Search Results - EGA European Genome-Phenome Archive

ATAC-Seq of inflamed and non-inflamed biopsies

Inflamed and non-inflamed biopsies were digested for 45 minutes at 37ºC with collagenase from Clostridium histolyticum (Sigma). Suspension cells were then stained with an antibody cocktail to sort live CD8-CD4+ T cells. After isolating crude nuclei of two thousand live CD4+ T cells, they were treated with Tagment DNA buffer and Tagment DNA Enzyme (Nextera DNA Library Prep Kit, Illumina), and then the DNA was purified by MinElute PCR Purification Kit (Qiagen). Transposed DNA fragments were amplified using specific adapters followed by purification with MinElute PCR Purification Kit (Qiagen). Fragments from 240-360pb were selected in the PippinHT system (Sage Science). The quality of the library and its DNA concentration were assessed by Bioanalyzer instruments (Agilent Technologies) and ultimately submitted for sequencing using Illumina HiSeq 2500 sequencer, V4 chemistry.

Study EGAS00001007344

National Human Genome Research Institute (NHGRI) GENEVA Genome-Wide Association Study of Venous Thrombosis (GWAS of VTE)

Overview: Our overall long-term goal is to determine risk factors for the complex (multifactorial) disease, venous thromboembolism (VTE), that will allow physicians to stratify individual patient risk and target VTE prophylaxis to those who would benefit most. In this genome-wide association case-control study (1300 cases and 1300 controls) we hope to identify susceptibility variants for VTE. Mutations within genes encoding for important components of the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways are risk factors for VTE. We hypothesize that other genes within these four pathways or within other pathways also are VTE disease-susceptibility genes. Therefore, we performed a genome wide association (GWA) screen and analysis using the Illumina 660W platform to identify SNPs within 1,300 clinic-based, non-cancer VTE cases primarily from Minnesota and the upper Midwest USA, and 1300 clinic-based, unrelated controls frequency-matched on patient age, gender, myocardial infarction/stroke status and state of residence. This is a subset of a slightly larger candidate gene study using 1500 case-control pairs to identify haplotype-tagging SNPs (ht-SNPs) in a large set of candidate genes (n~750) within the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways. Study Populations. Cases. VTE cases were consecutive Mayo Clinic outpatients with objectively-diagnosed deep vein thrombosis (DVT) and/or pulmonary embolism (PE) residing in the upper Midwest and referred by Mayo Clinic physician to the Mayo Clinic Special Coagulation Laboratory for clinical diagnostic testing to evaluate for an acquired or inherited thrombophilia, or to the Mayo Clinic Thrombophilia Center. Any person contacted to be a control but discovered to have had a VTE was evaluated for inclusion as a case. Cases were primarily residents from Minnesota, Wisconsin, Iowa, Michigan, Illinois, North or South Dakota, Nebraska, Kansas, Missouri and Indiana. A DVT or PE was categorized as objectively diagnosed when (a) confirmed by venography or pulmonary angiography, or pathology examination of thrombus removed at surgery, or (b) if at least one non-invasive test (compression duplex ultrasonography, lung scan, computed tomography scan, magnetic resonance imaging) was positive. A VTE was defined as: Proximal leg deep vein thrombosis (DVT), which includes the common iliac, internal iliac, external iliac, common femoral, superficial [now termed "femoral"] femoral, deep femoral [sometimes referred to as "profunda" femoral] and/or popliteal veins. (Note: greater and lesser saphenous veins, or other superficial or perforator veins, were not included as proximal or distal leg DVT). Distal leg DVT (or "isolated calf DVT"), which includes the anterior tibial, posterior tibial and/or peroneal veins. (Note: gastrocnemius, soleal and/or sural [e.g., "deep muscular veins" of the calf] vein thrombosis was not included as distal leg DVT). Arm DVT, which includes the axillary, subclavian and/or innominate (brachiocephalic) veins. (Note: jugular [internal or external], cephalic and brachial vein thrombosis was not included in "arm DVT"). Hepatic, portal, splenic, superior or inferior mesenteric, and/or renal vein thrombosis. (Note: ovarian, testicular, peri-prostatic and/or pelvic vein thrombosis was not included). Cerebral vein thrombosis (includes cerebral or dural sinus or vein, saggital sinus or vein, and/or transverse sinus or vein thrombosis). Inferior vena cava (IVC) thrombosis Superior vena cava (SVC) thrombosis Pulmonary embolism Patients with VTE related to active cancer, antiphospholipid syndrome, inflammatory bowel disease, vasculitis, a rheumatoid or other autoimmune disorder, a vascular anomaly (e.g., Klippel-Trénaunay syndrome, etc.), heparin-induced thrombocytopenia, or a mechanical cause for DVT (e.g., arm DVT or SVC thrombosis related to a central venous catheter or transvenous pacemaker, portal and/or splenic vein thrombosis related to liver cirrhosis, IVC thrombosis related to retroperitoneal fibrosis, etc.), with hemodialysis arteriovenous fistula thrombosis, or with prior liver or bone marrow transplantation were excluded. Controls. A Mayo Clinic outpatient control group was prospectively recruited for this study. Controls were frequency-matched on the age group (18-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+ years), sex, myocardial infarction/stroke status, and state of residence distribution of the cases. We selected clinic-based controls using a controls' database of persons undergoing general medical examinations in the Mayo Clinic Departments of General Internal Medicine or Primary Care Internal Medicine. Additionally persons undergoing evaluation at the Mayo Clinic Sports Medicine Center, and the Department of Family Medicine were screened for inclusion as controls. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to venous thrombosis through large-scale genome-wide association studies of 1,300 clinic-based, VTE cases and 1300 clinic-based, unrelated controls. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

Study phs000289

INCLUDE Data Hub: NDA GUIDs for Down Syndrome Research

The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality of life for people with Down syndrome. The INCLUDE Project Data Coordinating Center (DCC) and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions. The INCLUDE Data Hub includes an innovative researcher data repository, a portal, and a cloud-based platform for data analysis. The portal provides access to de-identified demographics and clinical metadata, as well as multi-omics datasets, including whole genome sequences, transcriptomes, proteomes, and metabolomes. The INCLUDE Data Hub uses a 'registered access' model, whereby users can access the portal and associated data after agreeing to terms of use. From the Data Hub, users can also reach 'controlled access' data (e.g., whole genome sequences) only for those studies for which they have obtained approval by an NIH Data Access Committee through the corresponding dbGaP study. A thoughtful linkage strategy to identify duplicate participants across studies enables researchers to merge multiple datasets and data types to create richer datasets and reduce costly redundancies in large-scale data generation (e.g., -omics) while mitigating potential risks introduced by linkage.Based on recommendations from the NICHD Office of Data Science and Sharing, which completed a comprehensive assessment of existing Privacy Protecting Record Linkage (PPRL) and associated data governance solutions, the INCLUDE DCC and the associated NIH Steering Committee have implemented NIMH Data Archive Global Unique Identifiers (NDA GUIDs) in the INCLUDE Data Hub. The NDA GUID Tool is a government-owned and operated PPRL technology that enables researchers to link data from multiple sources to the same individual without revealing personally identifiable information (PII). With the NDA GUID Tool, the same participant information will return the same GUID whenever or wherever it is entered. The GUID itself is not personally identifiable information or protected health information. Sharing data with NDA GUIDs allows approved researchers to link together data on a single participant, even if the data were collected at different locations or through different studies or are distributed from different data repositories. This dbGaP study enables researchers to access an INCLUDE GUID Mapping File that includes all available NDA GUIDs and their associations to study-specific Participant IDs across studies in the INCLUDE Data Hub. This dbGaP study only distributes NDA GUIDs associated with the INCLUDE Data Hub datasets. To access the associated controlled-access datasets, add the corresponding dbGaP studies to your Data Access Request. A list of these studies is available on the INCLUDE Data Hub Portal: https://portal.includedcc.org/studies.

Study phs003678

ImmunAID

The ImmunAID study is a multi-center research program aimed at improving the diagnosis and understanding of systemic autoinflammatory diseases. The study integrates immunological, and molecular data from patients across several European cohorts. Its objective is to identify biomarkers and biological pathways that characterize disease mechanisms and help differentiate between inflammatory conditions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779295. The project has generated genomic datasets, which are available through the European Genome-phenome Archive (EGA). In addition, it has produced several non-genomic datasets, listed below. All datasets have been fully anonymized in accordance with applicable ethical and regulatory frameworks. These non-genomic data are available upon request. To request access to these datasets, please contact: immunaid2024@gmail.com Your request will be studied by the Data Access Committee, and data will be made available upon approval. Non-genomic datasets available: Flow cytometry (general immune phenotype) — Dataset ID: ICKD2021569, size: 23 GB, 342 patients Somascan proteomics data — Dataset ID: ICKD2021570, size: 59 MB, 444 patients ELISA panel (Ferritin, CRP, HO1, IL-1B, IL-6, IL-8, IL-10, IL-12, IL-18, IFN-γ, TNF-α) — Dataset ID: ICKD2021571, size: 295 KB, 439 patients Mass spectrometry (MS/MS) — Dataset ID: ICKD2021572, size: 1.8 TB, 447 patients ELISA (CRP/ SAA) — Dataset ID: ICKD2021573, size: 321 KB, 443 patients Flow cytometry (inflammasome activity) — Dataset ID: ICKD2021574, size: 899 KB, 307 patients Plasma lipidomics — Dataset ID: ICKD2021575, size: 1.1 MB, 427 patients Urine lipidomics — Dataset ID: ICKD2021576, size: 833 KB, 368 patients ELISA (alarmins) — Dataset ID: ICKD2021577, size: 129 KB, 108 patients ELISA (IL-18 / IL-1) — Dataset ID: ICKD2021578, size: 271 KB, 330 patients Flow cytometry (NK cell alterations) — Dataset ID: ICKD2021579, size: 14 GB, 216 patients Chemokine measurements — Dataset ID: ICKD2021580, size: 489 KB, 404 patients Luminex (multiple analyte measurements) — Dataset ID: ICKD2021581, size: 759 KB, 369 patients

Study EGAS50000001393

Liver biopsy derived induced pluripotent stem cells provide an unlimited supply for the generation of patient-specific hepatocyte-like cells

Background: Hepatocyte-like cells (HLCs) differentiated from induced pluripotent stem cells (iPSCs) have emerged as a promising cell culture model to study metabolism, biotransformation, viral infections and genetic liver diseases. However the derivation of iPSCs is restricted by ethical and procedural constraints. Furthermore, incomplete HLC differentiation remains a major challenge. iPSC may carry-on a tissue of origin dependent expression memory influencing iPSC differentiation into different cell types. If liver derived iPSCs (Li-iPSCs) maintain a liver specific gene expression profile is not known. It is also not known, if Li-iPSCs would allow the generation of more fully differentiated HLCs.Methods: Primary liver cells (PLCs) were expanded from liver needle biopsies and reprogrammed into Li-iPSCs using a non-integrative Sendai virus-based system. Li-iPSCs were differentiated into HLCs using established differentiation protocols. The HLC phenotype was characterized at the protein, functional and transcriptional level. RNA sequencing data were generated from the originating liver biopsies, the Li-iPSCs, fibroblast derived iPSCs, and differentiated HLCs, and used to characterize and compare their transcriptome profiles.Results: PLCs were obtained from small pieces of liver biopsies and could be reprogrammed into Li-iPSCs. Li-iPSCs indeed retain a liver specific transcriptional footprint. Li-iPSCs can be propagated to provide an unlimited supply of cells for differentiation into Li-HLCs. Similar to HLCs derived from fibroblasts, Li-HLCs could not be fully differentiated into hepatocytes. Relative to the originating liver, Li-HLCs showed lower expression of liver specific transcription factors and increased expression of genes involved in the differentiation of other tissues.Conclusions: PLCs and Li-iPSCs obtained from small pieces of human needle liver biopsies constitute a novel unlimited source for the production of HLCs. Despite the preservation of a liver specific gene expression footprint in Li-iPSCs, the generation of fully differentiated hepatocytes cannot be achieved with the current differentiation protocols.

Study EGAS00001002676

Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

Study EGAS00001004861

SC_DDD-G-4

Post-QC (pre-imputation) genotype data for N=6,983 DDD probands included in the neurodevelopmental disorder discovery GWAS (Niemi et al., Nature 2018). Consists of filtered set of samples and variants from EGAD00010001598 and EGAD00010001600. Includes patient HPO phenotype terms and GWAS summary statistics (including imputed variants). Samples were genotyped on the Illumina HumanCoreExome BeadChip and Illumina InfiniumCoreExome Beadchip

Dataset EGAD00010001604

RNA sequencing of pretreatment, on-treatment and posttreatment gastric and gastroesophageal junction tumors treated with neoadjuvant anti-PD-L1 plus chemotherapy

This set contains a total of 78 files cram files with RNA sequencing data from 20 patients included in the PANDA study treated with 1 cycle of monotherapy atezolizumab and 4 cycles atezolizumab plus chemotherapy (docetaxel, oxaliplatin and capecitabine). RNA was isolated from fresh frozen material and sequenced at 4 timepoints baseline, after monotherapy atezolizumab, after combination atezolizumab plus chemotherapy and at resection (due to 2 missing samples, there is a total of 78 samples).

Dataset EGAD50000000241

NGS-based monitoring of the T cell receptor repertoire in living donor kidney transplant patients undergoing combination cell therapy

PBMCs from six kidney transplant recipients receiving as part of the Trex001 study autologous Tregs and donor bone marrow and six control patients not receiving either of the two treatments were collected pre-transplant and at one, three and six month post-transplant. Donor reactive T-cells were identified by mixed lymphocyte reactions (MLR) and lineage specific T-cell receptor (TCR) repertoires of native T-cells and proliferating and non-proliferating T-cells from MLRs were determined by next generation sequencing based profiling of the TCR.

Dataset EGAD50000000302

NHLBI TOPMed: Early-Onset Atrial Fibrillation in the Estonian Biobank

The Estonian Biobank is the population-based biobank of the Estonian Genome Centre of University of Tartu. The biobank is conducted according to the Estonian Gene Research Act and all participants have signed broad informed consent. The cohort size is currently 51,535 people from 18 years of age and up.

Study phs001606

Transcriptomic Profiles of Neoantigen-Reactive T Cells in Human Gastrointestinal Cancers

This study explores the transcriptomic profiles of neoantigen-reactive tumor-infiltrating lymphocytes (TILs) from human bile duct and pancreatic cancer. The submitted data are bulk tumor RNA-Seq, tumor and germline whole-exome sequencing from 10 patients, and single cell RNA-Seq data from TIL of 5 of these patients.

Study phs002765

Genetic Study on Nephropathy in Type-2 Diabetes

A collection of patients with type-2 diabetes was collected at the Joslin Diabetes Center. Patients were categorized to a group of cases with diabetic nephropathy, and a group of controls with normoalbuminuria. From these groups, 173 cases and 177 controls were randomly selected for a genome-wide association study.

Study phs000302

Analysis of Cell-Free DNA to Predict Outcome to Bevacizumab Combination Therapy in Metastatic Colorectal Cancer Patients

This dataset includes FASTQ files of low coverage whole genome sequencing of normal tissue (n = 8), tumor tissue (n = 55) and cell free DNA from plasma samples (n=101) from patients with metastatic colorectal cancer treated with bevacizumab. A total of 164 samples are present from two different cohorts. The first batch of samples (n = 139, sample names sAPD302T until sAPD502_P0) were collected from the AC-Angiopredict Phase 2 trial (NCT01822444), the second batch of samples (n = 25m, sample names sAPD503_P0 until sAPD527_P0) were collected from the UMM cohort.

Dataset EGAD50000000176

Genomic and Transcriptomic sequencing of neuroblastoma cell lines

Genomic and Transcriptomic sequencing of neuroblastoma HSR and ecDNA cell lines. Data represents five 96-well plates that were processed with G&T sequencing, resulting in genomic and transcriptomic data from the same single cells. Dataset contains 95 bam files containing the DNA sequencing data and 95 bam files containing the RNA sequencing data of CHP212 cells, 380 bam files (190 DNA and 190 RNA) for TR14 cells, 188 bam files (94 DNA and 94 RNA) for Kelly cells and 192 bam files (96 DNA and 96 RNA) for IMR5/75 cells.

Dataset EGAD50000000729

Multiple paralogues and recombination mechanisms contribute to the high incidence of 22q11.2 Deletion Syndrome

The 22q11.2 deletion syndrome (22q11.2DS) is the most common microdeletion disorder. Why the incidence of 22q11.2DS is much greater than that of other genomic disorders remains unknown. Short read sequencing cannot resolve the complex segmental duplications (SDs) to provide direct confirmation of the hypothesis that the rearrangements are caused by non-allelic homologous recombination between the low copy repeats on chromosome 22 (LCR22s). To enable haplotype-specific assembly and rearrangement mapping in LCR22 clusters, we used whole genome (ultra-)long read sequencing of 9 duos (patients and parent of origin).

Dataset EGAD50000000855

Single Cell Sequencing of Medulloblastoma Samples

This dataset contains the results of sequencing 9 samples (7 human/PDX, 2 cell lines) of medulloblastoma tumours (or 2 Neural Stem Cell lines). The data was collected to characterize the intratumour heterogeneity of chromothripsis in this tumour type. The 7 tumour samples underwent single cell DNA and RNA sequencing using the 10x CNVkit and 10xRNA protocols, and 1 of the tumour samples as well as the Neural Stem Cell lines underwent StrandSeq sequencing. All experiments were sequenced on Illumina sequencers. The provided files are paired-end fastq files from the sequencing experiments.

Dataset EGAD50000000909

Comprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities

This dataset includes paired tumor-blood whole exome sequencing data for 209 gastric cancer (GC) patients, along with whole transcriptome sequencing data for 125 GC samples. Whole exome sequencing was conducted using Agilent SureSelect Human All Exon V6 kits. For RNA sequencing, total RNA was isolated using the RNeasy Mini Kit, and libraries were prepared with the TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Aligned BAM files for both exome and RNAseq data are included in this dataset.

Dataset EGAD50000000660

Arcagen - NET / NEC G3

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files in .BAM format from 85 patients with extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3).

Dataset EGAD50000000913

Long-term Survival Update and Extended RAS Mutational Analysis of the CAIRO2 Trial: Addition of Cetuximab to CAPOX/Bevacizumab in Metastatic Colorectal Cancer

Here we present updated survival of the CAIRO2 trial and assessed whether the addition of anti-EGFR to anti-VEGF therapy could still be an effective treatment option for patients with extended RAS/BRAF wildtype and left-sided metastatic colorectal cancer (mCRC).

Study EGAS50000000775

Ex vivo modeling of precision immuno-oncology responses in lung cancer

Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics. The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics.

Study EGAS50000000593

Identifying rare genetic variants in 21 highly multiplex autism families

Study EGAS00001006928

PanelSeq IDC samples

PDX samples of breast cancer IDC mouse models created from patient tumor material of the NKI. Genomescan prepared the samples according to the procedure for Hybridization Capture using an Agilent SureSelect custom 0.5-2.9Mb kit. The prepared libraries were sequenced with Illumina sequencing technology. The samples are in fastq format and consist of the following PDX samples: IDC025, IDC026, IDC029, IDC031, IDC032, IDC038, IDC057, IDC062, IDC065, IDC069, IDC072, IDC090B, IDC092, IDC097, IDC099, IDC107, IDC113, IDC117, IDC143, IDC152, IDC159A, IDC159B, IDC180, IDC186, IDC192, IDC197, IDC198, IDC207, IDC209, IDC216, IDC218, IDC222, IDC229, IDC232, IDC274, IDC282, IDC290, IDC299, IDC307, IDC338, IDC344, IDC346, ILC006, ILC012, ILC083 and ILC248.

Dataset EGAD50000001149

Integrated Somatic and Germline Molecular Properties Dictating Biological and Clinical Phenotypes in Cancer

We performed single-nuclei matched RNA- and ATAC-sequencing of frozen biopsies from normal adjacent tissue, primary and metastatic esophageal adenocarcinoma specimens. The data was then further stratified into a tumor microenvironment and malignant compartment and was computationally analyzed to reveal cancer cell subtypes and programs driving malignancy.

Study phs003438

NGS-Based Mutational Analysis of 87 PMBL Patients from the GAINED Cohort (Subset of 382 Sequenced Patients)

Targeted DNA sequencing was performed on DNA extracted from 87 formalin-fixed paraffin-embedded (FFPE) samples, representing a subset of the 382 sequenced patients from the GAINED cohort. A custom “Lymphopanel” was used to identify mutations in 70 genes associated with lymphomagenesis. Library preparation, exome capture, sequencing, and analysis were conducted by IntegraGen SA (Evry, France). Sequencing was performed on an Illumina HiSeq4000 platform, with variant calling and annotation using established bioinformatics tools such as MuTect and VEP.

Dataset EGAD50000001359

Genetically unique biospecimens derived from individuals with or without Type 1 Diabetes

In this dataset, samples correspond to pools of genetically unique biospecimens derived from individuals with or without Type 1 Diabetes. Each sample alias contains all the DNA test numbers for that pool. Each experiment corresponds to scmxGEX (multiome RNA gene expression), scmxATAC (multiome ATAC sequencing), scRNA (single cell RNA sequencing), scATAC (single cell ATAC sequencing) for each pool. Runs link the appropriate fastq files to each experiment. Deconvolution using genetic information is required for single sample-based testing.

Dataset EGAD50000001257

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