dataesr/scientific-paragraphs-categorization huggingface dataset:
g. cases and controls). However, it is also important to characterize heterogeneity among groups of patients since inter-individual variability can serve as a basis for the definition of molecular phenotypes and patient stratification. Within each module, and for each individual subject, we used fixed cut-offs to count the number of transcripts that increase, decrease or do not change in abundance compared to a baseline value (e. g. absolute fold change in expression and absolute difference in expression vs. average of control samples). The percentage of differentially expressed genes for each module is then computed. These percentages are equivalent to values derived from group comparisons, except that they are derived for each individual sample. The sepsis cohort included in the reference dataset collection was used to illustrate how this approach can be employed to assess inter-individual variability for a given pathology (Fig.
shinyapps. io/BloodGen3Module/; video: https://youtu. be/IXJDGeVH1bs). This application permits the generation of fingerprint grid plots and heatmaps representing module aggregate activity across the 16 reference datasets (Fig. 6 ). This illustrative case focusing on sepsis shows that individual modular fingerprints can provide a means to achieve molecular stratification of patient cohorts. However, the biological and clinical relevance of such distinct molecular phenotypes would still remain to be determined in follow-on analyses. Profiling the abundance of A28 interferon-inducible genes at the aggregate level across reference patient cohorts. Having explained the approach implemented for the construction and characterization of the fixed BloodGen3 transcriptional module repertoire, we now present the analysis and visualization strategies for both group-and individual-level comparisons using an illustrative case focusing on the changes in abundance for module aggregate A28 (interferon responses).
The colors and intensities of the spots are based on the average across each given row of modules. A module annotation grid is provided where a color key indicates the functional associations attributed to some of the modules on the grid (top right). Positions on the annotation grid occupied by modules for which no consensus annotation was attributed are colored white. Positions on the grid for which no modules have been assigned are colored gray. b-d Fingerprint grid plots for additional reference datasets (COPD: chronic obstructive pulmonary disease, HIV: human immunodeficiency virus). influenza infection in the other cluster). The circle plots shown in Fig. 7b-d  provide a more granular illustration at the module and gene levels of the changes represented by spots on the heatmap. The gene composition for each of the six A28 modules is shown in circle plots in Fig.
1. In contrast, transcripts forming M15. 86, which showed very little change in response to those treatments, were markedly increased during acute HIV and influenza infection (Fig. 7c ). It is therefore possible that this gene set is more specifically induced by IFNγ. Interferon responses were weaker among RSV patients compared to these of patients with the two other viral infections. Similarly, in the context of bacterial Fig. 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 profiling data for reference transcriptome datasets (circulating leukocyte populations, hematopoiesis) is provided for each module.
These changes are represented on a heatmap, where an increase in abundance of a given module is represented in red, and a decrease in abundance is represented in blue. The subjects are organized as columns and the modules as rows. The order on the heat map was determined by hierarchical clustering. preferentially associated with hepatitis. The heatmaps available via the Prezi interface, which depicted the gene expression profiles for the A28 modules, provided 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 IFNβ (Supplementary Fig. 3  [GEO ID GSE60424);  29  ]. Despite the small number of subjects in this category, we observed a clear pattern of response to  interferon in vivo across all six modules.
a. A28 Fig. 8  Literature profiles and patterns of changes in abundance across reference datasets for the modules comprising aggregate A28. a Functional annotation by literature profiling. A. Portion of a heatmap comprising 382 modules organized as columns, and literature terms organized as rows. The six modules shown are associated with the consensus annotation "Interferon" or "Type 1 Interferon". The clusters of keywords associated with those modules are consistent with this annotation and provide added granularity to the module repertoire for functional profiling and interpretation. b Changes in abundance across 16 reference datasets. The heatmap represents the changes in abundance of transcripts constituting the six modules comprising aggregate A28 (columns). The modules are functionally associated with interferon responses.
The 16 reference datasets are arranged as rows corresponding to different health states. The columns and rows are arranged by hierarchical clustering. The heatmaps can be accessed and exported for all 16 datasets and 38 module aggregates using the web application: https://drinchai. shinyapps. io/BloodGen3Module/ (under the "MODULES X STUDIES" tab). years ago revealed interferon responses in the pathogenesis of SLE  33, 34. More recent profiling also revealed the prominence of this signature in patients with tuberculosis, which contrasts with findings made in other bacterial infections  20. The second cluster (labeled 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.
The illness conferred by RSV infection has a clinical presentation very similar to that of influenza in pediatric patients. However, studies suggest that interferon responses may be partially inhibited by RSV  35, 36. The third and final cluster was formed by pathologic and physiological states where an increase in abundance of interferoninducible genes was either modest or non-existent. This cluster included patients with juvenile dermatomyositis and B-cell deficiency (low levels), Kawasaki disease, melanoma, COPD, pregnancy (no increase), and individuals with MS (apparent decrease). The latter observation is interesting since IFNβ administration is one form of treatment for MS, which should compensate for the defect observed here in treatment-naïve individuals. 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.
By combining functional profiling with a reference dataset, we concluded that the modules responded differently to each interferon type: IFN-α induced an increase in the abundance of genes belonging to M1. 2; IFN-β induced an increase in the abundance of genes belonging to M1. 2 and M3. 4; and interferonγ 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 IFN-α and -β), M10. 1 and M15. 86 mapped to M3. 4 (inducible by IFN-β), and M13. 17 and M15. 64 mapped to M5. 12 (inducible by IFN-γ). Notably, the latter two modules did indeed segregate from the other four based on the literature enrichment profiling heatmap shown in Fig.
8a. We also used the six interferon modules to re-classify the adult SLE dataset profiled in our earlier study [GEO ID  GSE49454 24  ]. The resulting clustering and stratification mirrored our earlier findings made using the three interferon modules (Supplementary Fig. 4 ). Overall, these observations confirmed that interferon "sub-signatures" can be employed for patient stratification. This may be relevant in the tailoring of biologics targeting interferon in development for the treatment of SLE. However, the potential of utilizing the six modules from the new repertoire to improve on the three from the second-generation repertoire employed previously remains to be determined. Next, we aimed to illustrate how further insights can be obtained by examining changes in A28 modules abundance at the individual level concurrently with those of other module aggregates.
Parallel findings were obtained in pregnancy, which represents 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. The pattern of changes in influenza infection was more similar to that in sepsis, but with higher levels of interferon induction and a lower level of inflammation. Overall, the illustrative case presented here demonstrates the preliminary stepwise dissection of a given module aggregate and investigation of the underlying biological relevance. This process could be repeated for other module aggregates and while this is beyond the scope of the present study, it is a process that will support the scalable annotation infrastructure that has been developed here. Indeed, we expect the range of reference datasets and functional profiling approaches available for interpretation to continue to expand and the processes described here should also help determine to what extent subdivision of signatures in distinct modules is warranted.
Development and availability of ancillary resources. BloodGen3 modules can be reused as a framework for the analysis of blood transcriptome datasets which were not originally included for the constitution of the repertoire. We have developed custom bioinformatic resources to support downstream analyses visualization and interpretation carried out specifically using the BloodGen3 repertoire. These resources have been employed in several published use cases, all of which involving the analysis of multiple public datasets which were not employed for the construction of the BloodGen3 repertoire: (1) In one instance, BloodGen3 modules served as a framework for the development of targeted blood transcript panels (in the context of COVID-19  40  ); (2) Another illustrative case involved the use of the repertoire and associated interpretation resources described here to identify and characterize a systemic signature of psoriasis disease, and screen for constitutive genes targeted by existing drugs  41.
(3) In the final published illustrative case, the BloodGen3 module repertoire was employed as a framework for an integrative meta-analysis encompassing six independent RSV datasets  42. An R package that we have developed to support BloodGen3based data analysis and visualization (BloodGen3Module), was employed to carry out the analyses presented in these illustrative use cases and the current work. This tool has been described in detail in a separate publication  43  and scripts are openly available via GitHub (https://rdrr. io/github/Drinchai/BloodGen3Module/). Briefly, this resource includes functions that can be used to perform differential expression analyses and visualize datasets generated by the end users as fingerprint grid plots or heatmaps. Other project-specific bioinformatics resources that were developed include a web application that provides users with the ability to generate custom fingerprints plots (grids or  heatmaps), presently for the 16 reference cohorts employed for the construction of the repertoire (BloodGen3 app: https:// drinchai.
shinyapps. io/BloodGen3Module/). Different configurations can be selected for the heatmap, for instance to display fingerprints for modules within a given aggregate across the 16 reference datasets, or across all the individuals within a given dataset. For the latter application, it is also possible to select and combine multiple aggregates on the same heatmap. The resulting plots and corresponding tables can be exported and employed to prepare figures or generate plots using other tools. The Blood-Gen3 web application also integrates links to Prezi circle packing plots generated for each of the aggregates, thus presenting users with more unified access to supporting resources. Two additional project-specific web applications with similar functionalities have been developed. One was dedicated to the published COVID-19 use case mentioned above (https://drinchai.
shinyapps. io/ COVID_19_project/  40  ) and the second to the respiratory syncytial virus infections use case (https://drinchai. shinyapps. io/ RSV_Meta_Module_analysis/  42  ). Altogether these resources complement the extensive annotation framework presented earlier (interactive Prezi circle packing plots), to provide potential users means to leverage the Blood-Gen3 repertoire for the analysis of their own blood transcriptome datasets.
Discussion The BloodGen3 module repertoire is meant to be employed as a fixed and reusable framework for the analysis of blood transcriptome data. It has been constructed through co-expression analyses carried out across a wide range of diseases and physiological states. It constitutes the third iteration released by our group since 2008. Another blood transcriptome repertoire has been developed and made available by our collaborators from Emory University  23. Several differences in the approach employed for the construction of this latter 334-module repertoire should be noted. First, the development of the repertoire reported by Li et al. relied on public transcriptome datasets (N > 500), and second, selection of constitutive genes for the modules relied partly on co-expression and partly on functional convergences (ontology category, cell type-specific expression, interactome or bibliome).
More recently, Zhou and Altman described the development of another fixed transcriptional module repertoire based on the assembly of a collection of 2753 public datasets that were deposited in the NCBI GEO and encompassed 97,049 unique transcriptome profiles  44. Independent component analysis was then used to resolve co-expression relationships, which led to the identification of a set of 139 transcriptional modules. Subsequently, the biological relevance of these sets and advantages of their use for improving the robustness of analyses, especially for smaller datasets, were presented. Overall, this provided additional arguments in favor of the reuse of fixed transcriptional module repertoires, a practice that, to date, has not been widespread. However, Zhou and Altman chose datasets generated from a wide range of sample types as input data and the resulting framework may not capture some of the specificities that exist within more narrowly defined biological systems (for example a tissue or cell type such as the blood transcriptome in the case of the BloodGen3 repertoire).
It is also worth noting here that large collections of gene sets are commonly used for the interpretation of transcriptome profiling data [e. g. , gene set enrichment analyses (GSEA):  45, 46  ]. Such reference collections are very large, typically numbering tens of thousands of signatures. While these datasets have proven useful as a reference for functional interpretation, they have not been developed with a focus on a specific biological system or application, or to perform reductive analyses. Transcriptional module repertoires are also commonly constructed for "single use", using popular approaches such as wholegenome co-expression network analysis (WGCNA)  47. Such repertoires are based on and used for the analysis of a given dataset and are not designed to be reused, as is the case for the "fixed" module repertoires described above.
Notably, the continual reuse of fixed module repertoires over time periods usually spanning several years means that greater effort and resources can be dedicated to the development of tailored repertoire-specific analytic resources, as exemplified here in the case of BloodGen3. We previously attempted to develop such a support infrastructure for our second-generation of modules, albeit on a smaller scale. However, we learned that maintaining such resources over long periods of time proved a challenge as these now have gone offline through a combination of hardware failures, hacking or discontinued institutional support. Here we attempted to address these issues through adoption of zero cost infrastructure (e. g. depositing of R scripts in GitHub, deploying R Shiny apps, uploading videos to YouTube, or employing the Prezi platform that makes presentations freely accessible as long as public access is maintained).
These resources include a R package, "BloodGen3module", that can be used to generate fingerprint grid plots and heatmaps  43. These custom visualizations have been developed to support the interpretation of blood transcriptome data expressed at the module level. Notably, the two-tiered grouping, first at the module level and at the module aggregate level, is new for this third-generation module repertoire. This in turn led to adopting a different strategy for fingerprint grid plots visualization. Indeed, for earlier repertoires (Gen1 & Gen2), the order in which modules were selected determined their order on a given row of the grid (first to last, from left to right). For the BloodGen3 repertoire, modules within a given aggregate (=row) are grouped first according to functional annotations and then among the different annotations, from left to right according to alphabetical order.
The modules without annotations are added last and ordered in ascending order according to their module ID (i. e. based on the selection process). It may also be worth noting that changes in relative cell composition will in part be driving fluctuations in transcript abundance in whole blood. As such, co-expression analyses and the construction of module repertoires will permit the identification of gene sets associated with specific leukocyte populations. Indeed, functional annotations and reference leukocyte profiling datasets unequivocally associated a number of BloodGen3 modules with cell types (e. g. B-cells, T-Cells, cytotoxic cells, neutrophils or erythroid precursors). A selection of the 382 modules comprising this repertoire could therefore be employed specifically for bulk blood transcriptome data deconvolution and estimation of changes in abundance of those cell populations.
Analyses of other tissues or in the context of other species would require the development and use of separate frameworks. Indeed, we have also been involved in the development of fixed module repertoires for various mouse tissues  48  and in vitro culture systems, including human whole blood  49  and human dendritic cells  50. Second, it should be also noted that the BloodGen3 repertoire could be altered by the addition or removal of datasets used for its construction. However, we do not expect many changes at the least granular "module aggregate level". Indeed, the dominant functional themes found in BloodGen3 aggregates (plasma cells, interferon, inflammation, cytotoxic/T-cell responses, erythrocytes etc. ) were already present in both Gen1 and Gen2. More variations would be expected at the more granular module level.
We have previously found these inter-module differences to be biologically meaningful  24  , and conserved from Gen2 to Gen3 in the case of interferon. Third, we generated our transcriptome datasets for module construction using Illumina BeadArrays, a technology that predates RNA sequencing. Our choice to use this technology was based on availability at our institute for the construction of the BloodGen3 repertoire of a collection of well-characterized blood transcriptome datasets generated at the same facility and obtained across different projects using harmonized protocols. An alternative approach would be to compile publicly available blood transcriptome for which appropriately matched control data are available. The coverage afforded by RNA-seq data would likely result in somewhat larger modular gene sets; however, we consider it unlikely that entire co-expressed gene sets would be missed by microarrays to the extent that it would lead to the production of a repertoire that is fundamentally different to that produced using RNA-seq data.
It is also worth noting that the BloodGen3 repertoire is currently routinely used in our laboratory to perform analyses using RNA-seq data (some illustrative cases are already available  40, 41  ). In addition, the "Gen2" repertoire, also based on whole blood and Illumina BeadArrays  17  , has been used to analyze profiles generated from peripheral blood mononuclear cells using a targeted 700-transcript Nanostring panel  51. It is worth drawing the attention of investigators who wish to employ the framework for the analysis of RNA-seq data that the BloodGen3 repertoire was constructed at the probe level (those present on the Illumina HT12 v3 Beadarrays). This was to account for the possibility that changes in abundance for transcript variants measured by distinct probes may differ. And indeed, in some cases different probes mapping to the same genes were distributed across several modules (often within the same aggregate).
0 expression BeadChips were obtained from 16 groups of patients and controls selected as above. Thus, 16 datasets were used as the input data (Table  1 ). The expression data for each dataset were preprocessed and independently clustered. First, the probes were discarded if they were not detected (detection P < 0. 01) in at least 10 samples or in at least 10% of the samples, whichever was greater. Then, the sample data for each dataset were normalized using the BeadStudio average normalization algorithm. Once normalized, the signal was transformed such that all signals <10 were set to 10. Then, the fold change was calculated relative to the median signal for that probe across all samples. If the difference between a signal and the probe's median signal was <30, or the calculated absolute magnitude of the fold change was <1.
2, the fold change was set to 1 to reduce the noise from low-level responses. At this stage, the probes were filtered again. Probes were only retained if they had a calculated absolute fold change >1 in at least 10 samples or in at least 10% of the samples, whichever was greater. Finally, the data were transformed to the log 2 of the calculated fold changes. Module construction algorithm. Sets of coordinately regulated genes, or transcriptional modules, were extracted from the patient's whole blood microarray datasets. Full details and an example of the code are included in the supplemental methods (Supplementary Material). Briefly: each of the preprocessed microarray datasets was clustered in parallel using Euclidean distance and Hartigan's k-means clustering algorithm. The 'ideal' number of clusters (k) for each dataset was determined within a range of k = 1-100 using the jump statistic  53.