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215 results for “summary statistics”
Full summary statistics of mixQTL for GTEx v8 Uterus
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Genome-wide association summary statistics for sex- and age-specific analysis of chronic back pain
<p>The dataset comprises summary-level statistics for age- and sex-specific genome-wide association study of chronic back pain (cBP) in individuals of European descent from UK Biobank (<a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a>). The study was carried out under UK Biobank approved project #18219. </p> <p><strong>The dataset accompanies the paper (please cite if using the dataset):</strong></p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/33021770/">Freidin, Maxim B.; Tsepilov, Yakov A.; Stanaway, Ian B.; Meng, Weihua; Hayward, Caroline; Smith, Blair H.; Khoury, Samar; Parisien, Marc; Bortsov, Andrey; Diatchenko, Luda; Børte, Sigrid; Winsvold, Bendik S.; Brumpton, Ben M.; Zwart, John-Anker; HUNT All-In Pain; Aulchenko, Yurii S.; Suri, Pradeep; Williams, Frances M.K. Sex- and age-specific genetic analysis of chronic back pain. Pain. 2020. doi:10.1097/j.pain.0000000000002100.</a></p> <p>The phenotype of cBP was defined as back pain for 3+ months. Linear mixed-effects additive model was fitted adjusting for age, genotyping array type, and 10 genetic PCs provided by UK Biobank. The following filters were applied: minor allele frequency >0.001, genotyping and individual call rates >0.98%, imputation quality score (INFO) >0.7. GWAS were carried out in males and females separately in the whole sample (<strong>allages</strong>) as well as in groups of younger than 65 years (<strong>under65</strong>) and 65+ years old (<strong>65plus</strong>) as detailed in the paper. Accordingly, 6 files are deposited here, corresponding to each group. </p> <p><strong>Column headers:</strong></p> <p>SNP, SNP rsID </p> <p>CHR, chromosome</p> <p>BP, genomic position (GRCh37 build)</p> <p>EA, effect allele (coded as "1")</p> <p>OTHER, other allele (coded as "0")</p> <p>A1FREQ, frequency of effect allele</p> <p>INFO, imputation quality</p> <p>BETA, effect size (for effect allele)</p> <p>SE, standard error of effect size</p> <p>PVAL, p-value for association</p>
G2G-EBV GWAS summary statistics
<p>G2G results from manuscript titled:</p> <p><strong>The influence of human genetic variation on Epstein-Barr virus sequence diversity</strong></p> <p>The GWAS result files (*.mlma) contains:<br> chromosome, SNP, physical position, reference allele (the coded effect allele), the other allele, frequency of the reference allele, SNP effect, standard error and p-value (<a href="https://cnsgenomics.com/software/gcta/#MLMA">GCTA-MLMA</a>).<br> <br> The scripts used to generate the GWAS results, are available here: <a href="https://github.com/sinarueeger/G2G-EBV-manuscript">github.com/sinarueeger/G2G-EBV-manuscript</a>.</p>
Full Summary Statistics - TBDAR Genome-to-genome Study
<p><strong>TBDAR_G2G_Full_Summary_Stats.tar.gz: </strong>Full summary statistics (See README for details)</p> <p><strong>Mtb_Human_IDs.txt: </strong>Mapping between M.tb and human sequencing IDs, to faciliate joint analyses. </p> <p><strong>Supple_Data1.csv</strong>: <span lang="EN">G2G associations that meet the significance threshold of 5 × 10⁻⁸ </span></p>
Milo csQTL mapping Summary Statistics
<p>Summary statistics from Milo cell state quantitative trait loci mapping analysis on human peripheral immune cells from <a>Randolph <i>et al.</i></a> Each folder contains csQTLs per chromosome. Files are names 'chromosome_position_minorallele_majorallele_locus.tsv.gz'<i>. </i>Data are block gzip compressed and tabix indexed for rapid access based on SNP position (GRCh38 coordinates).</p>
Full eQTL summary statistics for the PAUSE trial
<p>This dataset contains full eQTL summary statistics for the study 'Immunosuppression causes dynamic changes in expression QTLs in psoriatic skin' (<em>Nat.Comm., </em>2023). The study analyzes 375 skin samples from patients with psoriasis. For each SNP-gene pair, the dataset provides information listed below.</p> <ol> <li><strong>ID</strong>: the variant identifier, with chromosome number, position, ref and alt alleles.</li> <li><strong>Estimate</strong>: estimate</li> <li><strong>Std.Error</strong>: standard error</li> <li><strong>df</strong>: degrees of freedom</li> <li><strong>t value</strong>: t-value of the association</li> <li><strong>Pr(>|t|): </strong>p-value of the association</li> <li><strong>Gene: </strong>Ensembl gene ID</li> <li><strong>CHROM: </strong>the variant chromosome</li> </ol>
Summary statistics from "Sex-Specific Causal Relations between Steroid Hormones and Obesity—A Mendelian Randomization Study"
<p>GWAMA summary statistics of four steroid hormone levels and one steroid hormone ratio using fixed-effect model.</p> <p>When using this data, please cite: Pott J, Horn K, Zeidler R, et al.. Sex-Specific Causal Relations between Steroid Hormones and Obesity - A Mendelian Randomization Study. <em>Metabolites</em> <strong>2021</strong>, <em>11</em>, 738. https://doi.org/10.3390/metabo11110738</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
Summary statistics from "Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease"
<p>GWAMA summary statistics of four steroid hormone levels using fixed-effect model and GWAS summary statistics of four other steroid hormones.</p> <p>When using this data, please cite: Pott J, Bae YJ, Horn K, et al.. Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease. <em>J Clin Endocrinol Metab</em> <strong>2019</strong> Nov 1;104(11):5008-5023. doi: 10.1210/jc.2019-00757</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>effect_allele</li> <li>other_allele</li> <li>effect_allele_freq</li> <li>min_info (minimal info score across all used studies)</li> <li>n (sample size per SNP)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>CochransQ (only in GWAMA; SNP heterogeneity across studies)</li> <li>pCochransQ (only in GWAMA; p-value of Cochrans Q value)</li> </ul>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
Summary Statistics from "Genetically regulated gene expression and proteins revealed discordant effects" (LWAS of biomarker)
<p>Summary statistics of 92 blood protein levels. The corresponding publication is currently under revision.</p> <p> The zipped txt file is tab-delimited and contains the following columns:</p> <ul> <li>protein: protein name abbreviation</li> <li>cytoband: genomic region</li> <li>gene: gene name abbreviation</li> <li>setting: either "combined" (adj. for sex & age) or sex-stratified ("males", "females"; adj. for age)</li> <li>variant_id_hg19: SNP ID according to hg19</li> <li>variant_id_hg38: SNP ID according to hg19</li> <li>chr: chromosome</li> <li>pos_hg19: base position according to hg19</li> <li>pos_hg38: base position according to hg19</li> <li>effect_allele: also known as counted allele in additive model</li> <li>other_allele: not-counted allele</li> <li>eaf: effect allele frequency</li> <li>maf: minor allele frequency</li> <li>info: imputation info score</li> <li>n_samples: number of samples</li> <li>beta: effect estimate</li> <li>se: standard error</li> <li>zscore: Z-statistic</li> <li>pvalue: p-value</li> <li>FDR: FDR by gene and setting</li> <li>BBFDR: hierarchical FDR by setting</li> <li>hierFDR: TRUE if SNP is significant after hierarchical FDR</li> </ul>
CONTENT -- Multi-context genetic modeling TWAS and eAssociation summary statistics
<p>We provide the summary statistics of running CONTENT, the context-by-context approach, and UTMOST on over 22 phenotypes. The phenotypes are listed in the manuscript, and their respective studies and sample size can be found in a table under the supplementary section of the manuscript. All 3 methods were trained on GTEx v7 as well as CLUES, a single-cell RNA sequencing dataset of PBMCs. The data include the gene name, model, cross-validated R^2, prediction pvalue, TWAS p value, TWAS Z score, and a column titled "hFDR" indicating whether the association was statistically significant while employing hierarchical FDR. The benefits of employing such an approach for all methods can be found in the manuscript.</p> <p> </p> <p>We also include the eAssociations that we obtain by training prediction models on GTEx and CLUES alone. For the CxC and UTMOST approaches, these files contain the gene, context, pvalue and adjusted R^2. For CONTENT, these include the gene, context and pvalue and adjusted R^2 for each CONTENT model--the column names are described like a regression of y~x, rsq_y_x, so rsq_observed_full is the adjusted R^2 from regressing the observed expression onto the cross-validated full model predictions. In cases where the R^2 is higher from the specific or shared models, it's best to use either of those rather than the full model for out of sample prediction.</p>
GWAS summary statistics and code for "Sequence variants affecting voice pitch in humans"
<p>Contents: GWAS summary statistics for voice pitch (median F0 in reading) and code for acoustic analysis</p> <p>Please refer to the corresponding publication:</p> <p>Gisladottir et al. Sequence variants affecting voice pitch in humans. <em>Science Advances</em></p> <p>The GWAS summary statistics is also available at: https://www.decode.com/summarydata/</p> <p>The code for acoustic analysis is also available at: https://github.com/cadia-lvl/deCODE</p> <p> </p> <p> </p> <p> </p>
QR GWAS summary statistics for 39 quantitative traits in the UK Biobank
<p>Quantile regression (QR) GWAS summary statistics from the study "Genome-wide discovery for biomarkers using quantile regression at biobank scale". The preprint is available at <a href="https://doi.org/10.1101/2023.06.05.543699" target="_blank" rel="noopener">https://doi.org/10.1101/2023.06.05.543699</a>. </p> <p><strong>List of traits</strong></p> <p>A comma-delimited text file, QRGWAS.Traits_n39.csv, includes the list of 39 quantitative traits from the UK Biobank reported in the QR GWAS analyses above.</p> <p><strong>Summary statistics</strong></p> <p>The tab-delimited text files are QR GWAS summary statistics, which are bgzip compressed (.tsv.gz files) and tabix indexed (.tbi files).</p> <ul> <li>Column "CHR": chromosome</li> <li>Column "POS": based pair position</li> <li>Column "ID": variant ID</li> <li>Column "REF": non-effect allele</li> <li>Column "ALT": effect allele tested in GWAS</li> <li>Column "EAF": frequency of the effect allele</li> <li>Column "N": sample size</li> <li>Column "P_QR": integrated p-value of the quantile regression (QR) model across multiple quantile levels.</li> <li>Column "P_LR": p-value of the linear regression (LR) association statistic</li> <li>Columns from "P_Q10" to "P_Q90": quantile-specific QR p-value for the quantile levels 0.1, 0.2, ..., 0.9 (10th, 20th, ..., 90th quantiles).</li> </ul>
Optimized summary-statistic-based single-cell meta-analysis. Input files
<p>This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project. </p> <p> </p>
Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex B to the technical report on the raw primary commodity model:</strong></p> <p>Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B:</p> <p>Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population)</p> <p>Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population)</p> <p>Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only)</p> <p>Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only)</p> <p>Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo)</p> <p>Table B.6 :Contribution of processed products to the average chronic RPC consumption</p>
Summary statistics - Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits
<p>Summary statistics for all trans-acting/target gene pairs tested in our manuscript.</p> <p>Preprint available: <a href="https://doi.org/10.1101/471748">https://doi.org/10.1101/471748</a></p>
Evaluation of the shucking of certain species of scallops contaminated with domoic acid with a view to the production of edible parts meeting the safety requirements foreseen in the Union legislation - Summary statistics on occurrence and consumption data and exposure assessment results
<p>DomoicAcid_Raw_Occurrence_Data.CSV contains the raw occurrence dataset on Domoic Acid contaminant in scallops as extracted from EFSA DWH on the 9 June 2020, 16,369 samples presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in .csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: outcome) to address the issue (e.g. delete record or update values in specific fields).</p> <p>The link to the catalogues of controlled terminologies can be found under "Related identifiers”.</p> <p><strong>Annex_</strong> DomoicAcid</p> <p>Table of contents</p> <p><br> Table A1</p> <p>Description of FoodEx2 codes used to describe scallop species and their anatomical parts</p> <p>Table A2</p> <p>Data cleaning steps applied to occurrence data on domoic acid in scallops</p> <p>Table A3</p> <p>Percentage of Left-Censored data and descriptive statistics for Limits of detection (LODs) and Limits of quantification (LOQs) for domoic acid in scallops (mg/kg)</p> <p>Table A4</p> <p>Descriptive statistics for domoic acid in scallops (mg/kg) as reported in the cleaned database (statistics weighted by number of units per sample)</p> <p>Table A5</p> <p>Descriptive statistics of body tissue weights (g) of scallops as submitted by data providers</p>
Full expression and splicing QTL summary statistics for three human spinal cord segments
<p>This dataset is part of the manuscript: <strong>"Integrative genetic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes</strong>" by Jack Humphrey, et al.</p> <p>The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and either gene expression or splicing phenotypes generated from RNA-seq of post-mortem spinal cord sections. All QTLs were mapped with TensorQTL.</p> <p>Nominal association files are coordinate sorted, bgzip-compressed and tabix-indexed tab-separated variable files. Top association files are gzip-compressed tab-separated variable files.</p> <p>Sample sizes for each dataset are as follows:</p> <p>Cervical spinal cord n = 216.</p> <p>Lumbar spinal cord n = 197.</p> <p>Thoracic spinal cord n = 68.</p> <p>Description of files:<br> CervicalSpinalCord_expression_peer30_gene.cis_qtl.txt.gz - <strong>Top per-gene </strong>eQTL summary statistics from cervical spinal cord <br> CervicalSpinalCord_expression_peer30_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> eQTL summary statistics from cervical spinal cord <br> CervicalSpinalCord_splicing_peer15_cluster.cis_qtl.txt.gz - <strong>Top per-cluster </strong>sQTL summary statistics from cervical spinal cord<br> CervicalSpinalCord_splicing_peer15_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> sQTL summary statistics from cervical spinal cord<br> LumbarSpinalCord_expression_peer30_gene.cis_qtl.txt.gz - <strong>Top per-gene </strong>eQTL summary statistics from lumbar spinal cord<br> LumbarSpinalCord_expression_peer30_gene.cis_qtl_nominal_tabixed.tsv.gz -<strong> Full nominal</strong> eQTL summary statistics from lumbar spinal cord<br> LumbarSpinalCord_splicing_peer10_cluster.cis_qtl.txt.gz - <strong>Top per-cluster </strong>sQTL summary statistics from lumbar spinal cord<br> LumbarSpinalCord_splicing_peer10_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> sQTL summary statistics from lumbar spinal cord<br> ThoracicSpinalCord_expression_peer10_gene.cis_qtl.txt.gz - <strong>Top per-gene </strong>eQTL summary statistics from thoracic spinal cord<br> ThoracicSpinalCord_expression_peer10_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> eQTL summary statistics from thoracic spinal cord<br> ThoracicSpinalCord_splicing_peer5_cluster.cis_qtl.txt.gz - <strong>Top per-cluster </strong>sQTL summary statistics from thoracic spinal cord<br> ThoracicSpinalCord_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - <strong>Full nominal</strong> sQTL summary statistics from thoracic spinal cord</p> <p>als_snps_maf0.01_alleles.tsv.gz<strong> - Allele information</strong> for all SNPs tested in the eQTL and sQTL analysis</p> <p>Table columns are formatted as follows:</p> <p>Nominal QTL results include all SNP-gene pairs tested using either a 1Mb window from each side of the transcription start site (TSS) of the gene (eQTLs) or 100kb either side of the middle of the splicing cluster (sQTLs). Table columns are formatted as follows:</p> <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30) for eQTL, or the ID of the splice junction for sQTLs, splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30),separated by a colon</li> <li>variant_id - SNP tested for association (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>maf - minor allele frequency in cohort</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> </ul> <p>Permuted QTL results include only the top SNP-gene association for each gene (eQTL) or cluster (sQTL). Table columns are formatted as follows:</p> <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30), or the coordinates of the cluster, formatted as coordinate, splicing cluster id, and gene Ensembl id (GENCODE v30), separated by a colon</li> <li>num_var - total number of variants tested in <em>cis</em></li> <li>beta_shape1 - first parameter value of the fitted beta distribution</li> <li>beta_shape2 - second parameter value of the fitted beta distribution</li> <li>true_df - effective degrees of freedom the beta distribution approximation</li> <li>pval_true_df - empirical <em>P</em>-value for the beta distribution approximation</li> <li>variant_id - ID of the top variant (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>maf -minor allele frequency in MiGA cohort</li> <li>ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not)</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>pval_perm - first permutation <em>P</em>-value directly obtained from the permutations with the direct method</li> <li>pval_beta - second permutation <em>P</em>-value obtained via beta approximation. This is the one to use for downstream analysis</li> <li>qval - Storey q-value derived from pval_beta (FDR adjusted)</li> <li>pval_nominal_threshold - nominal <em>P</em>-value threshold for calling a variant-gene pair significant for the gene</li> </ul> <p>Allele Information for each variant:</p> <ul> <li>CHROM - chromosome position of the variant</li> <li>POS - position of the variant in the chromosome</li> <li>REF - reference allele (GRCh38)</li> <li>ALT - alternative allele (this is the <strong>effect</strong> allele in the eQTL and sQTL analysis)</li> <li>ID - variant id (rsid or chr:position:ref:alt)</li> </ul>
Summary statistics of cell-type specific cis-eQTLs in eight brain cell-types
<p>This dataset contains eQTL summary statistics for all SNPs-gene pairs in 8 major brain cell types (within 1MB window surrounding the TSS of each expressed gene). For each cell type, there is one file per chromosome.</p> <p>Column description:</p> <p>1. Gene_id</p> <p>2. SNP_id</p> <p>3. Distance to TSS</p> <p>4. Nominal p-value</p> <p>5. Beta</p> <p>In addition, a file contains the SNP positions (snp_pos.txt) and tested allele.</p> <p>Update February 2023: The summary statistics from our 'tissue-like' analysis were added (pb[1-22].gz). These file contain eQTL summary statistics after aggregating all reads from all nuclei for each individual (instead of per cell type).</p>
Supplementary Data to *Informative and adaptive distances and summary statistics in approximate Bayesian computation*
<p>Supplementary code and data to <strong>Informative and adaptive distances and summary statistics in approximate Bayesian computation</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_slad, of which this archive is a snapshot.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.