12302 results for "cancer rna-seq"

in 16.33 milliseconds.

• RNA-seq data for genomic determinants of response and resistance to inotuzumab in B-ALL

  RNA-seq data for 101 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000095

• WES and RNA-Seq of HCC biopsies and HCC derived Organoids

  We aimed to identify somatic mutations and transcriptional differences that could explain the resistance to Doxorubicin. This dataset includes RNA-Seq of HCC biopsies and Organoids and WES of Organoids.

  Dataset EGAD50000000060

• RNA-seq dataset for gamma delta (γδ) T-ALL patients

  RNA-seq data for 85 patients with acute lymphoblastic leukemia expressing the gamma delta T cell receptor (γδ T-ALL)

  Dataset EGAD50000000027

• Genomic characterization of Malignant Pleural Mesothelioma.

  Genomic characterization of Malignant Pleural Mesothelioma using RNA-seq, Exome and Illumina 2.5 M array.

  Study EGAS00001001563

• BLUEPRINT release January 2015, RNA-Seq for monocyte

  RNA-Seq data for 8 monocyte sample(s). 8 run(s), 8 experiment(s), 8 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001191

• RNA-seq

  RNA-seq of primary and metastatic sites from highly clinically annotated HGSC samples. Samples were obtained pre-treatment based on a laparoscopic triage algorithm from patients who underwent R0 tumor debulking, or received neoadjuvant chemotherapy (NACT) with excellent (ER) or poor response(PR).

  Dataset EGAD00001005238

• Human tumour and matched-normal single-cell RNAseq

  We profiled 16 high-grade gliomas patient tumour samples by single-cell and single-nuclei RNA-seq and 3 normal-matched single-cell RNA-seq using 10X Chromium 3'. The fastq files are provided.

  Dataset EGAD00001006097

• IBS-M RNA-seq raw data

  The data set consists of fastq raw files from RNA-seq of seven mucosal biopsies of the colon from seven patients, among them three patients with irritable bowel syndrome with mixed type symptoms. Paired end sequencing on Illumina NovaSeq 6000 was used.

  Dataset EGAD00001006646

• Pheochromocytoma paraganglioma single nuclei RNA-seq

  Single nuclei RNA sequencing (snRNA-seq) on tissue samples from 11 patients (SDHB and RET). Files are fastq files of 10x-5'scRNAseq libraries.

  Dataset EGAD00001008739

• RNA-Seq data on multiple myeloma CD138positive bone marrow plasma cells

  RNA-Seq data on multiple myeloma CD138positive bone marrow plasma cells, 11 samples, sequenced on HiSeq 4000 and HiSeq X-Ten, using mostly the TruSeq stranded mRNA Kit.

  Dataset EGAD00001008619

• RNA-seq for identification of neotranscripts

  This dataset contains RNA-seq data (Fastq files of paired-end data) of 18 patient tumors used for identification of neotranscripts in 18 different types of fusion-driven sarcomas and other cancers as described in Vibert et al., Mol Cell 2022 (PMID: 35550257)

  Dataset EGAD00001008832

• RNA-seq of HCC patients undergoing sorafenib treatment.

  RNA-seq dataset (BAM files) of 28 HCCs and 19 non-tumor livers derived from 8 patients undergoing sorafenib treatment.

  Dataset EGAD00001008204

• Pediatric high grade glioma RNA-Seq

  We performed RNA-Seq in DIPG and hemispheric HGG.

  Dataset EGAD00001008278

• RNA-seq

  RNA sequencing

  Dataset EGAD00001005803

• Processed RNA-seq data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the TPM and raw counts tables.

  Dataset EGAD00001006199

• ScRNA-seq of 6 human FL

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Dataset EGAD00001007691

• Processed bam files for the whole exome sequencing of primary lung adenocarcinoma samples

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes. The extensive single cell profiles depicted a complex cellular atlas and dynamics during lung adenocarcinoma progression which includes cancer, stromal, and immune cells in the surrounding tumor microenvironments.

  Dataset EGAD00001010320

• RNA-seq TPMs quantification for baseline tumor samples originating from IMpower150, and relevant clinical metadata

  This dataset includes full tumor transcriptomes from 558 advanced NSCLC tumors. These data originate from pre-treatment samples from a clinical trial for first-line non-small cell lung cancer (IMpower150). The patients in this trial were treated with the PD-L1 inhibitor atezolizumab +bevacizumab + chemotherapy or bevacizumab + chemotherapy or atezolizumab + chemotherapy.

  Dataset EGAD50000001814

• Contribution of specific cell types to the development of Barrett’s esophagus and carcinoma via germline genetic risk

  This study was designed to elucidate how individual cell types in Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC) contribute to disease development via germline genetic risk. We performed single cell RNA-sequencing (scRNA-seq) of BE, EAC and paired normal esophagus and gastric fundus tissue samples. After analyzing individual cell types, we integrated the scRNA-seq data and data of a genome-wide association study (GWAS) on BE/EAC to determine cell type-specific genetic risk. Raw data from the RNA-sequencing is provided here.

  Study EGAS50000000530

• Single-cell RNA-sequencing and cellular indexing of transcriptomes and epitopes of peripheral blood mononuclear cells and peritoneal fluid from patients with achalasia

  The data published here contains cellular indexing of transcriptomes and epitopes (CITE-seq) and single-cell RNA- sequencing (scRNAseq) data on patients with achalasia. Paired PBMC and PF were subjected to 3' scRNAseq and CITE-seq, respectively. All single-cell data was generated using the 10X Genomics (Chromium Single Cell 3’ Reagent) and sequenced in a paired-ended fashion on the NovaSeq6000.

  Study EGAS50000000174

• Brain transcriptome of hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D)

  HCHWA-D is an early onset hereditary form of Cerebral Amyloid Angiopathy (CAA) caused by a point mutation resulting in an amino acid change (NP_000475.1:p.Glu693Gln) in the Amyloid Precursor Protein (APP). Post-mortem brain tissue (9 patients and 9 age-related controls; frontal and occipital cortex) was used for next generation sequencing of RNA (RNA-Seq with ribosomal RNA depletion).

  Study EGAS00001002730

• Bulk and RIP-seq transcriptome datasets from skin fibroblasts in PTBP1- and PTBP2-related disorders

  PTBP1- and PTBP2-related disorders are rare human conditions that cause variable neurodevelopmental phenotypes, including intellectual disability and behavioral problems such as autism spectrum disorder, anxiety, aggressiveness, and attention disorders, with or without hyperactivity. PTBP1-related disorder is also associated with skeletal dysplasia. In this study, we generated bulk RNA sequencing and RNA immunoprecipitation sequencing (RIP-seq) data from skin fibroblasts obtained from patients and healthy controls.

  Study EGAS50000001210

• Human transcriptomic (RNA-Seq) and epigenomic (ATAC-Seq) data of early B-cell lineage (bulk and single-cell) DAC

  Dac EGAC00001003265

• RNA alignment

  RNAseq aligned to hg38.p14 using nfcore/RNA-seq

  Dataset EGAD50000000428

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452