dataesr/scientific-paragraphs-categorization huggingface dataset:
The odML framework by itself is of a general nature and can easily be used in other domains of neuroscience than electrophysiology, or even other fields of science. An obvious use case would be to store metadata of neuroscientific simulation experiments. As we witness a similar increase in the complexity of in silico data, providing adequate metadata records to describe these data gains importance. However, datasets emerging from simulations differ in the composition of their metadata in terms of the more advanced technical descriptions required to capture the mathematical details of the employed models (e. g. , descriptions using NeuroML;  Gleeson et al. , 2010; Crook et al. , 2012) , and by the fact that simulations are often described on a procedural rather than a declarative level (e. g.
, descriptions based on PyNN;  Davison et al. , 2008). How these can be best linked to more generic standards for metadata capture and representation, and what level of description of metadata is adequate in this scenario, remains a matter of investigation supported by use-cases, as performed here for experimental data. A common storage mechanism for metadata of experimental and simulated data would simplify their comparison, a task that is bound to become increasingly important for the future of Computational Neuroscience. Using a well-defined, machine-readable format for metadata brings the potential for integration of the information across heterogeneous datasets, for example in larger databases or data repositories. In summary, the complexity of current electrophysiological experiments forces the scientific community to reorganize their workflow of data handling, including metadata management, to ensure reproducibility in research  (Stodden et al.
ACKNOWLEDGMENTS We commemorate  Paul Chorley  and thank him for his valuable input during the development of the metadata structures and templates. We thank  Benjamin Weyers  and  Christian Kellner  for valuable discussions. This work was partly supported by  Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB) ,  Human Brain Project  ( HBP ,  EU  Grant  604102 ),  German Neuroinformatics Node  ( G-Node ,  BMBF  Grant  01GQ1302 ),  BrainScaleS  ( EU Grant 269912 ), and  DFG   SPP Priority Program 1665  ( GR 1753/4-1  and  DE 2175/1-1 ). ANR-GRASP, CNRS , and  Riken-CNRS Research Agreement.
(2015). Star formation rates are computed within the same aperture. Galaxy sizes are quoted in physical units (e. g. pkpc), unless stated otherwise. A complete description of the EAGLE code and subgrid physics was presented by  Schaye et al. (2015) ; here we provide a concise overview. Initial conditions at z = 127 were generated using 2 nd order Lagrangian perturbation theory  (Jenkins 2010). The initial conditions were evolved using a parallel N-body smooth particle hydrodynamics (SPH) code, a modified version of Gadget-3 (based on Gadget-2, last described by  Springel 2005). The SPH implementation in EAGLE includes the Hopkins (2013) pressureentropy formulation, a time-step limiter  (Durier & Dalla Vecchia 2012) , artificial viscosity and conduction  (Dehnen & Aly 2012)  and the  Wendland (1995)  C2 kernel; this set of changes to the SPH is collectively referred to as Anarchy (Dalla Vecchia in prep.
This def- Active (@ z=0) Passive (@ z=0) From size-mass relation growth from z = 2 to z = 0 for individual galaxies selected based on their z = 0 stellar mass and SSFR. Galaxies that are active and passive at z = 0 are shown in blue and red. The size growth of these galaxies is determined based on their main progenitors at z = 2. Individual galaxies are shown when there are fewer than 10 galaxies per stellar mass bin. The shaded regions enclose the 16 th to 84 th percentiles. Dashed lines show the difference in the medians of the size -mass relations at z = 2 and z = 0, based on the data presented in Fig. 1. Blue and red colours show the median ratios of the active and passive galaxies respectively. Note that for the dashed curves, the stellar mass plotted along the x-axis applies at both z = 2 and z = 0.
Figure4. The size growth, R 50 (z = 0)/R 50 (z = 2), from redshift 2 to 0 as a function of stellar mass. Solid lines show the median size growth from z = 2 to z = 0 for individual galaxies selected based on their z = 0 stellar mass and SSFR. Galaxies that are active and passive at z = 0 are shown in blue and red. The size growth of these galaxies is determined based on their main progenitors at z = 2. Individual galaxies are shown when there are fewer than 10 galaxies per stellar mass bin. The shaded regions enclose the 16 th to 84 th percentiles. Dashed lines show the difference in the medians of the size -mass relations at z = 2 and z = 0, based on the data presented in Fig. 1. Blue and red colours show the median ratios of the active and passive galaxies respectively. Note that for the dashed curves, the stellar mass plotted along the x-axis applies at both z = 2 and z = 0.
ACKNOWLEDGEMENTS This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure. We also gratefully acknowledge  PRACE  for awarding us access to the resource Curie based in France at  Trés Grand Centre de Calcul. This work was sponsored by the  Dutch National Computing Facilities Foundation (NCF)  for the use of supercomputer facilities, with financial support from the  Netherlands Organization for Scientific Research (NWO).
RAC is a  Royal Society University Research Fellow. The research was supported in part by the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy OWNce  ([ AP P7/08 CHARM ]), the  National Science Foundation  under Grant No. NSF   PHY11-25915 , the  UK Science and Technology Facilities Council  (grant numbers  ST/F001166/1  and  ST/I000976/1 ), Rolling and Consolidating Grants to the  ICC, Marie Curie Reintegration Grant   PERG06-GA-2009-256573  and  ERC  grant agreement 278594-GasAroundGalaxies.
Data processing and meta-analytic methods Raw intensity data were obtained from 57,577 subjects from 43 separate datasets (Extended data table 2). After CNV calling and quality control (QC), 41,321 subjects were retained for analysis. In large datasets derived from multiple studies, variability in CNV detection between studies and array platforms presents a significant challenge. To minimize the technical variability across different studies, we developed a centralized pipeline for systematic calling of CNVs for Affymetrix and Illumina platforms. (Methods and Extended data figure  1 ). The pipeline included multiple CNV callers run in parallel. Data from Illumina platforms were processed using PennCNV  15  and iPattern  16. Data from Affymetrix platforms were analyzed using PennCNV and Birdsuite  17.
Discussion The present study of the PGC SCZ CNV dataset includes the majority of all microarray data that has been generated in genetic studies of SCZ to date. In this, the best body of evidence to date with which to evaluate CNV associations, we find definitive evidence for eight loci and we find significant evidence for a contribution from novel CNVs conferring both risk and protection. The complete results, including CNV calls and statistical evidence at the gene or probe level, can be viewed using the PGC CNV browser (URLs). Our data suggest that the novel risk loci that can be detected with current genotyping platforms lie at the ultra-rare end of the frequency spectrum and still larger samples will be needed to identify them at convincing levels of statistical evidence. Collectively, the eight SCZ risk loci that surpass genome-wide significance are carried by a small fraction (1.
4%) of SCZ cases in the PGC sample. We estimate 0. 85% of the variance in SCZ liability is explained by carrying a CNV risk allele within these loci (Supplementary Results). As a comparison, 3. 4% of the variance in SCZ liability is explained by the 108 genome-wide significant loci identified in the companion PGC GWAS analysis. Combined, the CNV and SNP loci that have been identified to date explain a small proportion (<5%) of heritability. The large dataset here provides an opportunity to evaluate the strength of evidence for a variety of loci where an association with schizophrenia has been reported previously. Of 33 published findings from the recent literature, we find evidence for 14 loci (P < 0. 05, Extended data table 5); thus, nearly half of the existing candidate loci are supported by our data.
However we also find a lack of evidence for many. A lack of strong evidence in this dataset (which includes samples that overlap with many of the previous studies) may in some cases simply reflect that statistical power is limited for very rare variants, even in large samples. However, it is likely that some of these original findings represent spurious associations. Indeed, the loci that are not supported by our data consist largely of loci for which the original statistical evidence was modest (Extended data table 5). Thus, our results help to refine the list of promising candidate CNVs. Continued efforts to evaluate the growing number of candidate variants has considerable value for directing future research efforts focused on specific loci. Novel candidate loci meeting suggestive criteria in this study highlight strong candidate loci that have not been previously implicated in SCZ.
The large CNV dataset here allows a more detailed view of the synaptic network and highlights subsets of genes account for the excess deletion burden in SCZ, including synaptic cell adhesion and scaffolding proteins, glutamatergic ionotropic receptors and protein complexes such as the ARC complex and DGC. Modest CNV evidence implicating Dystrophin (DMD) and its binding partners is intriguing given that the involvement of certain components of the DGC have been postulated  29, 30  and disputed  31  previously. Larger studies of CNV are needed to define a role for this and other synaptic sub-networks in SCZ. This study represents a milestone. Large-scale collaborations in psychiatric genetics have greatly advanced discovery through genome-wide association studies. Here we have extended this framework to rare CNVs.
Our knowledge of the contribution from lower frequency variants gives us confidence that the application of this framework to large newly acquired datasets has the potential to further the discovery of loci and identification of the relevant genes and functional elements. The PGC CNV resource is now publicly available through a custom browser at http://pgc. tcag. ca/gb2/gbrowse/pgc_hg18/. All seventeen loci listed contain at least one gene with Benjamini-Hochberg false discovery rate (BH-FDR) < 0. 05 in the gene-based test, with eight loci containing at least one gene surpassing the family-wise error rate (FWER) < 0. 05. Genomic positions listed are using hg18 coordinates. For putative CNV mechanisms, non-allelic homologous recombination (NAHR) and non-homologous end joining (NHEJ) are listed as the likely genomic feature driving CNV formation at each locus.
Extended data Table 5: CNV probe-level results -Previously reported CNVs Probe-level association results for all previously reported CNVs from genome-wide scans of SCZ. We report association results from the SCZ residual phenotype and from a CMH test stratified by genotyping platform. CNV loci in bold make up previously implicated loci, in which the most recent published p-value surpassed genome-wide correction. Quality control filtering: Following merging we applied filtering criteria for removal of arrays with excessive probe variance or GC bias and removal of samples with mismatches in gender or ethnicity or chromosomal aneuploidies. For Affymetrix data, we extracted the MAPD and waviness-sd from the APT summary file. We also calculated the proportion of each chromosome (excluding chrY) tagged as copy number variable and computed the number of CNV calls made for each sample.
We then retained experiments if each of these measures was within 3 SD of the median. For Illumina data, we extracted LRRSD, BAFSD, GCWF (waviness) from PennCNV log files. As with the Affymetrix data, we calculated the proportion of each chromosome (excluding chrY) tagged as copy number variable and computed the number of CNV calls made for each sample. We retained samples if each of the above measures was within 3 SD of the median. For both Illumina and Affymetrix datasets, large CNVs that appeared artificially split were combined together if one of the methods detected a CNV spanning the gap.
Extended However, samples where > 10% of the chromosome was copy number variable were excluded as possible aneuploidies. Further, we excluded CNVs that: 1) spanned the centromere or overlapped the telomere (100 kb from the ends of the chromosome); 2) had > 50% of its length overlapping a segmental duplication; 3) had >50% overlap with immunoglobulin or T cell receptor. The final filtered CNV dataset was annotated with Refseq genes (transcriptions and exons). After this stage of quality control (QC), we had a total of 52,511 individuals, with 27,034 SCZ cases and 25,448 controls. Filtering for rare CNVs: To make our final dataset of rare CNVs for all subsequent analysis we universally filtered out variants that present at >= 1% (50% reciprocal overlap) frequency in cases and controls combined.
Post-CNV Calling QC Overview: A number of steps were undertaken after CNV calling and initial filtering QC to minimize the impact of technical artifacts and potential confounds. In summary, we removed individuals not present in the PGC2 GWAS analysis 1 , removed datasets with non-matching case or control samples that could not be reconciled using consensus platform probes, and removed any additional outliers with respect to overall CNV burden, CNV calling metrics, or SCZ phenotype residuals. All steps are described in more detail below. Merging with GWAS cohort: By matching the unique sample identifiers, we retained only individuals that also passed QC filtering from the companion PGC GWAS study in Schizophrenia 1. This step filtered out samples with low-quality SNP genotyping, related individuals, and repeated samples across cohorts.
An additional benefit of the PGC analytical framework is the ability to account for population stratification across cohorts using principal components derived from probe level analysis. After the post-CNV calling quality control steps described below, we re-calculated principal components using the Eigenstrat software package  6. Sample information and subsequent CNV and GWAS filtered sample sets are presented in Extended data table 2. In the process of matching to the GWAS-specific cohort, all individuals of non-European ancestry were removed from analysis (~5. 8% of the post-QC sample comprising three separate datasets). We also removed 42 samples that had discordant phenotype designations between the GWAS analysis and CNV genotype submission. Individual dataset removal: Some datasets submitted to the PGC consisted of only case or control samples, affected trios, or recruited external samples as controls.
This asymmetry in case-control ascertainment and genotyping can present serious biases for CNV analysis, as the sensitivity to detect CNV will vary considerably across genotyping platforms, as well as within dataset and genotyping batch. Unlike imputation protocols commonly used for SNP genotyping, there is no equivalent process to infer unmeasured probe intensity from nearby markers. We took a number of steps to identify and remove datasets that showed strong signs of case-control ascertainment or genotyping asymmetry: 1) Identify genotyping platforms where case-control ratio was not between 40-60% 2) Where possible, merge similar genotyping platforms using consensus probes prior to CNV-calling pipeline in order to improve case-control ratio. 3) Examine overall CNV burden and association peaks for spurious results 4) Remove datasets that remain problematic due to unusual CNV burden or multiple spurious CNV associations.
The genotyping platforms identified and processed are listed in Extended data table  2. We were able to combine the Illumina OmniExpress and Illumina OmniExpress plus Exome Chip platforms with success by removing probe content specific to the Exome chip platform. We removed the caws Affymetrix 500 datasets due to a number of strong CNV association peaks not seen in any other dataset. We also remove the fii6 dataset due to a 2-fold CNV burden in cases relative to controls. In order to improve casecontrol balance, we had to remove the affected proband trio datasets (boco, lacw, and lemu) in the Illumina 610 platform, and the control-only uclo dataset in the Affymetrix 500 platform.
Thus, we focused on the main intensity metrics -median absolute pairwise difference (MAPD) for projects genotyped on the Affymetrix 6. 0 platform, and Log R Ratio standard deviation (LRRSD) in all other genotyping platforms. Among Affymetrix 6. 0 datasets, MAPD did not differ between in cases and controls (t=1. 14, p = 0. 25). However, among non-Affymetrix 6. 0 datasets, LRRSD showed significant differences between cases and controls (t=-35. 3, p < 2e  -16  ), with controls having a higher standardized mean LRRSD (0. 227) than cases (-0. 199). To control for any spurious associations driven by CNV calling quality, we included LRRSD (MAPD for Affymetrix 6. 0 platforms) as a covariate in downstream analysis. CNV metrics were normalized with their genotyping platform prior to inclusion in the combined dataset.
Regression of potential confounds on case-control ascertainment The PGC cohorts are a combination of many datasets drawn from the US and Europe, and it is important to ensure that any bias in sample ascertainment does not drive spurious association to SCZ. In order to ensure the robustness of the analysis, we controlled for a number of covariates that could potential confound results. Burden and gene-set analyses included covariates in a logistic regression framework. Due to the number of tests run at probe level association, we employed a step-wise logistic regression approach to allow for the inclusion of covariates in our case-control association, which we term the SCZ residual phenotype. Covariates include sex, genotyping platform, CNV metrics, and ancestry principal components derived from SNP genotypes on the same samples in a previous study  1.
We were unable to control for dataset or genotyping batch, as a subset of the contributing datasets are fully confounded with case/control status. CNV metric is normalized within genotyping platform prior to inclusion in the logistic model. Only principal components that showed a significant association to small CNV burden were used (small CNV being defined as autosomal CNV burden with CNV < 100 kb in size). Among the top 20 principal components, only the 1 st , 2 nd , 3 rd , 4 th , and 8 th principal component showed association with small CNV burden (with p < 0. 01 used as the significance cutoff). To.
Gene-set burden enrichment analysis: gene-sets Gene-sets with an a priori expectation of association to neuropsychiatric disorders were compiled based on gene annotations (Gene Ontology and curated pathway databases, downloaded June 2013) and published article materials (for details, see Extended Data were aggregated while striving to preserve biological homogeneity, so to have roughly equal-sized sets (2,600-1,300 genes). For all gene-sets, gene identifiers in the primary source were mapped to Entrez-gene identifiers using the R/Bioconductor package org. Hs. eg. db.
While permutation methods used to generate genome-wide cutoffs accurately reflect the testing space among observed CNVs (very rare CNVs have little to no contribution to the family-wise error rate), we wanted to estimate the proportion of CNV detectable at the probe level. Under our current analytical design and sample size, we calculated the power to detect associated CNV across various MAFs and effect sizes and determine the proportion of association tests capable of surpassing genome-wide correction. We simulated CNVs within our dataset (21094 cases and 20227 controls) and regressed them using the same association design with SCZ residual phenotypes. We simulated various effect sizes by randomly sampling cases and controls at different probabilities as We define statistical power as the proportion of simulations surpassing genome-wide significance.
For a fully penetrant risk CNV, we require a MAF of ~6e -4 (or about 25 CNV) to achieve 80% detection power. For CNV with a genotype relative risk (GRR) of 10, we require a MAF of 1e -3 (or at least 41 CNV) to achieve 80% detection power. Looking across the landscape of CNVs tested, on the whole about 10% of deletion or duplication CNV breakpoints reach a frequency greater than 1e -3 in the sample. On the other extreme, a CNV with MAF of. 005 (or at least 206 CNV) and a GRR of 2 will only be detected 58% of the time. association, we also ran additional tests controlling for individual dataset, splitting by sex, and examining a dosage model, whereby copy number is measured with one copy for deletion, two copies for no CNV, and three copies for duplication. We also examined significant CNV loci in an unfiltered CNV call set, using CNVs called prior to the removal of common CNVs (MAF > 1%) and CNV overlapping segmental duplications.
We further evaluated the associated regions by determining the concordance of calls within the call set with those determined by unsupervised clustering. Call set CNVs were defined as CNVs with at least a 50% overlap with regions in Table  1. We restricted this analysis to 26,959 samples across six cohorts (14,419 Affymetrix 6. 0, 12,540 Illumina platforms; 1. 1:1 case:control ratio). Features for clustering included the median logR ratio (mLRR) and the median logR ratio of the chromosome for which a locus resides in, controlling for large chromosomal abnormalities. We implemented Density-Based Spatial Clustering of Applications (DBSCAN) found in the python scikit-learn library (http://scikit-learn. org) because of high sensitivity to detect outliers in clusters. For each novel region and within each cohort, genotypes were assigned to every sample based on the DBSCAN defined cluster.
Proportion of variance in SCZ explained by top CNV loci To measure the proportion of variance explained on the liability scale of SCZ, we estimated the overall heritability of liability (or logRR genetic variance) explained by the eight CNV loci surpassing genome-wide significance. All eight loci were collapsed into a single signal. Two SCZ affected individuals were found to carry two CNVs in these loci, and their contribution was only counted once. In sum, we observed 298 SCZ patients with a CNV in these regions (1. 4% of the total SCZ affected sample), and 29 controls (0. 1%; CMH stratified OR = 10. 1). To estimate the variance in SCZ liability explained by loci surpassing genome-wide correction, we calculated the heritability of liability using the INDI-V online tool (cnsgenomics. com/software) described in 4 using an overall disease risk of 1% and a sibling recurrence risk of 8.
calculate the SCZ residual phenotype, we first fit a logistic regression model of covariates to affection status, and then extracted the Pearson residual values for use in a quantitative association design for downstream analyses. Residual phenotype values in cases are all above zero, and controls below zero, and are graphed against overall kb burden in Extended data figure We removed three individuals with an SCZ residual phenotype greater than three (or negative three in controls). After the post-processing round of QC, we retained a dataset with a total of 41,321 individuals comprising 21,094 SCZ cases and 20,227 controls.