dataesr/scientific-paragraphs-categorization huggingface dataset:
Visualizing outputs Once the analysis is complete, the results are visualized in a "HAllAgram" (Fig  4 ). This comprises a heatmap visualizing the relatedness and strength of association between pairs of features in the two datasets. Features are ordered along each axis according to their position in the hierarchical tree so that clusters of significant features can be boxed into contiguous units. Marginally associated pairs are dotted, and each hypothesis block is labelled with the rank of its association strength. Features not associated with any block are not plotted by default. For analysis results where large numbers of blocks are detected, only the strongest blocks are shown (30 by default), with potentiallyincomplete, lower-ranked blocks boxed in grey. Together, this set of plotting techniques allows users to visually understand the related sets of hypotheses that HAllA has detected.
HAllA increases power while controlling FDR to report blockwise associations When applied to paired datasets with no significantly related blocks of features, HAllA's descent algorithm reduces to all-against-all (AllA) direct pairwise feature testing. In such circumstances, HAllA is expected to perform similarly to AllA. However, when there are sets of correlated variables within one dataset that are correlated with another set of variables in the other, HAllA will report the block-wise associations. Notably, we expect this behavior to be common in multi-omics data, where we see large clusters of molecular features (e. g. co-expressed genes in a metabolic pathway). To evaluate these claims, we applied HAllA and AllA to paired, synthetic datasets generated with the data simulator function in the HAllA software.
These datasets contained pre-specified block associations, which allowed us to evaluate the statistical and computational performance of these two methods (Fig  2  and   For a variety of feature linkage relationships, we simulated 50 independent paired datasets, each containing 200 features, 50 samples, and clusters of correlated features. We then evaluated the ability of hierarchical versus all-against-all testing to recover these associations using a variety of similarity metrics. Performance was evaluated by comparing power and false discovery rates. Our hierarchical allagainst-all approach improved sensitivity relative to naive all-against-all approaches at a comparable false discovery rate. Similarity metrics that don't accept categorical data have not been evaluated in the categorical or mixed association type.
Other similarity metrics included in HAllA (dCor, NMI) were not applied in these simulations because their reliance on permutation tests made them too slow for simulations of this size (i. e. with many repeated iterations), although they are typically practical in individual realworld datasets. We evaluated many different forms of feature association, including linear, quadratic, logarithmic, sinusoidal, stepwise, parabolic, and mixed (combined discrete and continuous) data. We compared HAllA and AllA across these association types using a variety of similarity measures, including XICOR, mutual information (MI), Spearman correlation, and Pearson correlation. Across datasets and similarity measures, HAllA consistently detected more built-in associations (had better average power by as much as 10%) than AllA while controlling FDR at the same pre-specified level (Fig  3B ).
Associating microbes with metabolites in the infant gut microbiome In a prior study, Kostic and colleagues examined the development of the human gut microbiome in a prospective, longitudinally sampled cohort of 33 Finnish and Estonian infants at high risk for type-1 diabetes  [20]. Stool samples and clinical metadata (e. g. breastfeeding status, diet, and appearance of allergies) were collected monthly. Subjects' stool samples were subsequently analyzed using 1) 16S rRNA amplicon sequencing (to profile gut microbiome composition) and 2) targeted mass spectrometry (to profile host and microbial metabolites). The dataset included 103 samples from 19 individuals, each with paired metabolomics and 16S rRNA gene sequencing data. We applied HAllA to identify associations between the residuals of microbial and metabolite abundances after correcting for longitudinal trends and subject specific random effects using a linear mixed effects model  [21]  (S1 Appendix).
Associating the gut microbiome with host transcription in ulcerative colitis We next applied HAllA to data combining 1) 16S rRNA amplicon sequencing of the human gut microbiome and 2) Affymetrix microarray screens of ileal RNA expression across 204 individuals in a cohort of ileal pouch-anal anastomosis (IPAA) patients  [23]. In the original multivariate analysis of these data [24], microbial operational taxonomic unit (OTU) abundances were decomposed into principal components (PCs), and PCs accounting for up to 50% of the variance in the datasets were compared by all-againstall testing (an example of PC regression). While this approach enables well-powered comparisons of large numbers of features, the features are embedded as loadings in PCs, which complicates biological interpretation of the resulting associations.
HAllA identified 327 block associations in these microbial and gene expression data using an FDR threshold of 0. 05 and a FNT of 0. 1  (Fig 5B and S2 Table). Total relationships encompassed 125 OTUs, 187 transcripts, and the equivalent of 368 pairwise associations. The original study focused on the 9 th principal component (PC9) of the dataset due to its linking of a group of IL12/complement pathways to members of the microbiome, using an FDR threshold of 0. 25. Of HAllA's reported microbe-transcript associations when run with the same threshold, 20 genes were drawn from the 26 transcripts whose largest loading was in PC9. HAllA's findings support a surprising result of the original study: although PC9 represented only 1% of the transcriptional variation in these samples, it captured most associations between transcription and the microbiome during pouchitis.
HAllA's applicability to heterogeneous datasets We finally applied HAllA to identify associations between mixed clinical metadata and RNA expression in the breast cancer cohort of the Cancer Genome Atlas (TCGA)  [31]  available from the LinkedOmics R package [32], focusing on highly expressed yet variable transcripts (Fig 3 in S1 Appendix). HAllA identified 483 significant (Q-value < 0. 1) metadata-RNA associations within 261 blocks, including clusters of transcripts associated with tumor purity, PAM50 subtype, and ER Status. Notably, the transcripts occupying the block associated with PAM50 subtype include CA12, GABRP, NAT1, and TBC1D9, which have been previously proposed as predictor genes for breast cancer mortality, recurrence [33], and drug response  [34]. Coupled with the results of the preceding applications, these results speak to the generality of HAllA's associationdiscovery power across large, heterogeneous datasets.
Discussion In this work, we proposed and validated HAllA, a novel statistical method to find associations between multi-omic datasets. HAllA addresses several important methodological challenges in the analysis of high-dimensional datasets. It is applicable to data that are heterogeneous both within and between experiments, and it maintains statistical power using a novel hierarchical association testing and FDR control procedure. In this method, groups of correlated tests are modeled as blocks, ultimately reporting associations within blocks and between block representatives from multiple data types and experiments. This permits both great flexibility in the types of measurements to which it is applied and ease of interpretation of the resulting significant associations. Class prediction approaches are commonly used to model relationships between highdimensional datasets with variables measured using shared observational units.
For example, Partial Least Squares  [38]  and its close relative Canonical Correlation Analysis  [39]  identify latent variables in one dataset that are maximally correlated to latent variables in the other dataset. These methods, and robust and penalized varieties  [40] [41] , can identify blocks of variables that are correlated within one dataset and in turn with another block of correlated variables in another dataset. They do not, however, control for family-wise error or FDR, and so are most suitable for prediction or exploratory, visual, and descriptive analysis. With these methods, inference on the existence of associations between the variables of two datasets against null hypotheses of independence still relies on univariate hypothesis tests (and possibly dimension reduction or clustering) and is performed subsequently in a separate step.
The FDR for the potentially large number of tests can be controlled by the Benjamini and Hochberg method  [42] , which has been adapted for dependent tests  [43]  and hierarchically organized tests  [44]  that are continued until non-significance. The approach described here thus aims to combine the best features of these different existing approaches, yielding clustering of potentially heterogeneous variable types within each dataset with hierarchical testing and control of FDR. While these approaches are frequentist, Bayesian models are also used to improve power and share information among feature blocks  [45] [46] [47] [48]. While such methods are extremely powerful within their target domains, they are typically intended for incorporation of specific prior knowledge, such as graph structure  [44, 49] , phylogeny  [50] , or pathway-based functional roles  [51].
They can also be computationally expensive in cases where many or long simulation chains are required for convergence  [52]. HAllA's nonparametric frequentist approach will likely result in reduced power relative to such models within the domains for which they are designed, but with substantially reduced computational cost and without the need to specify model relationships and priors in each new application domain. Like most statistical tradeoffs, HAllA's generality as a tool for association discovery thus comes at a cost in specific circumstances where it is desirable to instead utilize prior knowledge and known data structure. A limitation of the current method is that it can only look for associations between two datasets at a time. While the method can be applied to multiple pairs of joint datasets manually, this becomes combinatorially prohibitive in particularly thorough studies where a large number of high-dimensional data types are available (e.
g. studies which collect genetics, gene expression, epigenetics, microbial profiles, metabolites, and metadata from each sample). In circumstances such as these, repeated application of HAllA across each pair of datasets would no longer properly control FDR. A potential extension would be to incorporate multivariate testing directly as an association measure, e. g. block PERMANOVA  [53] [54]  or Procrustes analysis  [55] , to lower the combinatorial burden by performing inference on sets of features rather than individual feature pairs. Second, the model does not share information between blocks, as would be the case in a fully multivariate test  [53]  or a hierarchical Bayesian model  [48]. Cases in which data do include such multi-layered nonindependence structure may indeed be better handled in a Bayesian framework.
Figure3. HAllA discovers block-structured associations while controlling false discovery rate. For a variety of feature linkage relationships, we simulated 50 independent paired datasets, each containing 200 features, 50 samples, and clusters of correlated features. We then evaluated the ability of hierarchical versus all-against-all testing to recover these associations using a variety of similarity metrics. Performance was evaluated by comparing power and false discovery rates. Our hierarchical allagainst-all approach improved sensitivity relative to naive all-against-all approaches at a comparable false discovery rate. Similarity metrics that don't accept categorical data have not been evaluated in the categorical or mixed association type. Other similarity metrics included in HAllA (dCor, NMI) were not applied in these simulations because their reliance on permutation tests made them too slow for simulations of this size (i.
used a fixed association structure with varying correlation strength led to similar conclusions (Fig 2 in S1 Appendix). Together these results show that the HAllA approach increases statistical power while maintaining the FDR across a wide variety of association structures under simulation. HAllA identifies novel fatty acid-xenobiotic metabolism associations in PPARdeficient mice PPAR is a nuclear receptor that regulates transcription of genes related to lipid metabolism in the liver [15]. These genes show high fatty acid catabolism rates, which can in turn affect hepatic fat storage and lipoprotein metabolism. We used HAllA to examine associations between 120 hepatic transcript levels and 21 liver lipid levels in a previously published dataset [16] (Fig 4). These data were originally collected from 40 wild type and peroxisome proliferator-activated receptor- (PPAR)-deficient mice [15].
Acknowledgements We thank  Alex Kostic ,  Tommi Vatanen , and  Vincent Carey  for assistance obtaining and curating datasets for the applications section;  Hera Vlamakis ,  Hector Corrada Bravo ,  William Shannon ,  A. Brantley Hall ,  Himel Mallick ,  Siyuan Ma , and  Susan Holmes  for helpful discussions, suggestions, and feedback. This study was supported by  Army Research Office  grant  W911NF-11-1-0429 ,  NSF   DBI-1053486 , and  NIH U54DE023798  to  Curtis Huttenhower.
System performance calculations The energy delivered to the samples during the OH treatments (Q OH ) was calculated according to Eq. 1 using the actual values of current (I) and voltage (U) recorded by the data logger during each experimental run. 𝑄𝑄 !" = # 𝑈𝑈(𝑡𝑡) * 𝐼𝐼(𝑡𝑡) 𝑑𝑑𝑡𝑡 # $ (Equation 1) This energy was compared to the theoretical energy required to conventionally heat (Q th ) the samples by temperature gradient (Eq. 2):  2 ) 𝑄𝑄 !" = 𝑚𝑚𝑚𝑚𝑚𝑚∆𝑇𝑇 (2) (Equation where m is the treated mass (kg), ΔT is the difference between final and initial temperature of samples (°C), and C P is the specific heat of must. The latter had been estimated by  Colombié et al. (2007)  as 3. 6 kJ. kg -1. K -1 for must containing 200 g/L of sugar. The ratio between the theoretical and OH energy (r) needed to heat the samples was calculated according to Eq.
Data points represent three independent experiments performed in quadruplate and are expressed as the mean ± SEM. ***P < 0. 001 when compared to dDAVP alone. e Effects of 0. 1 nmol/l dDAVP on cAMP production in microdissected rat outer medullary collecting ducts in the presence or absence of different concentrations (50 nmol/l, 500 nmol/l, and 50 µmol/l) of K17F (red bars) or LIT01-196 (blue bars). Six individual determinations were performed. Each point was compared to dDAVP (10 -6 mol/l). § denotes a statistical difference (p < 0. 001) with basal levels (No dDAVP). Groups were compared using ANOVA with Tukey's adjustment for multiple comparisons for figures (b-e). n. s. Not significant. stability of LIT01-196 as compared to apelin, we explored the possibility that the fluorocarbon chain could bind to plasma proteins, resulting in the protection of the peptide from enzymatic degradation by steric hindrance.
h-m In vivo effects of s. c. saline, tolvaptan, and LIT01-196. Evaluation of the effect of saline (1 ml/kg, n = 29 animals); LIT01-196 (900 nmol/kg, n = 23 animals) and tolvaptan (900 nmol/kg, n = 12 animals) in alert male Sprague-Dawley (SD) rats given normal chow on (h) urine output and (i) urine osmolality measured 3, 6, and 24 h after saline (S, gray bars, circles), LIT01-196 (L, blue bars, triangles), and tolvaptan (T, orange bars, squares). Metabolic parameters were measured 24 h after the s. c. administration of saline, LIT01-196, or tolvaptan (j, water intake; k, food intake; l, water to food intake ratio; m, body weight change). Data are shown as mean ± SEM. Multiple comparisons performed by Kruskal-Wallis, followed by post hoc Dunn's tests with Holm's adjustment. n. s. : P > 0. 05, *P < 0.
LVEF was determined in mice under isoflurane anesthesia at baseline and 3 h after s. c. administration of the compound, as previously described  52. Statistical analysis. We used Mann-Whitney U tests or paired Wilcoxon tests for comparisons between two groups, and analysis of variance or Kruskal-Wallis tests followed by post hoc comparisons for comparisons between multiple groups, when appropriate. We used a linear mixed-effects model to compare repeated values recorded over time, with time, treatment group, and the measured parameter as fixed effects and the unique animal identification number as a random effect. We assessed the effects of treatment by performing an analysis restricted to measurements taken after treatment administration. The normality of the data was assessed visually and with the Shapiro-Wilk normality test.
The data represented are means, with error bars to indicate the standard error of the mean. The number of experiments or animals used is indicated in each figure legend. Data were collected using Microsoft Excel for MacOS version 16. Statistical analysis was performed with R software version 3. 6. 3  53. A P value <0. 05 was considered significant. All analyses were two-sided. Exact P values for all analyses are provided in Supplementary Table  5. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Drs. Solène Boitard ,  Gwladys Haslé. and  Paul Del Rincon  from the  Laboratory of Central Neuropeptides in the Regulation of Body Fluid Homeostasis and Cardiovascular Functions  for animals and cell biology experiments, and  Pr. Nicolas Pallet , from the  Clinical Chemistry Department, Hôpital Européen Gorges Pompidou, APHP, Paris, France , for biochemistry determinations. We are grateful to  Nicolas Humbert  ( Laboratory of Biophotonics and Pharmacology, Illkirch, France ) for peptide synthesis,  Patrick Gizzi  and  François Daubeuf  for  PK experiments (Plate-forme de Chimie Biologique Intégrative de Strasbourg, TechMedILL ,  UMS  3286  CNRS/Université de Strasbourg, Illkich). We acknowledge the  Orion imaging facility, CIRB , for their support with the spinning-disk imaging and image analysis.
This work was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , including financial support for Proof of Concept, CoPoc Apelinatremia 2015-2017 by  INSERM Transfert , the  Centre National de la Recherche Scientifique , the  Université de Strasbourg , the  LabEx MEDA-LIS  ( ANR-10-LABX-0034 ), the  Collège de France , the  Agence Nationale de la Recherche  " Vie, santé et bien-être 2016 " ( ANR-16-CE18-0030 , FluoroPEP), the  Fédération Française de Cardiologie. and the  FRC (Frontier Research in Chemistry). A. F. was supported by a fellowship from  INSERM (Poste d'Accueil pour Hospitaliers ). P. -E. G. -S. was supported by a fellowship from the  Fondation pour la Recherche Médicale , grant number " PBR201810007643. "L. E. and S. M. R. were supported by a fellowship from the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche , and the  Agence Nationale pour la Recherche , respectively.
Pathogen growth was monitored in each community, both in vitro and in the tomato rhizosphere. We then expressed pathogen density and disease severity as a function of a direct (effect on pathogen) and indirect (effect on the helper strain) pathways for each of these rhizobacteria (Fig. 1 ). Part of the resulting data was also used to model the relative importance of direct versus indirect effects in determining realized pathogen density and subsequent disease severity. Based upon the results of these experiments, we discuss the potential utility of bioaugmentation strategies that target pathogen helpers as an element of integrated pathogen control.
The isolates were then re-streaked on TSA plates for colony purification. Approximately 5. 5% (35 isolates) of the bacterial isolates failed to grow on the TSA plates for unknown reasons when we re-streaked them and were therefore omitted from the dataset. The final collection thus consisted of 605 bacterial isolates derived from 20 rhizosphere soil samples. All purified isolates were cultured in 100 μl tryptone soy broth (TSB, liquid TSA) in 96well microtiter plates at 30 °C with shaking (rotary shaker at 170 rpm) for 18 h before freezing and storing at -80 °C in 15% glycerol. Strain identification. We sequenced the full 16 S rRNA gene to taxonomically identify all 605 rhizobacterial isolates. The 16 S rRNA gene was sequenced via Sanger sequencing of PCR products from glycerol stocks by Shaihai Songon Biotechnology Co.
, Ltd, Shaihai Station. The PCR system (25 μl) was composed of 1 μl of bacterial cells (overnight culture), 12. 5 μl mixture, 1 μl of forward (27 F: 5-AGA GTT TGA TCA TGG CTC AG-3) and reverse primer (1492 R: 5-TAC GGT TAC CTT GTT ACG ACT T-3) each  [17]  and 9. 5 μl of sterilized water. PCR was performed by initially denaturizing at 95 °C for 5 min, cycling 30 times with a 30-s denaturizing step at 94 °C, annealing at 58 °C for 30 s, extension at 72 °C for 1 min 30 s, and a final extension at 72 °C for 10 min. The 16 S rRNA gene sequences were identified using NCBI databases and homologous sequence similarity. A total of 90 bacterial isolates that were identified as Ralstonia solanacearum were removed from further analyses, resulting in 515 remaining isolates.
Assessing strain redundancy among the 515 non-Ralstonia solanacearum bacteria We assessed possible redundancy among the 515 strains of the non-Ralstonia solanacearum rhizobacteria. To encompass both taxonomic and functional redundancies, we considered the 16 S rRNA gene sequences as well as the direct effect of their supernatant on Ralstonia solanacearum. Self BLAST searches were performed on the full 515 sequence dataset using the makeblastdb and blastn commands from the BLAST command line tool  [19]. Sequences showing >99% identity over >95% of the full length of the 16 S rRNA gene were considered as taxonomically redundant. We then compared the direct effects on pathogen growth of the taxonomically redundant strains, and removed those showing the same patterns of interactions (positive, negative or neutral).
Phylogenetic tree construction The 16 S rRNA gene sequences of the 160 non-redundant bacteria were aligned using MUSCLE  [20]. Sequences in the alignment were trimmed at both ends to obtain maximum overlap using the MEGA X software, which was also used to construct taxonomic cladograms  [21]. We constructed a maximum-likelihood (ML) tree, using a General Time Reversible (GTR) + G + I model, which yielded the best fit to our data set. Bootstrapping was carried out with 100 replicates retaining gaps. A taxonomic cladogram was created using the EVOLVIEW web tool (https://evolgenius. info//evolview-v2/). To show the relationship between phylogeny and the effects of rhizobacteria on pathogen growth, we added taxonomic status (phylum) of each rhizobacterial strain and its effect on pathogen growth as heatmap rings to the outer circle of the tree separately (Fig.