dataesr/scientific-paragraphs-categorization huggingface dataset:
InfoSigMap is already used for the interpretation of Independent Component Analysis results in the BIODICA software (https:// github. com/LabBandSB/BIODICA) and of cBioPortal data using NaviCom web-service. 47 nfoSigMap was applied on some typical scenarios of cancer data analysis. The obtained results showed that a global view of the concordant behavior of functionally redundant signatures leads to an insightful interpretation with respect to what can be deduced from simple lists of significant signatures. In all four analyzed cases, the obtained results were found to fit with the previous experimental knowledge, confirming the reliability of our approach. However, also some indications concerning new candidate mechanisms to be experimentally investigated were extracted, showing how InfoSigMap can help in the formulation of new biological hypothesis.
Collections of signatures used in the analysis A vast collection composed of both data-derived and a priori knowledgebased signatures was considered as input for our analysis. The signature collections: Molecular Signature Database (MsigDB v5. 2),  17  ACSN,  48  the top ontributing genes of the components identified by Biton et al. (here denoted as CIT),  49  and the SPEED,  8  have been downloaded, obtaining a complete collection of 12,096 signatures. We consider as data-derived signatures: CIT, SPEED, and some MSigDB categories (clusters of genes coexpressed in microarray compendia (C4), signatures of oncogenic pathway activation (C6), the large collection of immunological conditions (C7), and chemical and genetic perturbations (CGP) part of the MSigDB collection canonical pathways and experimental signatures curated from publications (C2)).
We consider as knowledge-based signatures: ACSN and the MSigDB categories: genes sharing cis-regulatory motifs upstream or downstream of their coding sequence (C3), genes grouped according to GO categories (C5), and canonical pathways, including the well-known BIOCARTA, KEGG, and REACTOME databases. The MSigDB collections genes grouped by their location in the human genome (C1) and the H are not associated with any of the two previous classifications.
Procedure for the prioritization of those signatures that are informative in cancer biology To detect which of the starting 12,096 signatures were informative, we employed the Representation and quantification Of Module Activity (ROMA) tool, designed for the robust detection of overdispersed and coordinated gene sets. 42 The activity of each signature was thus evaluated in all the 32, previously described, expression datasets separately. We considered two p-values output of ROMA: the first one associated with the variance explained by the first principal component (ROMA L1 score) and the second one associated with the ratio between the variances explained by the first and second principal components (ROMA L1/ L2 score). The two p-values have been corrected for multiple testing through Benjamini-Hochberg method.
We will thus denote in the following with P_FDRs the corrected p-values. Only those signatures having the two P_FDRs <0. 05 in at least two tumor datasets were prioritized. To evaluate the number of false positives produced by our analysis, we designed a null model constituted by 1000 random signatures. These signatures have been built by sampling the size distribution of the starting 12,096 signatures and by extracting random genes for each one of the size percentiles. The genes for the null model have been randomly picked from the list of all the genes contained in the 12,096 signatures maintaining repetitions. Therefore, if a gene was present in 100 signatures in the original collection, it is present in multiple signatures also in the null model. We are thus preserving signatures dependences at least to some extent.
The procedure described above to detect informative signatures was applied to this collection of 1000 random signatures. None of these random signatures was found informative. Finally, we tested whether our analysis was biased by the use of TCGA data. Six hundred and ninety-two out of the 962 signatures remain informative for non-TCGA cancer datasets (colon 98 samples, cervix 33 samples, lung 58 samples, gastric 38 samples, and METABRIC breast cancer 1454 samples) (Supplementary Table  S2 ) Note that the non-TCGA datasets, except that of METABRIC, are smaller (in terms of samples) than the TCGA ones originally used for detecting informative signatures.
Test of the informative signatures in typical cancer analysis scenarios Our hypothesis that an informative gene set is more likely to be enriched in a typical transcriptomic data analysis, is tested by using GSEA, 17 a wellknown and widely adopted supervised approach. 17 GSEA was applied to three typical cancer-related examples using the entire collection of 12,096 signatures. The set of significant signatures was determined selecting those with a GSEA FDR q-value <0. 05. The statistical significance of the number of informative signatures present in the output of the GSEA analysis was evaluated through a Fisher's exact test.
Evaluation of the signatures eigengenes conservation across cancers Accorginly to the definition introduced in ref. 9  , eigengene is one of the right singular vectors of the gene expression matrix. Considering the singular value decomposition of a gene expression matrix (gene vs. sample) X = USV, eigengene is a row vector from matrix V, corresponding to the largest value on the diagonal of S. The metasample or sample activity profile is the U vector, corresponding to the largest value on the diagonal of S. The eigengenes associated with the informative signatures have been computed on all the 32 analyzed datasets (as a result of the PCA). For each informative signature, the pair-wise correlation between the eigengenes obtained in the 32 cancer types were computed and a conservation score was obtained as the absolute logarithm of the geometric mean of the Pearson correlation p-values.
Comparison between functional redundancy and intersection size of gene sets For each couple of gene sets, we have compared their normalized intersection size (JI) vs. their functional redundancy. The functional redundancy for a couple of gene sets was measured according to a two steps process. First, Pearson correlation coefficients between the metasamples associated to the two signatures were computed in each of the 32 cancer datasets. We thus obtained 32 correlation scores for each couple of signatures. Then to summarize them, their average was computed.
The areas of the network containing signatures associated with same biological functions are then identified and manually annotated on the top of the map to help the navigation of the users. In addition, also a purely data-driven layout was computed by applying tSNE dimension reduction method to the matrix of average pairwise correlations between the meta-samples defined by our signatures in all cancer types. This view of the InfoSigMap is available at http://navicell. curie. fr/pages/ maps_avcorrmodulenet. html (View/tSNE selection in the right-hand panel). Finally, the representation of the network as an interactive online map is achieved by using NaviCell,  50  powered by Google Maps API. Using InfoSigMap to have a global view of the signatures behavior Gene sets can be tested for differential activity across different experimental conditions using a tool of choice (e.
g. , GSEA, ROMA). Here the activity of the informative signatures is evaluated by applying ROMA, then the differential module activity is evaluated by Student's t-test, and fold change applied to the ROMA activity scores. Finally, the fold changes associated with a significant Student's t-test p-value (<0. 05) are mapped to the nodes of InfoSigMap as a color gradient, from red (upregulation) to white (no significant change) to green (downregulation), using the map staining approach described in ref. 50 The map is thus colored in the territories around each node creating a continuous colored pattern that helps a qualitative appreciation of the concordant/discordant behavior of large map regions. Classification of gene signatures for their information L Cantini et al.
Data availability The pan-cancer transcriptomic datasets used in this study are freely available on the TCGA data portal https://portal. gdc. cancer. gov. The InfoSigMap tool together with the. gmt file of the prioritized signatures are available at http://navicell. curie. fr/pages/maps_avcorrmodulenet. html. The tools and resources used in this study are publicly available. Custom codes used in the study are available upon request.
Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them. We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships.
We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset. Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis.
Introduction Metabolomics is rapidly becoming a standard tool for 'omics' research. The application of high-resolution mass spectrometry systems such as the Orbitrap  [1]  and high resolution Q-ToFs  [2]  allow the generation of rich datasets with accurate mass information that allows substantial inference on the metabolite composition of a mixture to be obtained. In complex mixtures, features detected using high resolution MS are often interpreted independently. These features are nevertheless related to each other since they may be involved in the same biochemical reactions (one being the substrate and the other the product). They also may be technologically connected due to ionisation modes (e. g. fragments, adducts). When annotating, it is important to take into account this information since it can provide valuable clues on the molecular nature of features.
g. "a sphingolipid"). To analyse all features in a network perspective, Breitling et al. [5]  proposed to build "ab initio" networks based on mass differences. For each pair of features in the network, if the mass difference between them is equal (up to few ppm) to the mass difference of a biochemical reaction, then these two features can be connected. Following this work, we proposed software (a Cytoscape plugin) called MetaNetter  [6]  allowing this network reconstruction to be performed. The MetaNetter plugin for Cytoscape 2 provided the capability to perform ab initio network prediction and was in the top 50 downloaded apps. Cytoscape is a powerful software package for displaying and manipulating networks. The original MetaNetter relied on a configurable list of potential chemical transforms.
Results Similar to MetaNetter, MetaNetter 2 requires an input file in text format with a list of masses. For retention time matching a column containing these times for each peak must be supplied. For correlation sorting or visual mapping of the nodes by abundance, additional columns may be provided headed with a sample label and containing numerical values for abundances. Datasets used in this example were derived from bovine serum extracts (used as inter-batch quality control) and a standards mix used primarily for improved annotation  [8]. A new addition to the capabilities of MetaNetter is the annotation of adducts in a dataset. This works in a similar manner to the chemical transformations described in the original paper, but is also capable of taking into account di-and trimers of the original molecule, decorated with a variety of adducts, as well as multi-charge adducts.
Display of the networks is configurable: the 'visual mapping' tab allows the colour and size of nodes to be mapped to either the mass of the compound (blue and small nodes are mapped to low masses, and red, large nodes mapped to high masses), or to the intensity of a mass/node pair in a given sample, with blue being low intensity and red being high. Additionally, both adduct and transformation edges are labelled with the adduct or transformation mapped, and furthermore transformations can be colour coded by type. Furthermore, the matching algorithm can be restricted to search only a specific retention time window for matches. For example adduct matches can be restricted to peaks occurring within a 10 s window. Fig. 1a  and b  shows the adduct pattern for a peak at 174. 1111, corresponding to arginine, from a Leishmania dataset  [11].
Fig. 1a  is visually formatted by mass, with red coloured and larger nodes being larger than blue small nodes. Fig. 1b  is visually mapped by retention time, demonstrating that all peaks mapped elute at the same time, towards the end of the run (denoted by the red colour of the nodes). In this example, bovine serum extracts were analysed in positive mode ionisation under acidic (pH 3. 0) and basic (ph 9. 0) conditions, using a ZIC-HILIC (Merck Sequant, Sweden) or ZIC-pHILIC (Merck Sequant, Sweden) column buffered with formic acid or ammonium carbonate respectively. Adduct pattern networks (see Fig. 2 ) were derived from each dataset and a table showing the number of detected adducts in each sample was generated using the Sample Match Table module (Table  S1 ). Overall, positive ionisation is favoured by a low pH, and consequently the proportion of adducts detected in HILIC is higher than those detected in pHILIC.
Transformations applied to all-ions fragmentation Transformation networks were available in the original version of MetaNetter. The new version provides the same functionality but also provides the ability (via the sample match table module) to map types of transformation to particular samples, based on a given threshold, or to determine the average ratio of transformed to untransformed molecules. This allows rapid profiling of the types of transformation detected across multiple samples and may be useful in identification of enzyme functions or the presence of particular chemical modifiers in different samples (see Tables  S2  and 1  ). One situation where the utility of the technique is likely to be highly helpful is the annotation of 'all-ions' fragmentation data. All-ions fragmentation is the application of collision energy to a sample without isolation of a specific ion first.
Analysis of anaerobically and aerobically grown Staphylococcus aureus To demonstrate the application to a biological sample, we applied the MetaNetter 2 app to a study of the pathogenic, Gram-positive organism Staphylococcus aureus grown under aerobic and anaerobic conditions. While not a substitute for a statistically driven untargeted analysis, MetaNetter 2 provides a visual way of looking at properties of the dataset that can provide insights into bulk properties. For example, Fig. 5  shows maps of the predicted lipids, connected via hydroxylation or chain length transformations. It is possible to see the interrelationships between detected compounds visually, as well as provide a visual guide to their relative quantitation. An example of this is given in Fig. 6 , where the selected transformations are phosphate, methionine, sulphate, diphosphate glucose-N-phosphate and glyoxalate.
Discussion Several other methods exist for graph presentation of mass spectrometry data. Software such as Pathos  [12]  and MassTrix  [13]  map detected masses on to KEGG pathways. Others like MetExplore  [14]  can use other databases than KEGG like genome scale networks provided in SBML formats. Nevertheless, both approaches rely only on metabolites belonging to the network and thus don't cover all the features generated in MS experiments. MetScape also allows mapping metabolomics data to these databases for Human (KEGG and EHMN) and add the ability to add correlation edges when semi-quantitative data are available. Other tools like Global Natural Products Social Molecular Networking (GNPS)  [15]  will generate molecular networks that can be loaded in Cytoscape. These networks are quite useful for identification purposes since they will connect similar MS/MS spectra.
This approach is different from the one implemented in MetExplore2  [14]  since this method is based on mass differences. Mummichog is the implementation of an approach aiming at performing metabolomics functional analysis by including features that are not necessarily identified. Mass difference is used to take into account adducts and confirm module or pathways enrichment. Nevertheless, mass difference is not used to reconstruct potential ab initio cascades of biochemical transformations in contrary to MetaNetter 2. One limitation of MetaNetter 2 is that it presupposes the existence (at some intensity) of an H + or H -ion that other adducts can be related to. Recent advances to adduct pattern matching  [16]  are a target for addition to the MetaNetter software in future.
Conclusion MetaNetter 2 provides a suite of expanded tools for de-novo network generation that provide unique capabilities to those seeking to annotate mass spectrometry data. The ability to map transformations and adducts across multiple samples to annotate the types of transforms and adducts detected on a per sample basis allows rapid screening for chemical and physicochemical modifications. The utility of MetaNetter 2 has been demonstrated in three usage cases: evaluating the adduct patterns arising from different buffer compositions, analysing the chemical transformations inherent in collisionally induced fragmentation patterns, and an application to Staphylococcus aureus grown under aerobic and anaerobic conditions. These cases illustrate only three of the many applications that the software can support and we envision novel applications in the field of metabolite identification to be discovered as usage of the tool expands.
Acknowledgements This work was supported by the  French Ministry of Research and National Research Agency  as part of the  French MetaboHUB, the national metabolomics and fluxomics infrastructure  (Grant  ANR-INBS-0010 ). KB was supported by a  Scottish Universities Life Science Alliance LEADERS Grant. Scottish Funding Council  provided funding for the purchasing of the instruments, including the mass-spectrometers and liquid chromatography systems. Glasgow Polyomics is supported by the  University of Glasgow and Wellcome Trust  (Grant numbers  097821/Z/ 11/Z  and  105614/Z/14/Z ).
Self-Organizing Maps (SOM)  [1]  are a popular clustering and visualization algorithm. Several implementations of the SOM algorithm exist in different mathematical/statistical softwares, the main one being probably the SOM Toolbox  [2]. In this presentation, we will introduce an R package, SOMbrero, which implements several variants of the stochastic SOM algorithm. The package includes several diagnosis tools and graphics for interpretation of the results and is provided with a complete documentation and examples.
Several implementations of the SOM algorithm exist in different mathematical/statistical softwares, most on them usable only for numeric data. The main one is probably the SOM Toolbox 1 , which is released under GPL2 licence and runs in Matlab. The toolbox contains many different functions to learn and interpret SOM and to handle various types of data, including datasets with missing data, symbol strings datasets and relational datasets (i. e. , datasets described by a dissimilarity,  [5] ). It also includes implementations of Generative Topographic Mapping  [6]  and of LVQ and metric learning for LVQ  [7] ,  [8]. The toolbox is fully documented and illustrated in the book  [2]. Since the free statistical software R  [9] , is one of the most (if not the most) widely used program in the statistical and bioinformatics communities, we have implemented a full R package providing various versions of the SOM algorithm and many functions for diagnosis and interpretation.
SOMbrero is inspired by Patrick Letremy's SAS programs 2. Prior SOMbrero 3 , several R packages were proposing SOM implementations (e. g. , class, som, popsom, kohonen,. see  [10]  for a more precise description of these packages). However, as far as we can tell, SOMbrero is the most complete one, with user-friendly features and implementations of variants of the original algorithm for relational data and contingency tables, based on the stochastic version of the method. Current version of the package is 1. 2 (2016/09/02) and the package runs on R (≥ 3. 0. 1) with dependencies to the packages wordcloud, scatterplot3d, RColorBrewer, shiny, grDevices, graphics and stats. The next sections describe the different features of the package. More precisely Section 2 presents the general organization of the package and its standard use for numeric datasets.
• prototypes can be initialized randomly as described in step 1 of Algorithm 1 or to one observation of the dataset, randomly chosen, each or by positioning them regularly on the first to PC of a PCA performed on X or finally, to values specified by the user; • X can be preprocessed by centering and scaling to unit variance or not preprocessed at all. In the case of a preprocessing of the data, the prototypes are returned in the original scale of X; • the assignment step can be performed in a standard way, as described in step 4 of Algorithm 1, or using the Heskes's modified assignment step  [11] : f t (x i ) ← arg min u=1,. ,U U u =1 H t (d(u, u )) x i -p t-1 u 2 ; • the number of iterations T can be set by the user. Default values is equal to 5n; • finally, the user can chose to save intermediate states of the training, which includes saving the prototypes, the clustering for all observations and the value of E t = n i=1 U u=1 H t (d(f t (x i ), u)) x i -p t u 2.
Plots and diagnosis functions SOMbrero has been conceived to help the user interpret the output of the algorithm. More precisely, two standard quality measures  [12]  are given with the function quality that computes: • the topographic error, which is the average frequency (over all observations) with which the prototype that comes second closest to an observation is not in the direct neighborhood (on the grid) of the winner prototype. It is a real number between 0 and 1, a value close to 0 indicating good quality; • the quantization error, computed as: Q = 1 n n i=1 x i -p f (xi) 2. Moreover, SOMbrero comes with many different plots, that can be used to represent the original dataset, the prototypes or an additional variable related to the observations, on the grid. 16 types of plots are available with the single function plot and two options what (what to plot? observations, prototypes or additional information) and type (which type of graph to plot?).