dataesr/scientific-paragraphs-categorization huggingface dataset:
In their study, Von Mering et al  (2002)  estimate that, for Saccharomyces cerevisiae, the protein-protein interaction data (PPI) reported in public databases account for only one-third of existing interactions. This observation suggests that methods relying heavily on network topology may not be very suitable. In particular, the effectiveness of methods based on clique identification may be questionable if we consider that a significant proportion of protein-protein interactions remain unknown. This observation leads us to believe that methods based very precisely on the topology of networks are not to their advantage. We think first of all of the methods based on the identification of cliques. Their performance is more than questionable if we consider that a large part of the interactions between proteins are unknown.
Thus, any method that relies on graph traversals will find that a large portion of the network is close to any typical node  (Cao et al, 2013). We indeed argue that network-embedding methods can provide the backbone of reliable methods by estimating distances between nodes that take into account the entire topology of the graph and, moreover, are little affected by the proportion of missing edges. Our original method works on an embedding of an interaction network by adopting a greedy algorithm and an active subnetwork relevance measure defined in other articles  (Ideker et al, 2002). The great advantage of our method is that it does not require any parameterization; it is not even necessary to indicate the number of modules to be identified or the size of the modules. We have checked that our method performs well on artificial datasets and compares favorably with existing methods that are the current state of the art.
We then processed a real dataset from a study focused on PDAC, on which AMINE retrieved modules associated with functions recapitulating PDAC metastatic process such as the response to hypoxia and extra-cellular matrix-dependent signalling. Moreover, our studies show that AMINE can identify modules corresponding to functions not revealed by traditional approaches consisting in analyzing only the most differentially expressed genes. Indeed, we found that BLIMP1, one of the most up-regulated genes in highly metastatic cells, was included by AMINE in a module ontologically associated to pro-inflammatory process, a result confirmed in vitro. Indeed, the silencing of BLIMP1 in human PDAC cells altered cytokine production. However, we stress that our method is not an alternative to methods based on the identification of the most differentially expressed genes, but rather a complement to these approaches.
Materials and Methods The AMINE method predicts active modules from data consisting of background knowledge about gene interactions and measurements representing, in the specific context of a given experiment, indicators of the involvement of genes in the studied process. This concept of gene involvement is materialized by a P-value which quantifies, for each gene, the statistical significance of its variation (Fig  5A ). Data about gene interactions and gene variations are merged to generate an attributed gene network in which genes are annotated with a numeric attribute representing the extent of their variation  (Fig 5B). Mathematically, the dataset is represented as an attributed graph G = (V,E,λ) consisting of a set of vertices (also called node, that symbolizes the genes), a set of edges E4{(u,v)2V 2 ⋁u≠v} and a value function λ(v):V→R which associates a value P2R to each vertex v2V.
An induced subgraph of G is a subset of the vertices of G together with those edges of with both endpoints in S. Many active module detection algorithms focus on identifying induced subgraphs whose values associated with their nodes stand out from the values associated with the other nodes of the graph. We hypothesize that focusing heavily on the detection of connected sets of genes may not be optimal, given the fact that the interactions between genes described in the databases are still largely incomplete. For this, we adopt a definition of a module that is closer to the one used in cluster analysis: objects that are grouped together (in a module) are more similar to each other than to those in other groups. The notion of similarity encompasses a component taking into account the distance on the graph between the vertices belonging to a module and a significant nearness between the values associated to these vertices.
Parameters used with other methods For all methods, we used the standard parameters used in the reference articles. DIAMOnD and DOMINO require a list of seed genes (i. e. , differentially expressed) as input. The choice of the number of these seed genes is not specified in the manuscripts presenting the methods and is left to the discretion of the user. From a list of n genes associated with adjusted P-values, we used as seed genes all genes with an adjusted P-value < 0. 001/n, as done by  Lazareva et al (2021)  in their article. GiGA needs as parameter the maximum size of the identified modules. We executed the method specifying a size of 20, which is the default. DIAMOnD needs as parameter the exact size of the module to be found. The default value is 200 but we consider this to be far too high.
As AMINE is The algorithm consists of two main stages. In the first phase (lines 3-11), a greedy approach is used to assign to each node in the graph, the set of neighboring nodes in the embedded space that maximizes the s score. The aim is to iteratively extend each cluster as long as the score continues to increase. The second phase (lines 14-21) involves combining the individual clusters while ensuring that the merged clusters maintain spatial cohesion. dedicated to the identification of relatively small modules (to focus on really relevant genes that can be investigated by biologists), we have targeted our tests on the identification of small modules of sizes 10 and 20.
Selection of the network for analyzing real datasets All experiments were performed using the mouse PPI STRING network with interactions having a global confidence score higher than 0. 7, as is usual. In the GitHub repository (https://github. com/claudepasquier/ amine), we provide access to three public sources of PPI networks, namely STRING  (Szklarczyk et al, 2019) , BioGRID  (Oughtred et al, 2021), and IntAct (Del Toro et al, 2022) , for four organisms, namely C. elegans, D. melanogaster, H. sapiens, and M. musculus. We also provide a file that represents the union of these three networks. In addition, users can upload their own PPI network and associated P-values using two simple files. We provide examples of these files so that users can easily adapt their own network for use with AMINE.
fr), users can analyze their datasets using the PPI networks for the four previously mentioned organisms. As with the experiments, only interactions with a confidence score greater than 0. 7 are used. Thus, a user only needs to provide the differential gene expression data generated by the pipeline of his choice. From a very simple interface (Fig S10 ), he only has to specify the name of the organism analyzed, the file on which the data are located, and the ID of the columns containing the genes' names, the P-values, and optionally, the fold changes to be able to launch the process. The address of the page containing the results is e-mailed to the user when the processing is completed. On the result page, the most significant modules are listed, however, all the modules found can be downloaded as an Excel document consisting of two sheets.
Evaluating the algorithm's resistance to noisy data To assess the impact of the network used by the AMINE method, and in particular, to provide some insights on the robustness of the method to noisy data, we analyzed the Chiou et al data using different interaction networks, including BioGRID, Intact, STRING with a global score threshold set to 0. 9 and 0. 4 (in addition to the threshold of 0. 7 in the article) and STRING applying a threshold of 0. 7 on the different components of the global score, that is, co-expression, database, experimental, and text mining (the filtering on neighborhood, fusion, and co-occurrence sources generate networks too small and sparse to be successfully used to identify a module). The modules identified using each of these configurations are presented in Tables  S8-S15 , and the best annotation found for each module is presented in Table  S16.
It can be seen that depending on the type of network used, the enrichments differ. However, the same subset of relevant annotations, such as those related to glycolysis and gluconeogenesis, ECM organization, and elastic fiber formation, are generally found. Looking at the number of enrichments obtained with an FDR < 1 × 10 -5 for each network, we see that the different versions of the STRING database filtered on the basis of the global score are those with which the top 5 modules obtain an annotation. STRING filtered on the database score also sees 5 of these modules enriched. STRING filtered with co-expression and text mining scores sees 4 of the first five modules enriched. STRING filtered by considering only experimental data associated with a score > 0. 7 is the network that gets the least significant enrichment, as only 2 out of 5 modules have enrichments associated with an FDR < 1 × 10 -5.
Regarding the other databases, the results obtained seem less relevant than with STRING because with BioGRID and Intact, only two modules are annotated with an FDR < 1 × 10 -5. One of the most relevant annotations in the context of the Chiou et al study is the pathway directly associated with hypoxia: "HIF-1 signaling pathway. " This annotation is only identified with STRING filtered on the basis of a global score higher than 0. 7 and higher than 0. 4. We can draw two main observations from this experiment. Firstly, the AMINE method performs well regardless of the network used and appears to be tolerant to noisy data, such as that derived solely from text mining, as relevant annotations are obtained from this dataset, consistent with those obtained using more complete data. Secondly, using the STRING database by default with a threshold of 0.
GiGA is the second-best performing method. For modules of size 20, the F1 scores of GiGA and AMINE are very close (FigS4A). 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 Figs1B 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.
Acknowledgements The authors would like to acknowledge  Camille B érenguier , who critically proofread the article. They are grateful to the  OPAL infrastructure from Universit é Côte d'Azur  and the  Universit é Côte d'Azur's Center for High-Performance Computing  for providing resources and support. This work was supported by the  French government , through the  UCA JEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01 , the  French National Research Agency  through the  LABEX SIGNALIFE program  ( ANR-11-LABX-0028-01 ),  La Ligue contre le Cancer  ( GB/MA/IQ-10607 ), and  Fondation ARC  ( PJA 2016 120 4740 ,  PJA 2019 120 9546  and  PJA 2018 207701 ).
Data Availability All data generated or analysed during this study are included in this published article and its supplementary information files. In addition, synthetic data generated by  Robinson et al (2017)  are available for download on the GitHub repository of AMINE, specifically in the "data/synthetic" directory. The artificial data generated for this project are also regenerable using the generator included in the code deposited on GitHub. RNA-Seq data GSE90625 and GSE90824 from Chiou et al (  2017 ) are accessible at https:// www. omicsdi. org/dataset/geo/GSE90625 and https://www. omicsdi. org/ dataset/geo/GSE90824, respectively, and have been copied onto the GitHub repository under the directory "data/real/expression/ chiou_2017. " Differential expression analyses of these data were performed with the DESeq2 R package with default configuration, and the resulting output has been stored in the same directory.
For a given sequencing effort, higher sample size increases estimates of allele frequencies within a population and the probability of detecting rare alleles, but at the cost of decreasing sequencing read depth across loci, which may not be sufficient to remove sequencing errors and potentially biasing downstream summary statistics  [38] [39] [40]. Recently, several methods have been developed to overcome this issue taking into account the base call uncertainty  [38, 39, [41] [42] [43] [44]. Despite the fact that these methods are still in their infancy, they offer the opportunity to evaluate the robustness of the summary statistics using classic base calling for low-coverage datasets  [44]. In this study, we revisit the phylogeographic history, showing the mito-nuclear discordance of C. islandicus using mitochondrial and RADseq data, while estimating the sensitivity of different analytical approaches (data-type, differentiation indices and base call uncertainty).
Here, we used the mitochondrial and double digest RADseq (ddRADseq) sequence data of the individuals sampled at four locations belonging to three mitochondrial clades (AA', E and F) previously identified by Kornobis et al. [8]  to revisit the phylogeography of C. islandicus and test the likelihood of the mitochondrial vs. ITS scenario. First, to evaluate the mitochondrial scenario proposed by Kornobis et al. [8] , we performed a Bayesian structured coalescent analysis  [47]  with the subset of three clades to reconstruct the phylogeographic history of the mitochondrial DNA. Second, we used ddRADseq data to test population structure using different data types (SNP, haplotypes, sequences), differentiation indices, including indices taking into account the base call uncertainty and multivariate and Bayesian multi-coalescence analyses.
Finally, using ddRADseq data we evaluated the level of incongruence among loci, and tested the amount of admixture between sampling locations caused by introgression or ILS. [8, 9]  showing mito-nuclear discordance. The grey dotted line represents the undefined "species tree". Th: Lake Thingvallavatn, Sa: Sandur Adaldal, Sv: Lake Svartárvatn, Kl: Klapparós.
The library was prepared for sequencing following the manufacturer's instructions with a final concentration of 38 nM. The library was sequenced on an Illumina HiSeq2500 using the Illumina TruSeq kit (2 × 125 bp). To increase coverage of the data, the library was resequenced twice on an Illumina MiSeq2000 using the MiSeq Reagent Kit v2 (2 × 150 bp). The process_radtag. pl command in STACKS v1. 3 program  [50]  was used to demultiplex the paired-end libraries from ddRadseq. Reads with uncalled and low quality (phred score < 10) bases were excluded, the remaining reads trimmed to 100 bp and barcodes and adapters removed. The reads of the HiSeq run and the two MiSeq runs were assembled. The reads were assembled de novo into loci using pyRAD v1. 3. 1  [52] , as the alignment clustering approach allows for the presence of indels to improve the identification of homology among divergent taxa.
The restriction recognition overhang sites were removed. Low quality converted base calls below a score of 20 in pyRAD were considered as N and reads with more than 10 Ns were removed. A threshold of 80% was used to cluster the reads within individuals  [25]. Heterozygosity and error rate were estimated in pyRAD using the maximum likelihood formula proposed by Lynch  [53]. We used a statistical base call with a minimum depth coverage of five and then, due to low-coverage data, we generated two additional datasets using a consensus base call with a minimum depth coverage of three (C3) and two (C2) to retrieve additional loci. We removed the consensus sequences with more than 10 Ns. To exclude potential paralogs, the consensus sequences with more than two alleles after error correction and 10 heterozygous sites were removed (step 5 in pyRAD).
Then, the consensus sequences were clustered across individuals at 80% similarity  [24]  and aligned (step 6 in pyRAD). Finally, we retained all the loci present at least in 50% of the individuals (n = 30) to generate the output formats of the C2 and C3 datasets (step 7 in pyRAD). Single nucleotide polymorphism analyses were performed using one SNP per locus. The SNPs were selected either randomly (C2SNPr, C3SNPr, see Table  1 ) or selecting the most informative SNP ((C2SNPb, C3SNPb) as the one with the lowest number of missing data (individuals) and in the case of equal amount of missing data one was chosen randomly). The SNP selections were performed in R  [54]  from the. vcf output file provided by pyRAD. Haplotype analyses were performed using the information of all the SNPs present in a locus.
We used the. alleles output file provided by pyRAD to generate fasta alignments for each locus. Then, we generated the haplotype files in Fstat formats removing the sequences with more than 25% ambiguous position (i. e. , second read missing) and considering indel as a 5th state (Cov2H5th, Cov3H5th) or as missing data (Cov2Hmis, Cov3Hmis) using the haplotype function of the R package haplotypes  [55]. Finally, all the haplotype datasets were also derived in their sequence equivalent datasets considering nucleotide information (Table  1 ). To test the influence of low-frequency alleles, potential remaining paralogs and loci detected under selection, we considered a "brute" dataset and a "clean" dataset, where we filtered out the loci in low frequency, considered as potential paralogs and under selection.
We removed all the loci with a minimum allele frequency (MAF) below 5%  [19, 56]  and those with an observed heterozygosity above 0. 5, which can be considered as potential paralogs  [19, 57]. To remove loci detected under selection, we ran Bayescan v2. 01  [58] , considered as one of the most robust programs, using the differentiation approach  [59]. To decrease the high false discovery rate, we followed Lotterhoos and Whitlock  [60]  using a prior odd = 10,000. For each SNP and haplotypes dataset, we performed two Bayescan runs using 50,000 MCMC generations preceded by 20 pilot runs of 5000 generations. The program Blastn v2. 4. 0 in NCBI (https://blast. ncbi. nlm. nih. gov/Blast. cgi?PROGRAM= blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome (accessed on 16 March 2016)) was used to infer the role of loci putatively under selection through search in GenBank.
Genotype Likelihood Datasets with ANGSD As low-coverage datasets are prone to base call uncertainty, which can bias downstream analyses  [39, 40] , we applied recent methods that by-pass the base call step and estimate the genotype likelihood  [38]  to compute almost unbiased summary statistics such as Fst-like  [39]. Such an approach implemented in ANGSD  [43]  and other related software  [41]  requires converting the original fastq files into BAM files. As no reference genome was available for C. islandicus or its closely related taxa, we generated two mock reference genomes for each dataset (cov2 and cov3) using a consensus sequence with a threshold of 95% similarity among all the individuals of a concatenated sequence of all the selected loci assembled by PyRAD (see previous paragraph).
The demultiplexed fastq files of each individual were aligned separately against these mock reference genomes in Bowtie2  [61]  and only the aligned paired-end reads were retained and converted into SAM files. SAMTOOLS  [62]  was used to sort the reads and convert the files in BAM format for the two datasets (cov2GL and cov3GL). ANGSD v0. 9. 1 was used to perform quality filtering of the reads. Bad reads with SAM score > 255 (-remove_bad 1) and reads with multiple best hits (-unique_only 1) were removed with a minimum mapping quality of 30 (-minMapQ 30) and a minimum base quality of 20  (-minQ 20). Sites likely to be polymorphic with a p-value less than 10 -6 (-SNP_pval 1e-6) and with a minimum coverage of 2 and a maximum coverage of 20 for at least 50% of the individuals were retained. To test the effect of low-frequency sites, we also considered a "brute" dataset and a "clean" dataset, where the sites with a MAF < 0.
4. 2 (available at http://tree. bio. ed. ac. uk/software/figtree/, accessed on 26 March 2016). Table  1. Datasets used in the present study and the number of markers (i. e. , SNPs or loci) retained after filtering (Nb. Mark. ). Dataset Name: abbreviations of the name of datasets, Data Type: the type of genetic data involved for each locus (i. e. , SNP, haplotype or DNA sequence), Min. Cov: the minimal coverage retained for a locus, Cl. Data: using a brute (br) dataset coming from pyRAD or a cleaned (cl) dataset removing loci with a minimum allele frequency (MAF) below 5%, or with an observed heterozygosity above 0. 5 or detected under selection by Bayescan  [58]. Nb. Mark: number of DNA markers retained in the analysis. a) Loci with a MAF below 5% were removed. ( b) Maximum number of markers used in pairwise comparisons.