dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). Results were represented as the normalized area of the MS picks in log 2 scale using a. u. Normalization was performed by correcting the area of the MS picks across the batches using the quality control (QC) pooled samples (regularly injected during the analysis) and by centering their values around the mean of the QC areas. Differentially expressed metabolites were identified using the R/Bioconductor package limma  (Ritchie et al. , 2015). Metabolic pathway analysis was performed with Metab-oAnalyst 3. 0 (http://www. metaboanalyst. ca/) (Xia et al. , 2015) , using the hypergeometric test for representation analysis, and relative-betweenness centrality was selected for pathway topology analysis. For targeted metabolomics, statistical differences in BAs were calculated using a one-way ANOVA with Tukey's multiple comparison post hoc test, including sex as cofactor.
Statistical Analysis Unless otherwise specified, all experimental data are reported as mean ± SEM, except for the boxplots that were generated using Tukey's method. Statistical differences were calculated using two-tailed Student's t test for pairwise comparisons between two groups and one-way ANOVA with Tukey's correction for multiple comparisons for more than two groups. Survival analysis was performed by using the Kaplan-Meier method, and statistical differences were analyzed with the log-rank (Mantel-Cox) test and Gehan-Breslow-Wilcoxon test. Power analysis of survival was 80%. Mortality rate was calculated using the Gompertz model. Statistical differences between mortality curves was calculated using the extra-sum-of-squares F test. Maximal survival was analyzed using one-tailed Fisher's exact test at the 80 th percentile  (Wang et al.
ACKNOWLEDGMENTS We thank  Drs. V. Quesada ,  A. Fueyo ,  A. R. Folgueras ,  G. Velasco ,  M. Mittelbrunn , and  J. M. Fraile  for helpful comments and advice; and  R. Feijoo ,  A. Moyano , and  S. Alvarez-Miranda  for excellent technical assistance. We also thank  M. S. Pitiot ,  V. Garcı ´a de la Fuente ,  M. C. Mun ˜iz ,  V. Loredo , and  E. Pascual  for histological and micro-computed tomography (mCT) analysis. We also acknowledge the generous support by  J. I. Cabrera. The  Instituto Universitario de Oncologı ´a  is supported by the  Fundacion Bancaria Caja de Ahorros de Asturias. C. L. -O. is supported by grants from the  European Union  (ERC-2016-ADG, DeAge);  Ministerio de Economı ´a y Competitividad (MINECO/FEDER :  SAF2014-52413-R  and  SAF2017-87655-R );  Instituto de Salud Carlos III (RTICC) ;  Progeria Research Foundation  ( PRF2016-66 ); and EDP Foundation.
J. M. P. F. is supported by the  Ministerio de Economı ´a y Competitividad (MINECO/FEDER :  SAF2015-64157-R ) and  Gobierno del Principado de Asturias. G. M. is funded by the  Ramon y Cajal Program  ( RYC-2013-12751 ) and supported by the  Ministerio de Economı ´a y Competitividad (MINECO/FEDER :  BFU2015-68539 ) and  BBVA Foundation  ( BBM_BIO_3105 ). G. K. is supported by the  Ligue contre le Cancer  (equipe labelisee);  Agence National de la Recherche (ANR) -Projets Blancs ;  ANR  under the frame of E-Rare-2; the  ERA-Net for Research on Rare Diseases ;  Association pour la Recherche sur le Cancer (ARC) ;  Canceropôle Ile-de-France ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  Fondation pour la Recherche Medicale (FRM) ; the  European Commission (ArtForce) ; the  European Research Council (ERC) ; the  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; the  SIRIC Cancer Research and Personalized Medicine (CARPEM) ; and the  Paris Alliance of Cancer Research Institutes (PACRI).
Results We sequenced the protein coding portions of the genomes (exomes) of 50 Batwa rainforest hunter-gatherers and 50 Bakiga agriculturalists (dataset originally reported in  [23] ), identified single nucleotide polymorphisms (SNPs), and analyzed the resultant data alongside those derived from published whole genome sequence data for 10 Andamanese rainforest huntergatherers and 10 Brahmin agriculturalists (dataset from  [25] ). We restricted our analysis to exonic SNPs, for comparable analysis of the Asian whole genome sequence data with the African exome sequence data. To polarize allele frequency di↵erences observed between each pair of hunter-gatherer and agriculturalist populations, we merged these data with those from outgroup comparison populations from the 1000 Genomes Project  [26] : exome sequences of 30 unrelated British individuals from England and Scotland (GBR) for comparison with the Batwa/Bakiga data, and exome sequences of 30 Luhya individuals from Webuye, Kenya (LWK) for comparison with the Andamanese/Brahmin data.
0086; Brahmin p = 0. 0149; Fisher's combined p = 0. 00128; convergence empirical p < 0. 001; q < 0. 001) and 'protein autophosphorylation' (GO:0046777; Bakiga p = 0. 033; Brahmin p = 0. 0099; Fisher's combined p = 0. 003; convergence empirical p = 0. 001; q = 1). We also used Bayenv, a Bayesian linear modeling method for identifying loci with allele frequencies that covary with an ecological variable  [9, 32] , to assess the level of consistency with our convergent polygenic PBS shift results. Specifically, we used Bayenv to test whether the inclusion of a binary variable indicating subsistence strategy would increase the power to explain patterns of genetic diversity for a given functional category of loci over a model that only considered population history (as inferred from the covariance of genome-wide allele frequencies in the dataset.
To a minor extent, the growth factor-and heart-related functional categories highlighted in our study do overlap: of the 123 total genes annotated across the three heart-related categories ('cardiac muscle tissue development' GO:0048738, 'cardiac ventricle development' GO:0003231, and 'cardiocyte di↵erentiation' GO:0035051), nine (7. 3%) are also included among the 66 annotated genes in the 'growth factor binding' category (GO:0019838). However, even after excluding these nine genes from our dataset, we still observed similar polygenic PBS shifts in the Batwa and Andamanese for both growth factor-and heart-related functional categories (Suppl. Text), demonstrating that our observations are not driven solely by cross-annotated genes. We hypothesize that the evolution of growth hormone sub-responsiveness, which appears to at least partly underlie short stature in some rainforest hunter-gatherer populations  [33] [34] [35] [36] [37]  may in turn have also resulted in strong selection pressure for compensatory adaptations in cardiac pathways.
An important caveat to our study is the lack of statistical significance for our populationspecific analyses after controlling for the multiplicity of tests resulting from hierarchically nested GO terms. The absence of strong signals of positive selection that are robust to the multiple testing burden likely reflects both the expected subtlety of evolutionary signals of selection on polygenic traits and the restriction of our dataset to gene coding region sequences. However, our comparative approach to identify signatures of convergent evolution is more robust. Therefore, while we cannot yet accurately estimate the extent to which signatures of positive selection that potentially underlie the evolution of the pygmy phenotype occurred in the same versus distinct genetic pathways between the Batwa and Andamanese, we do feel confident in our findings of convergent growth-related and cardiacrelated pathways evolution.
Sample collection and dataset generation Sample collection, processing, and sequencing have been previously described  [17, 23]. Briefly, sampling of biomaterials (blood or saliva) from Batwa rainforest hunter-gatherers and Bakiga agriculturalists of southwestern Uganda took place in 2010  [17]. The study was approved by the Institutional Review Boards (IRBs) of both the University of Chicago (#16986A) and Makerere University, Kampala, Uganda (#2009-137), and local community approval and individual informed consent were obtained before collection. DNA samples of 50 Batwa and 50 Bakiga adults were included in the present study. Exome capture, sequencing, and variant calling were described previously  [23]. Briefly, sequence reads were aligned to the hg19/GRCh37 genome with BWA v. 0. 7. 7 mem with default settings  [46] , PCR duplicates were detected with Picard Tools v.
1. 94 (http://broadinstitute. github. io/picard), and re-alignment around indels and base quality recalibration was done with GATK v3. 5  [47]  using the known indel sites from the 1000 Genomes Project  [26]. Variants were called individually with GATK HaplotypeCaller  [47] , and variants were pooled together with GATK GenotypeGVCF and filtered using VQSR. Only biallelic SNPs with a minimum depth of 5x and less than 85% missingness that were polymorphic in the entire dataset were retained for analyses. Variant data for the Andamanese individuals (Jarawa and Onge) and an outgroup mainland Indian population (Uttar Pradesh Brahmins) from  [25]  were downloaded in VCF file format from a public website. To ensure the exome capture-derived African and whole genome shotgun sequencing-derived Asian datasets were comparable, we restricted our analyses of these data to exonic SNPs only.
Merging with 1000 Genomes data We chose outgroup comparison populations from the 1000 Genomes Project  [26]  to be equally distantly related to the ingroup populations: Reads from a random sample of 30 unrelated individuals from British in England and Scotland (GBR) and Luhya in Webuye, Kenya (LWK) were chosen for the Batwa/Bakiga and Andamanese/Brahmin datasets, respectively. We re-called variants in each 1000 Genomes comparison population at loci that were variable in the ingroup populations using GATK UnifiedGenotyper  [47]. Variants were filtered to exclude those with QD < 2. 0, MQ < 40. 0, FS > 60. 0, HaplotypeScore > 13. 0, MQRankSum < -12. 5, or ReadPosRankSum < -8. 0. We removed SNPs for which fewer than 10 of the 30 individuals from the 1000 Genomes datasets had genotypes.
Computation of the Population Branch Statistic (PBS) and the per-gene PBS index Using these merged datasets, we computed F ST between population pairs using the unbiased estimator of Weir and Cockerham  [48] , transformed it to a measure of population divergence [T = log(1 F ST )], and then calculated the Population Branch Statistic (PBS), after  [29]. PBS was computed on a per-SNP basis. We computed an empirical p-value for each SNP, simply the proportion of coding SNPs with PBS greater than the value for this SNP, which we adjusted for FDR. SNPs were annotated with gene-based information using ANNOVAR  [49]  with refGene (Release 76)  [50]  and PolyPhen  [51]  data. As the Andamanese/Brahmin dataset spanned the genome and the Batwa/Bakiga exome dataset included o↵ target intronic sequences as well as untranslated regions (UTRs), and microRNAs, we restricted our analysis to only exonic SNPs.
For both the Batwa/Bakiga and Andamanese/Brahmin datasets, we computed a "PBS selection index" for each gene as follows. We compared the mean PBS for all  19  SNPs located within that gene to a distribution of values estimated by shu✏ing SNP-gene associations (without replacement) and re-computing the mean PBS value for that gene 10,000 times. We defined the PBS selection index of the gene as the percentage of these empirical mean values that is higher than its observed mean PBS value. When identifying outlier genes, gene-based indices were adjusted for FDR. In order to assess potential biases related to variation in gene length and SNP minor allele frequencies (MAF), we repeated all analyses after computing the PBS selection index with binning of genes by length or SNPs by MAF. Complete details of these methods are included in the Supplemental Text.
Creation of a priori lists of growth-related genes To test the hypothesis that genes with known influence on growth would show increased positive selection in rainforest hunter-gatherer populations, we curated a priori lists of growth-related genes as described fully in the Supplemental Text. Briefly, we obtained the following gene lists: i) 3,996 genes that a↵ect growth or size in mice (MP:0005378) from the Mouse/Human Orthology with Phenotype Annotations database  [52] ; ii) 266 genes associated with abnormal skeletal growth syndromes in the Online Mendelian Inheritance in Man (OMIM) database (https://omim. org), as assembled by  [53] ; iii) 427 genes expressed substantially more highly in the mouse growth plate, the cartilaginous region on the end of 20 long bones where bone elongation occurs, than in soft tissues [lung, kidney, heart; >= 2.
0 fold change;  [54] ]; and iv) 955 genes annotated with the Gene Ontology "growth" biological process (GO:0040007). As the GH/IGF1 pathway is a major regulator of growth and disruptions to the pathway have been implicated in the pygmy phenotype, we also collected lists of genes associated with GH1 and IGF1 respectively from the OPHID database of proteinprotein interaction (PPI) networks  [55]. Separately, we also used a list of genes found to be associated with the pygmy phenotype in the Batwa  [17].
Statistical overrepresentation and distribution shift tests Using the PBS and Bayenv indices, we next tested for a statistical over-representation of extreme values (p < 0. 01) for the above a priori gene lists as well as all Gene Ontology (GO) terms using the topGO package of Bioconductor  [56] , gene-to-GO mapping from the org. Hs. eg. db package  [57] , and Fisher's exact test in "classic" mode (i. e. , without adjustment for GO hierarchy). We similarly performed a statistical enrichment test using the Kolmogorov-Smirnov test again in "classic" mode, which tested for a shift in the distribution of the PBS or Bayenv statistic, rather than an excess of extreme values. In all cases, we pruned the GO hierarchy to exclude GO terms with fewer than 50 annotated genes to reduce the number of tests, leaving 1,742 and 1,816 GO biological processes and 266 and 285 GO molecular functions tested for the African and Asian datasets, respectively.
To further reduce the number of redundant tests, we also computed the semantic similarity between GO terms to remove very similar terms. We computed the similarity metric of  [58]  as implemented in the GoSemSim R package  [59] , a measure of the overlapping information content in each term using the annotation statistics of their common ancestor terms, and then clustered based on these pairwise distances between GO terms using Ward Hierarchical Clustering. We then pruned GO terms by cutting the tree at a height of 0. 5 and retaining the term in each cluster with the lowest p-value. With this reduced set of GO overrepresentation and 21 distribution shift results, we adjusted the p-value for FDR.
Supplemental 3,996 genes that a↵ect growth or size in mice (MP:0005378) from the Mouse/Human Orthology with Phenotype Annotations database  [1] ; ii) 266 genes associated with abnormal skeletal growth syndromes in the Online Mendelian Inheritance in Man (OMIM) database (https://omim. org/), as assembled by  [2] ; iii) 427 genes expressed substantially more highly in the mouse growth plate, the cartilaginous region on the end of long bones where bone elongation occurs, than in soft tissues (lung, kidney, heart; >= 2. 0 fold change;  [3] ; and iv) 955 genes annotated with the Gene Ontology "growth" biological process (GO:0040007). Separately, we also considered in our analyses the set of 166 genes located within the 16 genomic regions previously associated with the pygmy phenotype in the Batwa, using an admixture mapping approach  [4] , as well as GH1-and IGF1-associated genes using data from OPHID database of proteinprotein interaction (PPI) networks  [5].
Acknowledgments The authors would like to thank the  Batwa and Bakiga  communities and all individuals who participated in this study, and  J. A. Hodgson  and  E. C. Reeves  for helpful discussions. This work was supported by  NIH   R01-GM115656  (to G. H. P and L. B. B. ),  1 F32 GM125228-01A1  (to C. M. B), and  ANR   AGRHUM ANR-14-CE02-0003-01  (to L. Q. -M. ). M. L. was supported by the  Fondation pour la Recherche Médicale  ( FDT20170436932 ). This research was conducted with Advanced CyberInfrastructure computational resources provided by The  Institute for CyberScience at The Pennsylvania State University.
Script and data availability All scripts used in the analysis are available at https://github. com/bergeycm/rhg-convergenceanalysis and released under the GNU General Public License v3. Exome data for the Batwa and Bakiga populations have previously been deposited in the European Genome-phenome Archive under accession code EGAS00001002457. Extended data tables are available at https://doi. org/10. 18113/S1N63M.
Second, to control for di↵ering MAF values for SNPs, we did the permutation-based computation of the PBS selection index while matching SNPs on global MAF (computed 6 using the African or Asian datasets for within-continent analyses. ) SNPs were grouped by MAF into bins of size 0. 01, and for each SNP in a gene, SNPs were sampled from only the set in the MAF bin. Neither modification to the PBS selection index computation algorithm majorly a↵ected the PBS selection index values nor the GO-based downstream analyses. Corrected and uncorrected PBS selection index values were highly correlated (R 2 = 0. 993 to 0. 997 and 0. 953 to 0. 985 for the gene size-and MAF-corrections respectively; Fig. S4).
20 indicates slight agreement, 0. 21 to 0. 40 fair agreement, 0. 41 to 0. 60 moderate agreement, 0. 61 to 0. 80 substantial agreement, and 0. 8 to 1 almost perfect agreement  [43]. Calculations were performed with the software EPIDAT 3. 1, which is available online at http://www. sergas. es/Saude-publica. Parasitic loads obtained from positive samples in protocols A and H were compared in a Bland-Altman difference plot in order to quantify the agreement between the two methods  [44]. The Spearman rank correlation coefficient (r s ), which describes the strength of a monotonic relationship between paired data, was also calculated  [45]. Ranging from -1. 0 to +1. 0, values close to +1. 0 indicate positive association whereas values close to -1. 0 indicate negative association. The interpretation of the absolute value of r s is as follows: 0 to 0.
Neuroblastoma, a pediatric tumor of the sympathetic nervous system, is predominantly driven by copy number aberrations, which predict survival outcome in global neuroblastoma cohorts and in low-risk cases. For high-risk patients there is still a need for better prognostic biomarkers. Via an international collaboration, we collected copy number profiles of 556 high-risk neuroblastomas generated on different array platforms. This manuscript describes the composition of the dataset, the methods used to process the data, including segmentation and aberration calling, and data validation. t-SNE analysis shows that samples cluster according to MYCN status, and shows a difference between array platforms. 97. 3% of samples are characterized by the presence of segmental aberrations, in regions frequently affected in neuroblastoma.
Tumors with only numerical aberrations have a favorable prognosis, while the presence of at least one segmental aberration is indicative of poor survival outcome  8. Neuroblastoma is a rare disease, therefore it is challenging to collect enough tumor material and data for a sufficiently powered study. Within the Ultra-High-Risk (UHR) working group of the International Neuroblastoma Response Criteria (INRC) consortium  5  , we collected DNA copy number profiles and clinical data of 556 high-risk neuroblastoma patients, resulting in a unique dataset. The aim of this project was to explore whether specific (combinations of) segmental DNA copy number aberrations allow to better discriminate high-risk patients with fatal outcome from high-risk patients with favorable outcome  9. The collected copy number profiles were generated on several aCGH and SNP array platforms.
DNA copy number data collection Copy number profiling data from 556 primary neuroblastoma tumors were collected through the UltraHigh-Risk (UHR) working group of the International Neuroblastoma Response Criteria (INRC) consortium. These tumors originated from high-risk patients enrolled in the SIOPEN, GPOH, COG or Japanese treatment protocol (SIOPEN: European Society for Paediatric Oncology Neuroblastoma, GPOH: Society for Paediatric Oncology and Haematology, COG: Children's Oncology Group). Published  6, 11, 12  and unpublished copy number profiling data generated on different platforms including aCGH arrays from Agilent (resolution ranging from 44k-1M) and NimbleGen (72k) and SNP arrays from Affymetrix (250k and 2. 6 M) and Illumina (550k) were included in this study. Initially, data from 671 high-risk samples were collected, with high-risk defined as either stage 4 and older than 1 year, or MYCN-amplified (any age and stage).
MYCN amplification status was determined by FISH or qPCR. Of the 671 samples, 16 samples were discarded due to bad quality profiles and 98 samples were discarded due to suspected contamination with normal cells (49 silent profiles without detected copy number aberrations and 50 with almost undetectable aberrations), resulting in a set of 556 samples. Sample annotations, including among others age at diagnosis, disease stage, MYCN status and overall and eventfree survival, are available at Figshare (Table  1 : Patient annotations, Data Citation 1). However, event-free survival data are not available for the Japanese dataset. This collection strategy has been reported already in the Supplementary Methods of the research paper originally related to this dataset  9. The patient annotations in this publication are the same as Supplementary Table  1  in the related paper  9.
DNA copy number data processing The processing steps performed on the collected data to obtain segmented copy number data is summarized in Fig. 1. Raw or normalized copy number data were collected. Raw data were mediannormalized. When necessary, chromosomal coordinates were converted from genome build hg17 or hg18 to genome build hg19 using the "liftOver" function from the R package "rtracklayer". Only for the COG cohort (Illumina platform), instead of using liftOver, we replaced the probe positions with hg19 positions downloaded from the Illumina website. These normalized, hg19 probe-level copy number data are available at GEO with accession number GSE103123 (Data Citation 2). An overview of the processing steps applied to each of the collected datasets to obtain the data available at GEO is available at Figshare (Table  2 : data processing steps, Data Citation 1).
Presence of segmental aberrations of frequently affected chromosome arms For each patient, we determined the presence of aberrations in any of the frequently affected chromosome arms, namely loss of 1p, 3p, 4p, 11q and 14q, and gain of 1q, 2p and 17q. Aberrations of (partial) chromosome arms were defined as gains and losses larger than 3 Mb, hereby excluding whole chromosome aberrations and amplifications. In case of aberrations spanning the centromere, the aberration is considered to be on the p arm if the part on p is longer and vice versa. Note that this is a computational scoring generating a general image of abundance of aberrations, not aiming to establish a detailed genomic profile for individual patients. The resulting annotations are included in the patient annotations available at Figshare (Table  1 : Patient annotations, Data Citation 1).
GSE103123 Table  3  prognostic markers Depuydt et al. 9  technical validation explore with R2 and other tools Figure  1. Overview of copy number data processing of 556 high-risk primary neuroblastoma tumors from probe-level to segmented data. Raw/normalized probe-level data were converted to hg19 genome build using liftOver, followed by median normalization and segmentation using SegAnnDB. Data are available in Gene Expression Omnibus (GEO). Segmented data are available in Table  3.