dataesr/scientific-paragraphs-categorization huggingface dataset:
Together with a Gaussian fit to single emitter events in the x-y plane (Maximum Likelihood Estimation, MLE, using the NEO_analysis software) we were able to obtain a the 3D spatial coordinates of single molecules with high resolution. A first transformation (cross-correlation) was applied to precisely realign detection events from both cameras. Finally, for each detection, a ratio of intensity between each camera was calculated (I1/(I1+I2)) in order to reassign the detection to one of the two fluorophores. Super-resolution images (. tiff) with a pixel size of 10nm and localization files (. csv) containing the 3D spatial coordinates of each demixed detection (detection of each fluorophores) were generated. Multicolor 3D-dSTORM analysis Point Clouds Analyst (PoCA),  46  a continuation of SR-Tesseler  47  and Coloc-Tesseler,  48  was used to identify Single Synaptic and Multi Synaptic Boutons and to quantify VGlut(color 1) and Bassoon (color 2) clustering from localized molecule 3D coordinates.
For each color, a 3D Voronoi diagram was computed by creating polyhedrons of various sizes centered on the localized molecules. Automatic segmentation allowed detecting clusters for each protein (VGlut cluster to identify excitatory presynaptic boutons, and Bassoon clusters to identify Active Zones) by using a density threshold 2 for each color (di1 and di2, respectively) based on the mean density of molecules for the whole dataset, such that di1 R 4dd1 and di2 R 2dd2 for color 1 (VGlut) and color 2 (Bassoon), respectively. We only selected clusters with a minimum number of localizations, set to 200 and 50 localizations for color 1 and color 2, respectively. The colocalization between VGlut and Bassoon was computed from the overlapping clusters. Because of the stringent segmentation performed on color 1 (VGlut), which was needed due to the high density of synapses in brain slices, a significant amount of color 2 clusters (Bassoon) did not colocalize with color 1 clusters (Figure  S3 ).
QUANTIFICATION AND STATISTICAL ANALYSIS Data analysis was performed, and graphical visualisation made, using R 3. 4. 1 (R Foundation for Statistical Computing; http://www. r-project. org/) and RStudio 1. 2. 5042 (https://rstudio. com). Single variable data distributions were visualised using kernel density estimation plots. This non-parametric probability density function provided a more effective way to view the distribution of a variable. Pooled synapse density and morphological measurements were visualised using violin or boxplots. Box-and-whisker plots indicate the median (thick horizontal line), the 25th-75th percentiles (box) and the 10th-90th percentiles (black whiskers); circles within and to the right of the box represent mean and individual values, respectively. Violin plots are comparable to boxplots, but give a representation of the kernel probability density of the data.
Data availability d The serial block face scanning EM datasets used in Figures  1  and 2  have been deposited at WEBKNOSSOS (https:// webknossos. org/) and are publicly available as of the date of publication. Accession numbers are listed here (https://wklink. org/1289; https://wklink. org/1290; https://wklink. org/5367; https://wklink. org/1928) and in the key resources table. d All original code has been deposited at https://github. com/juliavg/multi_synaptic_boutons and is publicly available as of the date of publication. DOI is listed in the key resources table. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. EXPERIMENTAL MODEL AND SUBJECT DETAILS All animal procedures were approved by the local ethics committee and licensed under the UK Animals (Scientific Procedures) Act of 1986.
ACKNOWLEDGMENTS We would like to thank Winnie Wefelmeyer for useful feedback and comments on the manuscript and Vincenzo Mastrolia for the graphical abstract. We would also like to thank Nicolas Bourg and Abbelight for the SAFeRedSTORM module and software, as well as Olympus for the IX3 microscope used for dSTORM. This research was funded in whole, or in part, by the Wellcome Trust.
The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches.
It is then possible to focus on the detection of subspaces containing genes that have a high activity. Consequently, the identified modules may not necessarily be fully connected in the original graph structure, in contrast to other methods that operate on the graph. Our approach thus favors the proximity of the nodes in the reduced vector space but there is no constraint for connectedness. Following this line, we propose AMINE (Active Module Identification through Network Embedding), a new and efficient method for active module detection based on Node2vec  (Grover & Leskovec, 2016). Our method uses a greedy approach to build the clusters based on the similarity of the nodes' encoding vectors and a metric that takes into account the activity of the contained nodes. We evaluated the behavior of AMINE on artificially generated datasets on which it is possible to accurately measure the performance of the algorithms.
On sparse interaction networks, in a task consisting of finding 3 distinct gene modules, AMINE outperforms the MRF method  (Robinson et al, 2017) , which itself achieved better results than four other methods published between 2009 and 2015 using the exact same dataset. On dense, more realistic networks, AMINE can locate modules with a higher accuracy than other evaluated methods. Furthermore, the work is done fairly quickly (30 min for a network of 10,000 genes) and without any parameterization. We next evaluated the performance of AMINE in predicting known pathways/biological processes on a publicly available transcriptomic dataset comparing pancreatic ductal adenocarcinoma (PDAC) with low and high metastatic potency. Finally, we explored in vitro unexpected functions predicted by AMINE for BLIMP1/ PRDM1, one of the most overexpressed genes in pro-metastatic cells.
Results Evaluation of AMINE on artificial data generated by  Robinson et al (2017)  Many studies dealing with the identification of active modules have tested their methods on datasets generated by themselves and which are, at times, difficult to reproduce. Robinson et al (2017)  are among the few to give access to all materials used to test the MRF method they proposed. These materials contain the graph itself, the P-values associated with the nodes, and the modules to be identified. It gives us the opportunity to apply our method on exactly the same data. The simulated experiment used to evaluate the MRF method  (Robinson et al, 2017)  consists of a set of 1,000 scale-free graphs, each containing 1,000 vertices associated with values simulated from a standard uniform distribution. In this dataset, each graph contains three distinct modules to be identified (called "hit modules"), with each module containing 10 vertices.
To simulate the fact that the vertices belonging to these modules represent differentially expressed genes, and are therefore associated with low P-values, these vertices are assigned simulated values from a truncated Gaussian distribution with mean 0 and SD equals to 0. 05. Robinson et al (2017)  compared their MRF method with NePhe  (Wang et al, 2009) , Knode  (Cornish & Markowetz, 2014) , and BioNet  (Beisser et al, 2010)  and reported that MRF gives the best performance in terms of recall. Because it is known that there are exactly 30 true hits in the dataset, the authors rate the different methods by considering only the proportion of true hits in each hit list of size 30. We ran AMINE on these data to detect the three most significant modules. The median recall of amine is 0. 79, whereas MRF has less than 0.
7. For AMINE, 50% of the recall score range between 0. 73 and 0. 84, whereas the same range for MRF is 0. 6 and 0. 75, respectively  (Fig S1A). The precision (number of true positives divided by the number of nodes identified) and the F1 score (harmonic mean of precision and recall) were also plotted on the same figure. The median value of the F1 score is 0. 76, whereas the minimum and maximum values are 0. 31 and 0. 94, respectively. The total number of genes in the three identified modules range from 18 to 46 with a median of 28  (Fig S1B). This means that AMINE is able to identify modules close to the ground truth (although slightly smaller) without the need to specify their size a priori.
The performance of AMINE was compared with the methods GiGA  (Breitling et al, 2004) , BioNet  (Beisser et al, 2010) , COSINE  (Ma et al, 2011) , DIAMOnD  (Ghiassian et al, 2015) , DOMINO  (Levi et al, 2021) , and a baseline consisting in simply picking the genes with the lowest P-values. Our results indicate that identifying a module on a denser network is a much more complicated process, as the median F1 score drops significantly from 0. 76 on a sparse graph to values just above 0. 5  (Figs 1A and S4A). Overall, however, the scores for the other methods are lower. COSINE and DIAMOnD scores are lower than the baseline strategy that relies solely on P-values. The F1 score obtained by BioNet is slightly below the baseline for the identification of an active module of size 10 and slightly above for the task of identifying an active module of size 20.
In the case of COSINE and BioNet, these poor results come from predicting large modules with a median size exceeding 150 nodes for COSINE and 75 nodes for BioNet  (Figs 1B and S4B). GiGA is the second-best performing method. For modules of size 20, the F1 scores of GiGA and AMINE are very close (Fig  S4A ). However, it should be noted that GiGA uses a parameter that determines the maximum size of the module to be identified. In our experiment, we set the maximum size equal to the expected size of the module to be identified, which, of course, facilitates the procedure. As shown in Figs  1B  and S4B , for GiGA, the size of the identified modules is always smaller than the maximum. The other method that needs the expected module size is DIAMOnD which produces a module with exactly the specified size.
DOMINO, a method that does not need the expected module size as a parameter identifies on average, for both configurations, small modules of less than 10 genes. For AMINE, without any indication on the size of the modules searched, we can see that the method predicts modules with a median size close to the ground truth size. The results obtained by the DOMINO algorithm are atypical, because it identifies modules that closely match the ground truth on some networks, whereas on others, it completely misses the modules that need to be identified, leading to poor results. We believe that the fundamental assumption on which the algorithm was designed is the cause of this issue. In their article,  Levi et al (2021)  noted that modules detected by active module identification methods often include GO terms that are also found in modules identified on randomly permuted data, indicating that they are not specific to the biological context of the omics dataset being analyzed.
The authors address this bias by partitioning the network into disjoint, highly connected subnetworks, which they term as "slices. " This process is carried out statically because it only deals with the network of interactions and does not take into account the values associated with the nodes. The search for active modules is then performed within each of these slices. This method therefore relies on the strong assumption that an active module must be included in one of the slices identified on the network, which may not be true on artificially generated networks where an active module may consist of a set of connected genes that are not necessarily clustered together. This hypothesis could well be verified on real interaction networks, but this remains to be proven. Meanwhile, as far as artificial datasets are concerned, this specificity of the DOMINO algorithm explains, in our opinion, to a large extent, the poor performance of the method.
This can be explained by the fact that, as the number of nodes in the network increases, the number of non-hit nodes that are associated with a random value higher than the values associated with hit genes increases. So, the probability that random modules score higher than the hit module increases too. This effect is less important for modules of size 20, for which the method works and for networks of 1,000 nodes (Fig  S6A ). Regarding sizes, they are still close to the ground truth, although there is a greater spread of values for modules of size 20 (Figs  S5B  and S6B ). Except for GiGA, which performs reasonably well, all other methods display poor results.
Validation using a real gene expression dataset To test the ability of AMINE and other methods to identify relevant biological functions, we downloaded from Gene Expression Omnibus a dataset relative to a study aimed at characterizing processes and genes associated to metastatic spreading in PDAC. With a 5-yr survival that has not significantly evolved for 30 yr despite progresses in anticancer therapies (<6%), PDAC is a cancer with one of the bleakest prognoses of the most fatal cancers. In the study of  Chiou et al (2017) , the authors compared two populations of primary PDAC cells according to the expression of HMGA2, a gene associated to a high metastatic potency and poor outcome in several cancers, including PDAC. RNA-Seq quantification was carried out from six pairs of HMGA2+/HMGA2-cell populations, each pair originating from PDAC primary tumors spontaneously generated in a genetically engineered PDAC mouse model (PKC mice).
To assess the usefulness of the active module identification methods and enable cross-method comparisons, we used the considered algorithms to analyze the differential gene expression of the HMGA2+/HMGA2-RNA-Seq experiment performed using DESeq2  (Love et al, 2014). The goal was to determine whether it was possible to identify gene modules involved in the functions associated to the metastatic signature of cancer cells, for example, EMT, ECM reorganization, glycolytic reprogramming, angiogenesis, involvement of RAS and PI3K/ AKT signalling pathways. All the methods tested on the artificial datasets have been executed except COSINE which is a very slow method and always generates large modules of little interest. Parameters used to run the methods are specified in the Materials and Methods section.
GiGA, BioNet, DIAMOnD, DOMINO, and AMINE have identified 5, 104, 1, 8, and 193 modules, respectively. Table  S1  shows the top 5 modules identified by the different methods. We Comparison of AMINE with GiGA, BioNet, COSINE, DiAMOND, and DOMINO on a task consisting of identifying a module of size 10 on a dense artificial network with 1,000 vertices (on these networks, using the generation method described in the article, we count an average of 4,300 edges). The boxplots summarize the results obtained on 1,000 different networks. "Expr. value," that is used for baseline, consists of selecting the 10 genes with the lowest P-values. next used gene set enrichment analysis  (Subramanian et al, 2005)  to compute enrichments with hallmark and curated terms using annotated genesets from the Molecular Signatures Database  (Liberzon et al, 2015).
As performed by  Chiou et al (2017)  on the differential HMGA2+/HMGA2-cell dataset, we searched for terms related to the general metastatic signature of PDAC cancer cells with each module. As expected, we recovered the hypoxic signature already found by  Chiou et al (2017). However, contrasting with their analysis, we found evidence for enrichment for previously described gene signatures of metastatic PDAC cells, in many of the five best modules identified by the different methods. The list of all enrichments is presented in Table  S2. In Table  1 , we specified the false discovery rate (FDR) associated with hallmarks related to metastatic PDAC cells, that is, EMT, ECM organization, glycolysis (carbohydrate metabolism), angiogenesis, RAS and PI3K/AKT pathways, and finally hypoxia  (Whittle et al, 2015; Chiou et al, 2017; Wang et al, 2021b).
We can see that all methods generate modules that can be associated to one or several metastatic hallmarks (Table  1 ). Overall, the DOMINO and AMINE methods are the most successful in generating modules associated to metastatic signature. Interestingly, the five modules retrieved by AMINE achieved 17 correspondences matching with metastatic features, whereas only 11 correspondences were identified from the modules generated by DOMINO. To have a more complete vision of the functions associated with each module, we carried out, for each of them, an enrichment analysis using the facilities offered by the STRING website (https:// string-db. org -  Szklarczyk et al, 2019). The lists of all enrichments associated with each module with an FDR < 0. 05 are presented in Table  S3. The first five modules identified by AMINE and their most significant enrichment are presented in Fig 2.
The same figures, made to illustrate the results of the methods DOMINO, BioNet, and GiGA are in Figs S7-S9. Results of DIAMOnD are not shown as the method identifies only one module. To ensure that the choice of the annotation associated to each module is not biased, we have systematically shown on the figures the enrichment associated with the lowest FDR among the curated datasets KEGG  (Kanehisa et al, 2023) , Reactome  (Gillespie et al, 2022) , WikiPathways  (Martens et al, 2021) , and Gene Ontology Biological Process (Gene Ontology Consortium, 2019). It was found that the different methods produced modules with varying degrees of overlap. BioNet was the method that generated the most overlapping modules, whereas GiGA produced perfectly separated modules. Most of the modules generated by all methods clearly corresponded to specific functions or pathways with very low FDR.
However, it was noted that for the DOMINO, GiGA, and BioNet methods, some modules showed less clear enrichment, with FDR values above 0. 001. In contrast, the AMINE method generated Module 2 3. 32 × 10 -5 6. 20 × 10 -10 Module 3 4. 57 × 10 -7 3. 9 × 10 -3 1. 29 × 10 -5 Module 4 4. 57 × 10 -5 3. 39 × 10 -9 Module 5 1. 53 × 10 -2 3. 42 × 10 -14 1. 06 × 10 -3 First two columns list the name of the methods and the identified modules. The following columns contain the false discovery rate associated with hallmarks related to metastatic PDAC cells, that is, epithelial-mesenchymal transition, ECM organization, glycolysis (carbohydrate metabolism) angiogenesis, RAS and PI3K/AKT pathways, and finally, hypoxia. modules that were all enriched with terms associated with an FDR below 1 × 10 -7. Importantly, the AMINE method was the only one to identify a module specifically associated with the hypoxia pathway.
Module 2 was indeed associated with the KEGG "HIF-1 signaling pathway" with an FDR below 1 × 10 -8. Overall, the comparison of the four methods highlighted the strengths and weaknesses of each approach, with AMINE standing out as particularly effective in identifying enriched modules with very low FDR values and identifying a specific module associated with the hypoxia pathway. Another strong point of the AMINE method is that it makes it possible to identify a list of modules that is usually very long. As we have shown above, it is then possible to focus on the modules considered as the most significant or, conversely, to specifically target modules associated with some genes of interest. This is what we propose in the following, by analyzing in more detail the results provided by AMINE and, as Chiou et al did in their article, by directing our analysis towards the BLIMP1 gene.
Interestingly, it is known that neoantigen expression in PDAC results in exacerbation of an inflammatory microenvironment that drives disease progression and metastasis  (Hegde et al, 2020). It was therefore tempting to validate this result using a series of functional experiments. In this perspective, we first explored a RNA-Seq experiment performed by Chiou et al revealing deregulated genes in BLIMP1-silenced PDAC cells compared with control  (Chiou et al, 2017). AMINE profiling of genes negatively regulated by BLIMP1 silencing revealed 345 modules with associated P-values < 0. 05 (Table  S6 ). Among the 10 best modules, we found that modules 2 (P-value < 1. 01 × 10 -11 ) and 9 (P-value < 2. 83 × 10 -8 ) were associated to cytokine production and inflammatory process (Fig  3  and Table  S5 ) after functional enrichment.
Next, to confirm the putative involvement of BLIMP1 in epithelial cancer cell inflammatory process in vitro, we silenced BLIMP1 in MIA PaCa-2 cells, a human PDAC cell line, using siRNA silencing, (Fig 4A ), and explored how it modified the profile of cytokine secretion using a cytokine profiling array and Western blot experiments. Indeed, we found that BLIMP1 repression triggered the production of IL-18Bpa and angiogenin, two antiinflammatory factors  (Lee et al, 2014)  and reduced the secretion IL-6, a major pro-inflammatory interleukin  (Tanaka et al, 2014 ) (Fig 4B  and C ). Altogether, these results indicate that the participation of blimp-1 in inflammatory process predicted by AMINE could be confirmed in vitro. They further validate AMINE as a valuable method to detect relevant functional modules from large experimental datasets.
Discussion This article proposes a new method for identifying gene modules that are activated as a result of a state shift caused by a biological experiment. Our method, called AMINE, uses as inputs, on the one hand, the ultimate result of any RNA-Seq analysis pipeline which is the differential expression of genes, and on the other hand, a network modeling the interactions between genes. Although many methods have been developed over the past two decades, AMINE stands out for its ability to accurately identify modules on datasets designed to mimic the structure of biological networks, outperforming many other competing methods. Extrapolating these results to a measure of accuracy on real datasets is very difficult. There is no method to ensure that good predictions on artificial data translate into good predictions on real datasets.
However, we have a special effort to ensure that our simulations are close to real datasets. The networks we generate, with the parameters presented in this article, are closer to a real interaction network than the networks used by some competing methods. In addition to the results reported in this article, several studies utilizing AMINE to analyze various types of data have already been published  (Feliz Morel et al, 2022; Pasquier & Robichon, 2022a; 2022b) , which also emphasizes the relevance of the results obtained by the method. Several studies utilizing AMINE to analyze various types of data have already been published  (Feliz Morel et al, 2022; Pasquier & Robichon 2022a; 2022b). It is known that the interaction networks stored in public databases are both incomplete and contain erroneous interactions.