo5e4 ). This application permits the generation of fingerprint grid plots and heatmaps representing module aggregates activity across the 16 reference datasets (Figure  6 ).
Module aggregates Datasets ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• ••••••••••••••••••••••••••• HIV MS Influenza SLE Kawasaki Melanoma Pregnancy B-cell deficiency JDM COPD Staph SoJIA Sepsis RSV TB LiverTransplant A1 A2 A3 A4 A5 A6 A7 A8 A15 A16 A17 A18 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37.
From the six modules, four seemed to be "core" interferon modules (M15. 86, M10. 1, M8. 3, M15. 127), while the remaining two (M13. 17, M15. 64) were associated to the interferon literature to a lesser degree. These latter two modules were more strongly associated with the Herpes simplex virus than the other four modules while the four core modules were preferentially associated with hepatitis. The heatmaps available via the Prezi interface, which depict the gene expression profiles for the A28 modules provide additional perspectives. For example, the dataset from Speake et al. comprised samples of patients with MS collected immediately before and 24 h after the administration of their first dose of interferon-b (Supplementary Figure  3  [GEO ID GSE60424);  (15) ]. Despite the small number of subjects in this category, we observed a clear pattern of response to interferon in vivo across all six modules.
This observation confirms the functional associations obtained from enrichment analyses. Next, we examined the degree of changes for the six interferon modules across the 16 input datasets (Figure  8B ). The first cluster showing the highest induction levels comprised SLE, influenza infection, HIV infection and active M. tuberculosis infection (noted as C1 on the figure). Interferon has antiviral properties; therefore, it is no surprise to see viral infections included in this set. Blood transcriptome profiling studies conducted nearly 20 years ago identified interferon responses in SLE pathogenesis  (18, 19). More recent profiling also revealed the prominence of this signature in patients with tuberculosis, which contrasts with findings made in other bacterial infections  (6). The second cluster (noted as C2) comprised diseases with an "intermediate" level of interferon responses, including RSV infection, sepsis caused by Staphylococcus aureus in pediatric patients and a range of bacterial pathogens in adults, SoJIA and liver transplant recipients receiving maintenance immunosuppressive therapy.
Taken together, this closer examination of annotations for A28 modules and patterns of changes in abundance in different reference datasets provided a clearer picture of its biological significance. •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• •••••• A28 SLE Influenza.
4 and then M5. 12. Based on this finding, we could stratify SLE patients based on whether one, two or all three of those interferon modules were activated. By combining functional profiling with a reference dataset, we concluded that the modules responded differently to each interferon type: interferon-a induced an increase in the abundance of genes belonging to M1. 2; interferon-b induced an increase in the abundance of genes belonging to M1. 2 and M3. 4; and interferon-g induced an increase in the abundance of genes belonging to M5. 12. Next, we sought to determine the equivalence between these three, second generation interferon modules and the six new, third generation interferon modules regrouped in aggregate A28. Based on gene composition, M8. 3 and M15. 127 mapped to M1. 2 (inducible by both interferon-a and b), M10.
1 and M15. 86 mapped to M3. 4 (inducible by interferon-b), and M13. 17 and M15. 64 mapped to M5. 12 (inducible by interferong). Notably, the latter two modules did indeed segregate from the other four on literature the enrichment profiling heatmap presented earlier (Figure  8A ). We also used the six interferon  Individuals     modules to re-classify the adult SLE dataset profiled in our earlier study [GEO ID GSE49454  (11) ]. The resulting clustering and stratification mirrored our earlier findings made using the three interferon modules (Supplementary Figure  4 ). Overall these observations confirm that interferon "sub-signatures" may be employed for patient stratification. This may be relevant in the tailoring of biologics targeting interferon which are under development for the treatment of SLE.
This finding was paralleled in pregnant women, who represent another immunosuppressive state, although increases in abundance of a subset of A35 modules appeared to be more prevalent in this group than in melanoma patients. Influenza infection was more similar to sepsis but with higher levels of interferon induction and a lower level of inflammation. Taken together, the use case presented here illustrates the preliminary stepwise dissection of a given module aggregate and investigation of the underlying biological relevance. Such a process would be to be repeated for other module aggregates. While this is beyond the scope of the present work it is a process that the scalable annotation infrastructure that has been developed here will support. We indeed expect the range of reference datasets and functional profiling approaches available for interpretation to continue to expand.
Zhou and Altman recently described the development of another fixed transcriptional module repertoire via co-expression analysis and discuss the advantages of such approaches  (27). In brief, they assembled a collection of 2753 public datasets that were deposited in the NCBI GEO that encompassed 97,049 unique transcriptome profiles. They used independent component analysis for resolving co-expression relationships which led to the identification of a set of 139 transcriptional modules. They went on to demonstrate the biological relevance of these sets and advantages their use presents for improving robustness of analyses, especially for smaller datasets. Altogether this article provides additional arguments in favor of the reuse of fixed transcriptional module repertoires, an approach that to date has not seen widespread use so far.
This includes a compendium of functional profiling reports and reference transcriptional profiles that can be accessed via an interactive web application. In addition, we are releasing another web application for data browsing and multi-tiered visualization. And it should also be noted that this ecosystem is scalable and will continue to grow over time as more analyses are being conducted, custom scripts developed, and reference dataset profiles added to the annotation scaffold. It is illustrated by ongoing efforts that consist in compiling relevant aggregate-centric information for A28. This is with the intent of improving over time understanding of the clinical relevance and biological significance of this transcriptional signature (accessible by clicking the information icon on the A28 interactive presentation: https://prezi.
com/view/E34MhxE5uKoZLWZ3KXjG/ and video demonstration: https://youtu. be/0j-kcE1tlXA). A bioinformatics analysis toolkit that comprises a collection of custom R scripts has also been developed and made available to the community via GitHub  (28). Notably, earlier attempts were made by our group to develop such a support infrastructure in the past, albeit on a smaller scale. But maintaining such resources over long periods of time proved a challenge and these by now have gone offline through a combination of hardware failures, hacking or discontinued institutional support. Such issues should be addressed here in part through adoption of zero cost infrastructure (e. g. hosting of Shiny apps, Prezi presentations or YouTube videos is free as long as public access is maintained). Finally, the resource that we are making available here has some limitations which are also worth noting.
First, our framework is specifically designed to analyze and interpret human blood transcriptome profiling data. Analyses of other tissues or in the context of other species would require the development and use of separate frameworks. Indeed, we also developed fixed modular frameworks for various mouse tissues  (29)  and in vitro culture systems, including human whole blood  (30)  and human dendritic cells  (31). Second, we generated our transcriptome datasets for module construction using Illumina BeadArrays, a technology that pre-dates RNA sequencing. Our choice to use this technology was based on data availability at our institute, and the decision to use data that was generated at a single facility over a relatively short period of time. An alternative approach would be to compile available blood RNAseq data that is publicly available and that also comprises appropriately matched controls.
Going forward, future developments include, firstly, the design and implementation of targeted transcriptional profiling assays based on the current repertoire. Indeed, such repertoires constitute a useful framework for module-based target selection and the development of "Transcriptome Fingerprinting Assays" [a preprint is available:  (32) ]. Such assays are costeffective, robust and scalable. Secondly, fixed transcriptional module repertoires may be leveraged for conducting meta-analysis. It was for instance employed as a basis to perform such an integrative analysis encompassing six independent RSV datasets encompassing 490 unique profiles [a preprint is available:  (33) ]. More recently, this resource has also been used in a different context to identify signatures associated with CTLA4 and combined CTLA4-PD1 blockade in peripheral blood of ovarian cancer patients [P249:  (34) ].
Positions on the gird for which no modules have been assigned are colored grey. B-D. FIGURE LEGENDS Fingerprint grid plots for additional reference datasets. A gene list, along with the ontology, pathway, literature term enrichment and transcriptional profiling data for reference transcriptome datasets (circulating leukocyte populations, hematopoiesis) is provided for each module. Zoom in and out functionalities for close-up examinations of the text and figures embedded in the presentation is possible. Web links providing access to modules within a given aggregate are listed in Table  3. For a demonstration video, please visit: https://youtu. be/8ajESET2mqI. The columns and rows were arranged by hierarchical clustering. Such heatmaps can be accessed and exported for all 16 datasets and 38 module aggregates using the web application: https://drinchai.
Module construction algorithm Sets of coordinately regulated genes, or transcriptional modules, were extracted from the patient's whole blood microarray datasets. Each of the preprocessed microarray datasets were clustered in parallel using Euclidean distance and the Hartigan's K-Means clustering algorithm. The 'ideal' number of clusters (k) for each dataset was determined within a range of k=1-100 by means of the jump statistic  (36). Taking the 16 sets of clusters as the input (Table  2 ), a weighted co-cluster graph was constructed  (Chaussabel, Quinn et al. 2008 , Chaussabel and Baldwin 2014). To select modules, an iterative algorithm was used to extract the sets of probes that are most frequently clustered together in the same datasets, proceeding from the most stringent requirements to the least, as previously described  (Chaussabel, Quinn et al.
Module annotation Gene Ontology / Pathway enrichment: Module gene lists were investigated using Database for Annotation Visualization and Integrated Discovery (DAVID) version 6. 7  (12). This database uses a modified Fisher exact test to identify specific biological/functional categories that are overrepresented in gene sets in comparison with a reference set. The top matched DAVID annotation cluster (using default settings), the top matched canonical pathway from Kyoto encyclopedia of genes and genomes (KEGG), the top matched pathway from BioCarta, and the top matched Gene Ontology biologic process (GO_BP) and molecular function (GO_MF) terms were identified for each module. Each module was also investigated for significant overlap with two other established blood transcriptome module repertoires  (3, 9).
The findings are summarized in the module annotation spreadsheet (Supplemental File 2). Gene Set Annotation (GSAn): To further annotate the modules, a new alternative to statistical enrichment analysis tool called GSAn was applied  (13). Statistical enrichment methods may have limitations  (36) (37) (38) , as these methods tend to focus on the subpart of the most studied genes and to provide gene set annotation results that cover a limited number of the well-annotated genes. To address these issues, GSAn offers: (i) an original method that combines semantic similarity measures and data mining approaches to perform a unified and synthetic annotation for a gene set of interest, and (ii) a visualization approach to facilitate the interactive exploration of the gene set annotation results according to the hierarchical structure of Gene Ontology (13 After completing the analysis, the summary result page was exported to an xls file.
Fingerprint grid plot visualization Modules were arranged on a grid based on their similarities in patterns of activity across the 16 input datasets, each of them corresponding to a different pathological or physiological state. First, the modules were partitioned using k-means clustering, which resulted in the constitution of 38 clusters. Given the possibility of collapsing values of the modules constituting each cluster in a single "aggregate" value, the term "module aggregate" was used to designate each cluster (A1 to A38). Of these 38 k-means clusters, 27 comprised >1 module. The modules were then arranged on a grid with each row corresponding to modules belonging to the same aggregate (Figure  4 ). The total number of rows on the grid equaled 27 and number of columns equaled the largest number of modules for a given aggregate (42 for aggregate A2).
Module Fingerprint analysis and visualization The modular analysis was performed by using 14,168 transcripts. Fold change was computed using gene expression data prior log2 transformation. For group comparisons, paired t-test was performed on log2-transformed data (Fold change (FC) cut off = 1. 5; FDR cut off = 0. 1). For individual-patients analysis, each sample was compared to mean of control samples in each dataset. Cut off comprised a absolute FC > 1. 5 and a difference in gene expression level > 10. A module was considered to be "responsive" when the proportion of differentially expressed transcripts (as defined above) was greater than 15%. Data visualization were performed using "ComplexHeatmap"  (37).
A. A collection of 16 transcriptome datasets spanning a wide range of immunological and physiological states were used as input. B. Each dataset was independently clustered via k-means clustering. C. Gene co-clustering events were recorded in a table, where the entries indicate the number of datasets in which co-clustering was observed for a given gene pair. D. The coclustering table served as the input to a weighted co-clustering graph (see also Supplementary Figure  1 ), where the nodes represent genes and the edges represent co-clustering events. E. The largest, most highly weighted sub-networks among a large network (here constituting 15,132 nodes) were identi ed mathematically and assigned a module ID. The genes constituting this module were removed from the selection pool and the process was repeated, resulting in the selection of 382 modules constituted by 14,168 transcripts.
Positions on the gird for which no modules have been assigned are colored grey. B-D. Fingerprint grid plots for additional reference datasets. Figure 4 Functional annotation of the transcriptional module repertoire. An interactive application is available to explore the 382 modules comprising the blood transcriptome repertoire. A gene list, along with the ontology, pathway, literature term enrichment and transcriptional pro ling data for reference transcriptome datasets (circulating leukocyte populations, hematopoiesis) is provided for each module. Zoom in and out functionalities for close-up examinations of the text and gures embedded in the presentation is possible. Web links providing access to modules within a given aggregate are listed in Table  3. For a demonstration video, please visit: https://youtu.