Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,227
datasets available to search
ShareScore release 0.9.0
Dataset results
2,227 results for “Tissue expression”
Expression Data from "A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis, pulmonary fibrosis, and pulmonary arterial hypertension"
<p>Normalized expression data (PCL files) and labeled PCL files (LPCL) that contain the WGCNA coexpression module assignment from Taroni, et al. A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis, pulmonary fibrosis, and pulmonary arterial hypertension. <em>bioRxiv</em>. doi: 10.1101/038950</p> <p>See also Gene Expression Omnibus under the following accession numbers: GSE9285, GSE32413, GSE45485, GSE59785, GSE76806, GSE76807, GSE76808, GSE48149, GSE68698, GSE19617, and GSE22356. </p>
Heat shock protein gene expression varies among tissues and populations in free living birds
<p>Climate change is dramatically altering our planet, yet our understanding of mechanisms of thermal tolerance is limited in wild birds. We characterized natural variation in heat shock protein (HSP) gene expression among tissues and populations of free-living Tree Swallows (<em>Tachycineta bicolor</em>). We focused on HSPs because they prevent cellular damage and promote recovery from heat stress. We used quantitative PCR to measure gene expression of three HSPs, including those in the HSP70 and HSP90 families that have robust experimental connections to heat in past literature. First, to evaluate how tissues and, by extension, the functions that they mediate, may vary in their thermal protection, we compared HSP gene expression among neural and peripheral tissues. We hypothesized that tissues with particularly vital functions would be more protected from heat as indicated by higher HSP gene expression. We found that brain tissues had consistently higher HSP gene expression compared to the pectoral muscle. Next, we compared HSP gene expression across four distinct populations that span over 20 degrees of latitude (>2300 km). We hypothesized that the more southern populations would have higher HSP gene expression, suggesting greater tolerance of, or experience with, warmer local conditions. We observed largely higher HSP gene expression in more southern populations than northern populations, although this pattern was more striking at the extremes (southern Indiana vs. Alaska) and it was stronger in some brain areas than others (ventromedial telencephalon vs. hypothalamus). These results shed light on the potential mechanisms that may underlie thermal tolerance differences among populations or among tissues.</p>
GTEx: DICOM converted whole slide hematoxylin and eosin stained images from the Genotype-Tissue Expression (GTEx) Project
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=gtex" target="_blank" rel="noopener">GTEx</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p>The<a href="https://commonfund.nih.gov/GTEx"> Genotype-Tissue Expression (GTEx) Project</a> established a data resource and tissue bank to study the relationship between genetic variants and gene expression in multiple human tissues and across individuals. The project included contributions from numerous groups with diverse expertise in biospecimen collection and processing, pathology review, molecular analysis, and data management. The contributors are collectively called the GTEx Consortium.</p> <p>GTEx collected a total of 26,468 unique tissue samples from 50+ different tissue types, from 956 healthy postmortem donors. The standardized biospecimen collection and analysis practices applied during the study served to minimize preanalytical variability associated with specimen-related factors and their potential impact on analytic endpoints. Each GTEx tissue was divided into two tissue blocks, one for histology and one for molecular analysis; both tissue blocks were preserved in PAXgene Tissue Fixative (Qiagen) solution for 6 to 24 hours, followed by PAXgene Tissue Stabilizer (Qiagen) as specified in the project-specific<a href="https://biospecimens.cancer.gov/resources/sops/library.asp"> standard operating procedures</a>. Tissue blocks were processed and embedded in paraffin at the GTEx central repository at the Van Andel Institute (MI) and hematoxylin and eosin–stained slides were generated from all GTEx donors. Digitally scanned whole slide images of PAXgene-fixed/stabilized, paraffin-embedded tissue sections were created using Aperio Scanscope software (Leica Biosystems). The digital images were then reviewed and annotated by one of four board-certified pathologists assigned to the GTEx study. There are a total of 25,503 digital histology images in the GTEx collection.</p> <p>GTEx was supported by the NIH Common Fund (2010 – 2019). Additional resources include the<a href="https://gtexportal.org/home/biobank"> GTEx Biobank</a>, the<a href="https://gtexportal.org/home/"> GTEx Portal</a>, and the full dataset at<a href="https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v9.p2"> dbGaP</a> (accession number phs000424).</p> <p>Please refer to the listed GTEx publications below for more details [2-7]. </p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>gtex-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>gtex-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>gtex-idc_v19-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <div>Please acknowledge the GTEx Consortium in any published work that includes the images. A sample statement for the acknowledgment of the Genotype-Tissue Expression (GTEx) Project dataset(s) follows.</div> <p>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (<a href="http://commonfund.nih.gov/GTEx" target="_blank" rel="noopener">commonfund.nih.gov/GTEx</a>). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the Broad Institute of MIT and Harvard. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc. (HHSN261200800001E). The Brain Bank was supported with supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941& MH101814), the University of Chicago (MH090951, MH090937, MH101825, & MH101820), the University of North Carolina - Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822).</p> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] Sobin, L., Barcus, M., Branton, P. A., Engel, K. B., Keen, J., Tabor, D., Ardlie, K. G., Greytak, S. R., Roche, N., Luke, B., Vaught, J., Guan, P. & Moore, H. M. Histologic and quality assessment of genotype-Tissue Expression (GTEx) research samples: A large postmortem tissue collection. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0467-OA">10.5858/arpa.2023-0467-OA</a></p> <p>[3] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).</p> <p>[4] GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).</p> <p>[5] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).</p> <p>[6] Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., Guan, P., Korzeniewski, G. E., Lockhart, N. C., Rabiner, C. A., Rao, A. K., Robinson, K. L., Roche, N. V., Sawyer, S. J., Segrè, A. V., Shive, C. E., Smith, A. M., Sobin, L. H., Undale, A. H., Valentino, K. M., Vaught, J., Young, T. R., Moore, H. M. & GTEx Consortium. A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).</p> <p>[7] Branton, P. A., Sobin, L., Barcus, M., Engel, K. B., Greytak, S. R., Guan, P., Vaught, J. & Moore, H. M. Notable histologic findings in a ‘normal’ cohort: The National Institutes of Health Genotype-Tissue Expression (GTEx) project. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0468-OA">10.5858/arpa.2023-0468-OA</a></p>
Meta-analysis of scRNA-seq Co-expression in Human Neural Organoids Reveals High Variability in Recapitulating Primary Tissue
<p>Contains all code and data for Werner and Gillis, Meta-analysis of scRNA-seq Co-expression in Human Neural Organoids Reveals High Variability in Recapitulating Primary Tissue, 2024. </p> <p>Additionally, the code and data for this paper can be found at https://github.com/JonathanMWerner/meta_organoid_analysis with an easy to view github markdown file containing all the code used to generate all figure panel plots at https://github.com/JonathanMWerner/meta_organoid_analysis/blob/main/figure_plots_with_data_code.md.</p> <p>Due to file size limits on github, there are several data files not available on github, but are available here on zenodo in the data_for_plots.zip file, see below:</p> <pre>umap_embeddings_Fig2A.Rdata<br>cross_dataset_aggregated_exp_metaMarker_all_fetal_SuppFig1B_Fig2E.Rdata<br>organoid_egad_results_ranked_6_26_24_Fig3D.Rdata<br>fetal_egad_results_ranked_6_26_24_Fig3D.Rdata<br>org_eigenvec_matrices_SuppFig3CD.Rdata</pre> <p><br>The R package developed for this paper is available at https://github.com/JonathanMWerner/preservedCoexp</p>
Table S5. The differential expression of 5946 genes in cancer and normal tissues
<p>The file contains GeneID, GeneType, symbol, logFC, FDR, RCI, OverallSurv, DiseaseFreeSurv information for 5946 genomes. Among them, FDR indicates false discovery rate;RCI indicates the logarithm of the ratio of concentration to nucleus (Log<sub>2</sub>), positive value indicates distribution in cytoplasm, negative value indicates distribution in nucleus; 0 in OverallSurv and DiseaseFreeSurv indicates insignificant, 1 indicates significant.</p>
Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows
<p>Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows</p>
Transposon DNA sequences facilitate the tissue-specific horizontal transfer: te expression supplementary datasets
<p>These datasets contain data on analyses of TE expression stability. Raw RNA seq counts were processed using DESeq2 R package. Low count genes and TEs were removed. Counts across samples were normalized for library sizes and log-transformed using 'regularized log' transformation. Batch normalization was performed on log-transformed data with ComBat function from sva R package. Expression variability (EV) of TEs and genes (probes) was estimated using the previously described method [1, 2].</p> <p>1. Bashkeel, N., Perkins, T.J., Kærn, M. et al. Human gene expression variability and its dependence on methylation and aging. BMC Genomics 20, 941 (2019). https://doi.org/10.1186/s12864-019-6308-7<br> 2. Alemu EY, Carl JW Jr, Corrada Bravo H, Hannenhalli S. Determinants of expression variability. Nucleic Acids Res. 2014;42(6):3503-3514. doi:10.1093/nar/gkt1364</p> <p> </p> <p>PC.zip - the results of TE expression and stability in prostate cancer.</p> <ul> <li>0.PC.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.PC.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.PC.TE.rlogcpm.mad.xls -stability measures according MAD (median absolute deviance) criteria </li> <li>0.PC.TE.rlogcpm.sd.xls - stability measures according SD criteria </li> <li>0.PC_TE_bootstrap.pdf - TE expression stability</li> <li>PC.deseq.logCPM.csv - TE log transformed expression matrix </li> <li>PC.TE_count_table.csv - TE raw count matrix </li> <li>PC_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul> <p> </p> <p>MM.zip - the results of TE expression and stability in multiple myeloma.</p> <ul> <li>0.MM.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.MM.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.MM_TE_bootstrap.pdf - TE expression stability</li> <li>MM.deseq.logCPM.csv - TE raw count matrix </li> <li>MM.rlog.mad.xlsx- stability measures according MAD (median absolute deviance) criteria</li> <li>MM.rlog.sd.xlsx - stability measures according MAD (median absolute deviance) criteria </li> <li>MM.TE_count_table.csv - TE raw count matrix </li> <li>Myeloma_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>
Adipose Tissue Gene Expression and Metabolomics Links to the Gut Microbiome-brain Axis
ClinicalTrials.gov study NCT06869941. IPD Sharing: Not stated. Countries: 1. Publications: 31.
Effect of Hula Hooping as Compared to Walking on Adipose Tissue Distribution, Metabolic Parameters and Adipose Tissue Gene Expression
ClinicalTrials.gov study NCT01913171. IPD Sharing: Not stated. Countries: 1. Publications: 1.
TIFACT Study - Tissue Factor Expression by Adipose Tissue in Extremely Obese Subjects.
ClinicalTrials.gov study NCT00379704. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Incidence of Expression of Tumor Antigens in Cancer Tissue From Patients With Pathologically Demonstrated Bladder Cancer
ClinicalTrials.gov study NCT01706185. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Relationship Between the Differential Expression of FosB Protein in Laryngeal Cancer Tissues and Clinical Prognosis
ClinicalTrials.gov study NCT06836765. IPD Sharing: NO. Countries: 1. Publications: 3.
Evaluation of PD1 / PDL1 Expression on Blood Cells & Tumor Tissue, Their Role as a Prognostic Target in NSCLC Patients
ClinicalTrials.gov study NCT02758314. IPD Sharing: YES. Countries: 1. Publications: 5.
Effect of Exercise Training on Protein Expression in Skeletal Muscle Tissue After Exercise in Peripheral Arterial Disease
ClinicalTrials.gov study NCT01871779. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Clinical Relevance of P16 Expressing CTCs Detection Comparing With HPV Infection in Cancer Tissue in HNSCC Patients.
ClinicalTrials.gov study NCT02791607. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Assessing Her2/Neu Expression in Gastric Cancer With Dual or Multiple Tumor Tissue Paraffin Blocks
ClinicalTrials.gov study NCT02852096. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PSMA Expression and PSMA PET Imaging in Soft Tissue Sarcomas and Urothelial Cell Carcinomas
ClinicalTrials.gov study NCT05522257. IPD Sharing: NO. Countries: 1. Publications: 1.
Tissue-resident Memory T Cells Expression Among the Repigmentation Patterns Induced by NB-UVB Phototherapy in Vitiligo
ClinicalTrials.gov study NCT05506995. IPD Sharing: NO. Countries: 1. Publications: 10.
Expression of Angiogenic Factors in Myocardial Tissue
ClinicalTrials.gov study NCT01414621. IPD Sharing: NO. Countries: 1. Publications: 7.
Tissue-resident Memory T Cells Expression in Melasma
ClinicalTrials.gov study NCT05698342. IPD Sharing: NO. Countries: 1. Publications: 9.
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.