7188 results for "({!join from=dataset_studies to=id v=$q1}) OR ("transcriptome" OR "RNA-seq" OR "rna sequencing" OR "rna-sequencing")"

in 166.19 milliseconds.

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008040

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008041

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008042

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008043

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008044

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 1, glucose concentration 1mM

  Dataset EGAD00001008045

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 2, glucose concentration 1mM

  Dataset EGAD00001008046

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008049

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008050

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008051

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008052

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008053

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008087

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008088

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008089

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008090

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008078

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008079

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008080

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008081

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008082

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008083

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008084

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008085

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008086

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008073

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008074

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008075

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008076

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008077

• Multi-Modal Single-Cell, Spatial, and Genomic Analyses of Human Non-Small Cell Lung Cancer Brain Metastases

  We performed single nuclei RNA-sequencing (snRNA-seq) with matched T cell receptor sequencing (TCR-seq), and pool matched low pass whole genome sequencing (WGS) of 12 treatment-naive non-small cell lung cancer (NSCLC) primary tumors (PTs) and 31 treatment-naive NSCLC brain metastases (BMs). In total, we recovered 277,206 cell transcriptomes in 43 samples. Single-nuclei suspension from frozen NSCLC metastases were generated using methods published in Slyper et al. (Nature Medicine, 2020, PMID:32405060) with modifications. The tissue was first cut in 20 µm sections using a cryostat and stored on dry ice until the next step. Then the tissue was put on wet ice and washed once with 5 ml PBS to remove OCT. After this step nuclei were extracted by vigorous pipetting in salt-tris buffer with Tween-20 (TST) followed by a high-volume wash in salt-tris buffer (ST). Nuclei were counted and 10,000-14,000 and were loaded using a Chromium controller (10X genomics) and Chromium Single Cell 5' Reagents (V1.1 and V2). Complementary DNA libraries were generated according to manufacturer's instructions, with the modification of one additional cycle of amplification. Matched single nuclei T cell receptor (snTCR) sequencing libraries were prepared from amplified cDNA libraries using either Chromium Single Cell V(D)J Enrichment Kit for human T cells Reagent Kit, v1.1, or single cell human TCR amplification kit. Final snTCR sequencing libraries were prepared using Single Index Kit T Set A for V1.1 libraries or Library construction kit and Dual Index Kit TT set A for V2 libraries. Sequencing libraries were quantified using Tapestation D5000 reagents and a 2200 TapeStation and pooled for sequencing. Pool matched low pass whole-genome sequencing (lp-WGS) was performed in addition to snRNA-seq to improve the identification of the malignant and non-malignant cell compartments. Nuclei from snRNA-seq sample preparations were collected by centrifugation (500g, 5 min), snap frozen after removing excess ST buffer, and stored at -200C until further processing. If insufficient nuclei (0C for 9 minutes. Adapters were not diluted, and the optional PCR (5 cycles) and cleanup were performed. After library cleanup with AMPure beads (Beckman Coulter, Brea, CA), libraries were quantified with Tapestation D5000 HS tapes (Agilent) and stored at -200C until sequencing.Using multiplexed immunofluorescence in an independent cohort of treatment-naïve pairs of primary tumors and brain metastases from the same patients with NSCLC, we validated genomic and tumor-microenvironmental findings and identified a cancer cell population characterized by neural features that strongly enriched in brain metastases. This comprehensive analysis provides insights into human NSCLC brain metastasis biology and serves as an important resource for additional discovery.

  Study phs003865

• Cryptococcosis in Previously Healthy Adults

  Cryptococcus is a fungus that causes infections most commonly in immunocompromised patients, such as those with AIDS and solid organ transplant recipients and is currently responsible for an estimated 15% of all AIDS-related deaths globally. However, in developed countries, approximately one-third of cases fall outside these groups such that cryptococcal meningoencephalitis (CM) has become the most common cause of non-viral meningitis in the U.S. Within the U.S., approximately 15-20% have no readily identifiable immune defect and cryptococcal infection in these hosts has a mortality rate of 30-50% despite optimal antifungal therapy. NIH Clinical Study Protocol 93-I-0106 (NCT00001352). The objectives of this protocol can be broadly categorized as: 1) Characterize the immunologic and genetic mechanisms predisposing to disease acquisition; 2) Understand the post-infectious inflammatory response and distinguish its consequences from those directly due to fungal growth; and 3) Management of post-infectious neuro-inflammatory syndromes associated with cryptococcal meningitis (CM). This protocol recruits patients who have microbiological evidence of cryptococcal neurologic or non-CNS disease (typically pulmonary or bone). Cerebrospinal fluid (CSF) and blood samples collected during clinical care are used to measure serum and CNS cellular and soluble cytokines as well as to perform in-situ immunohistochemistry. CSF and blood samples are also collected to measure transcriptional (gene expression), proteomic (protein), and lipidomic (lipids) changes that occur throughout disease and treatment. Observational data detailing audiological, ophthalmological and neurocognitive deficits in these patients is also recorded. We have recently described a post-infectious inflammatory syndrome (PIIRS) associated with cryptococcal meningoencephalitis (CM) which can be best described as a neuro-inflammatory state during which CM patients present with altered mental status (Montreal Cognitive Assessment Score As part of the protocol, we treat patients for either microbiological failure or neuroinflammatory sequelae by conventional therapies utilizing FDA-approved pharmaceuticals at FDA-approved doses. To understand and identify the predominant biological pathways responsible for PIIRS neuroinflammation, we examined patient CSF at baseline (at time of PIIRS diagnosis) and post-treatment with different FDA-approved pharmaceuticals at FDA-approved doses compared to CSF from non-PIIRS donors. Upstream pathway analysis of differentially expressed genes in the CSF of PIIRS patients revealed a prominent role for the JAK/STAT pathway in the pathogenesis of PIIRS and support the use of the JAK inhibitor, ruxolinib, as a pathway-instructed therapy to treat PIIRS patients. Data available through dbGaP: single cell RNA sequencing (scRNA-seq) data from CSF cells of a patient with PIIRS at the time of diagnosis and following treatment with steroids (1g solumedrol/day) and NanoString transcriptional data from CSF cells of patients with PIIRS at the time of diagnosis compared to CSF cells from non-PIIRS donors.

  Study phs003871

• Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma

  Homologous recombination DNA repair deficient (HRD) tumors account for about 50% of tubo-ovarian high-grade serous carcinomas (HGSC) and are associated with a high response to platinum-based chemotherapy. However, a subset of patients with HRD tumors have unexpected resistance to chemotherapy and experience short survival. Our study compared BRCA-deficient and proficient HGSC among patients with short (<3 years, the lowest quartile of overall survival (OS)) versus longer-term (>3 year OS) to identify prognostic markers and potential therapeutic opportunities. Primary tumors from a cohort of patients with advanced-stage HGSC (n=154) enriched for those with HRD tumors and short survival (n=42) were analyzed using whole-genome sequencing, bulk RNA-sequencing, and DNA methylation profiling. A subset of HRD tumors (n=143) were characterized for immune cell densities by multiplex immunofluorescence. Clinicopathological features were assessed in 1,389 patients with HGSC, including 282 with pathogenic germline BRCA variants (gBRCApv). Findings were validated in two international HGSC cohorts: the Ovarian Tumor Tissue Analysis consortium (n=5,875) and Multisciplinary Ovarian Cancer Outcome Group (n=895). In gBRCApv-carriers, residual disease following surgical cytoreduction was not associated with shorter OS unlike in non-carriers where it was strongly associated. While most patients with gBRCApv had a more favorable OS compared to non-carriers, a survival advantage was not apparent in those with variants located outside of BRCA1 exon 10/23 and BRCA2 exon 11/27. Patient survival correlated with HRD score, such that those with a score ≥63 had significantly longer OS compared to those with HRD scores of 42-63 and <42. While higher HRD scores were associated with better survival outcomes, most gBRCApv carriers with a short survival had tumor HRD scores that exceeded the threshold typically considered predictive of a good response to carboplatin or PARP inhibitors. BRCA2-deficient HGSC with loss of the tumor suppressor NF1 was associated with exceptionally favorable outcome (median OS 17 years), whereas amplification of both PIK3CA and RAD21, was associated with poor outcome in BRCA2-deficient HGSC (median OS 2.9 years). The density of tumor-infiltrating CD8+ PD-1+ T cells was prognostic for OS in gBRCApv-carriers. In summary, rather than simply the presence or absence of a germline BRCA pathological variant influencing patient survival, the co-occurrence of specific somatic gene alterations, the extent of immune cell infiltration, the level of homologous recombination deficiency, and BRCA-variant location may modify clinical outcomes in patients with BRCA-deficient HGSC. Analysis of exceptional outcome patients, in this case gBRCApv-carriers with an atypically short survival, may identify determinants of outcome applicable to the wider HGSC patient population.

  Study EGAS00001008059

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• Spatially resolved TME alterations in GCPM

  Whole exome sequencing of 66 gastric samples and whole transcriptome sequencing of 191 gastric samples.

  Dataset EGAD50000000715

• Whole exome and transcriptome sequencing of BPDCN

  This data set contains whole exome and transcriptome data from 47 case with BPDCN. For exome data, bam files are provided (mapped against GRCh38), for transcriptome raw fastq-files (paired-end data).

  Dataset EGAD00001008692

• ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  Dataset EGAD00001000356

• _RNAseq___Colorectal_organoids_and_tumoroids

  Transcriptome sequencing of tumour tissue, adjacent normal tissue and derived organoids/tumoroids from colorectal cancer

  Study EGAS00001000985

• Leeds Paired Primary and Recurrent GBM RNAseq

  Whole transcriptome, strand-directional RNAseq data for paired primary dn recurrent samples from patients with glioblastoma

  Dataset EGAD00001005224

• 64 RNAseq BAMs in the HF-GBM-Tumor-Neurosphere-Xenograft study

  Whole transcriptome sequencing generated from patient, neurosphere and xenograft samples

  Dataset EGAD00001002214

• single cell transcriptome data of gluten-specific T cells

  The data contains paired-end fastq files of 1440 single cells transcriptome sequencing data from 4 Celiac disease patients. CD4+ T cells were sorted by HLA-DQ-gluten tetramers carrying four immunodominant gluten epitopes. All single cell libraries were constructed following SmartSeq2 and sequenced on Illumina NextSeq 500.

  Dataset EGAD00001007975

• Dataset for transcriptome and whole genome bisulfite sequencing of glioblastoma(GBM) tumor cells

  This dataset contains transcriptome and whole genome bisulfite sequencing for 40 samples with glioblastoma(GBM). The sequencing was performed on Illumina NextSeq 2000 and Illumina NextSeq 550, . The sequencing was always paired.

  Dataset EGAD00001015614

• Whole exome sequencing (WES), shallow whole genome sequencing (sWGS), ultra-deep targeted sequencing (TS), RNA whole transcriptome sequencing (RNAseq), targeted TCR sequencing in RNA (RNA-TCRseq) from breast cancer samples.

  Whole exome sequencing (WES), shallow whole genome sequencing (sWGS), ultra-deep targeted sequencing (TS), RNA whole transcriptome sequencing (RNAseq) bam files. Targeted TCR sequencing in RNA (RNA-TCRseq) cram files.

  Dataset EGAD00001003933

• Whole-genome and transcriptome sequencing of seven patients with gene therapy-induced T-cell acute lymphoblastic leukemia

  Dataset EGAD00001006016

• Transcriptome of (peripheral blood), from donor Sample B, replicate 2 time-course biological replicates; 10x lane replicate 3

  time-course biological replicates; 10x lane replicate 3

  Dataset EGAD00001010093

• Transcriptome of (peripheral blood), from donor Sample B, replicate 1 time-course biological replicates; 10x lane replicate 3

  time-course biological replicates; 10x lane replicate 3

  Dataset EGAD00001010092

• Transcriptome of (peripheral blood), from donor Sample B, replicate 1 time-course biological replicates; 10x lane replicate 1

  time-course biological replicates; 10x lane replicate 1

  Dataset EGAD00001010088

• Transcriptome of (peripheral blood), from donor Sample B, replicate 2 time-course biological replicates; 10x lane replicate 1

  time-course biological replicates; 10x lane replicate 1

  Dataset EGAD00001010089

• Transcriptome of (peripheral blood), from donor Sample B, replicate 1 time-course biological replicates; 10x lane replicate 2

  time-course biological replicates; 10x lane replicate 2

  Dataset EGAD00001010090

• Transcriptome of (peripheral blood), from donor Sample B, replicate 2 time-course biological replicates; 10x lane replicate 2

  time-course biological replicates; 10x lane replicate 2

  Dataset EGAD00001010091

• Single Cell and Tissue Level Functional Genomics Analysis of Astrocyte-Related Mechanisms in Taupathy

  This data is the first part of a larger single cell and tissue level functional genomics analysis of dementias with tau pathology.  Cellular resolution will clarify the role of glial genes and pathways in neurodegenerative disease. This includes understanding the role of microglia and astrocytes in neurodegenerative disorders characterized by Tau protein pathology. To achieve a refined view of microglial neuroinflammatory signaling in tau-associated neurodegeneration, we performed a functional genomic analysis that integrated time-course gene expression data from mouse models expressing human mutant MAPT (the tau gene) with single nuclear sequencing data obtained from patients with frontotemporal dementia with Tau protein pathology (Pick’s disease). This included microglia-specific gene expression data obtained from single nuclear sequencing of postmortem frozen precentral gyrus samples collected from 16 total individuals, including 8 subjects with clinical diagnosis of behavior variant frontotemporal dementia and neuropathological diagnosis of Pick’s disease (Tau protein pathology) and controls (n=8). Our findings present detailed molecular profiles of disease-associated microglial transitions over the course of tau-associated neurodegeneration. Furthermore, we demonstrate overlap between microglia profiles obtained from precentral gyrus of patients with Pick’s disease with those up-regulated during the neurodegenerative stage in mice expressing human MAPT mutations.

  Study phs002197

• Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes

  Molecular characterization of the individual cell types in human kidney as well as model organisms are critical in defining organ function and understanding translational aspects of biomedical research. Previous studies have uncovered gene expression profiles of several kidney glomerular cell types, however, important cells, including mesangial (MCs) and glomerular parietal epithelial cells (PECs), were missing or incompletely described, and a systematic comparison between mouse and human kidney is lacking. To this end, we used Smart-seq2 to profile 4332 individual glomerulus-associated cells isolated from human living donor renal biopsies and mouse kidney. The analysis revealed genetic programs for all four glomerular cell types (podocytes, glomerular endothelial cells, MCs and PECs) as well as rare glomerulus-associated macula densa cells (MDCs). Importantly, we detected heterogeneity in glomerulus-associated Pdgfrb-expressing cells, including bona fide intraglomerular MCs with the functionally active phagocytic molecular machinery, as well as a unique mural cell type located in the central stalk region of the glomerulus tuft. Furthermore, we observed remarkable species differences in the individual gene expression profiles of defined glomerular cell types that highlight translational challenges in the field and provide a guide to design translational studies.

  Study EGAS00001004943

• Dataset for the manuscript of Scywalker: scalable end-to-end data analysis workflow for nanopore single-cell transcriptome sequencing

  Dataset for the manuscript Scywalker: scalable end-to-end data analysis workflow for nanopore single-cell transcriptome sequencing. Contains fastq files for 4 brain samples obtained from short-read NovaSeq 6000 v1.5 Illumina and long-read Oxford Nanopore PromethION sequencing. Single-cell suspensions were generated using 10x Genomics Chromium Next GEM Single Cell 3'Kit v3.1

  Dataset EGAD50000000768

• Transcriptome profiling of head and neck squamous cell carcinomas with paired patient-derived xenografts

  This dataset includes transcriptome profiling of 88 head and neck primary patient tumor samples and 45 paired patient-derived xenograft samples that successfully engrafted in mice. NGS was performed using the Illumina TruSeq stranded total RNA sample preparation kit on the Illumina NovaSeq X Plus platform at the Princess Margaret Genomics Centre. This dataset consists of paired-end fastq files.

  Dataset EGAD50000000995

• Whole-transcriptome sequencing of tumor samples

  Whole transcriptome sequencing of RNA samples from tumor samples of patients in the Neo-Pembro trial. For each patient, up to three samples were sequenced; one tumor biopsy at baseline, one tumor biopsy after induction chemotherapy, and a tumor sample from resection material at cytoreductive surgery. RNA was obtained from fresh-frozen samples where possible, or formalin-fixed paraffin-embedded when no fresh-frozen sample was available.

  Dataset EGAD50000001151

• Transcriptome_analysis_of_LCM_samples_

  We aim to set up a RNA pipeline for LCM samples to study transcriptome landscape of tumor and normal tissues. RNA was extracted from Lazer capture micro-dissection tissues and cDNA libraries were prepared with SMARTseq2 protocol. Libraries are pooled to be sequenced.

  Study EGAS00001003862

• Single-cell transcriptome of human fetal pancreas and in vitro pancreatic spheroids

  This dataset combines single cell transcriptome data from fetal pancreas at 7-10 wpc, embryonic stem cell-derived pancreas progenitors and spheroids generated from both fetal pancreas and human pluripotent stem cell-derived pancreas progenitors.

  Study EGAS00001005151

• Whole transcriptome and 97 antibodies of one healthy bone marrow

  Whole transcriptome sequencing using BD Rhapsody with 97 antibodies of a healthy young adult bone marrow (Young3/BM3) mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access links is https://figshare.com/s/901bcddb9ee18e226031.

  Dataset EGAD00001008186

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015302

• SCRNA10X_SA_CHIP0080_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_001 1 samples; filetype=fastq

  Dataset EGAD00001006461

• SCRNA10X_SA_CHIP0142_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_001 1 samples; filetype=fastq

  Dataset EGAD00001006464

• SCRNA10X_SA_CHIP0142_003

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_003 1 samples; filetype=fastq

  Dataset EGAD00001006465

• SCRNA10X_SA_CHIP0146_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0146_002 1 samples; filetype=fastq

  Dataset EGAD00001006466

• SCRNA10X_SA_CHIP0149_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_002 1 samples; filetype=fastq

  Dataset EGAD00001006468

• SCRNA10X_SA_CHIP0150_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_002 1 samples; filetype=fastq

  Dataset EGAD00001006470

• SCRNA10X_SA_CHIP0152_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_001 1 samples; filetype=fastq

  Dataset EGAD00001006471

• SCRNA10X_SA_CHIP0163_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_002 1 samples; filetype=fastq

  Dataset EGAD00001006474

• TENX064

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX064 1 samples; filetype=fastq

  Dataset EGAD00001006479

• SCRNA10X_SA_CHIP0080_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_002 1 samples; filetype=fastq

  Dataset EGAD00001006462

• TENX066

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX066 1 samples; filetype=fastq

  Dataset EGAD00001006481

• SCRNA10X_SA_CHIP0149_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_001 1 samples; filetype=fastq

  Dataset EGAD00001006467

• SCRNA10X_SA_CHIP0163_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_001 1 samples; filetype=fastq

  Dataset EGAD00001006473

• SCRNA10X_SA_CHIP0063_000

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0063_000 1 samples; filetype=fastq

  Dataset EGAD00001006478

• SCRNA10X_SA_CHIP0152_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_002 1 samples; filetype=fastq

  Dataset EGAD00001006472

• SCRNA10X_SA_CHIP0172_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_001 1 samples; filetype=fastq

  Dataset EGAD00001006475

• TENX062

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX062 1 samples; filetype=fastq

  Dataset EGAD00001006477

• TENX065

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX065 1 samples; filetype=fastq

  Dataset EGAD00001006480

• SCRNA10X_SA_CHIP0172_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_002 1 samples; filetype=fastq

  Dataset EGAD00001006476

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015288

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015294

• Genome and transcriptome sequence data from a ependymoma tumor patient

  Genome and transcriptome sequence data from a ependymoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015287

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015296

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015300

• SCRNA10X_SA_CHIP0006_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0006_001 1 samples; filetype=fastq

  Dataset EGAD00001006460

• SCRNA10X_SA_CHIP0150_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_001 1 samples; filetype=fastq

  Dataset EGAD00001006469

• Genome and transcriptome sequence data from a pineoblastoma tumor patient

  Genome and transcriptome sequence data from a pineoblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015280

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015293

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015309

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015316

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015332

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015337

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015338

• SCRNA10X_SA_CHIP0141_004

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0141_004 1 samples; filetype=fastq

  Dataset EGAD00001006463

• TENX068

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX068 1 samples; filetype=fastq

  Dataset EGAD00001006482

• TENX069

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX069 1 samples; filetype=fastq

  Dataset EGAD00001006483

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015264

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015265

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015267

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015270

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

  Genome and transcriptome sequence data from a angiosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015272