12004 results for "cancer rna-seq"

in 20.07 milliseconds.

• Bulk RNA-seq of Human Epiglottis and Subglottis

  This study contains bulk and single nucleus RNA-seq of human epiglottis and subglottis tissue.

  Dataset EGAD50000001278

• Glioblastoma stem cell lines RNA-seq

  RNA-seq data for three Glioblastoma stem cell (GSC) lines exposed to PRMT5 inhibitor and control samples.

  Dataset EGAD00001006095

• 7 samples RNA-seq raw data

  Dataset comprising raw paired RNA-seq data in fastq.gz format for 7 samples of rosette forming brain tumors

  Dataset EGAD00001009265

• RNA-seq of UTUC samples

  This dataset includes transcription profiling by RNA-seq of 3 upper urinary tract urothelial tumours (UTUC).

  Dataset EGAD00001008138

• Vcf RNA-Seq data

  Variants derived from mapped whole transcriptome RNA-Seq data from 476 human samples of early stage urothelial carcinoma.

  Dataset EGAD00001001941

• Un-mapped RNA-Seq data

  Un-mapped whole transcriptome RNA-Seq data from 476 human samples of early stage urothelial carcinoma.

  Dataset EGAD00001001940

• RNA-seq data from sorted CML and normal samples

  RNA-seq data from sorted populations from 10 CML samples and 4 normal bone marrow samples.

  Dataset EGAD00001004079

• snRNAseq of ATCWGS42

  We performed single nucleus RNA-sequencing (snRNA-seq) on two regions from ATCWGS42 (ATCWGS42_E and ATCWGS42_R).

  Study EGAS50000001491

• Human data for chromatin accessibility (ATAC-Seq and scATAC-Seq) and transcriptome (RNA-Seq and scRNA-Seq) in eight B-cell precursors, from HSC to Naive B cells

  We generated paired transcriptomic (RNA-Seq) and chromatin accessibility (ATAC-Seq) profiles from 8 immune cell populations representing the early lymphoid hematopoietic differentiation stages along the B cell lineage: HSC, CLP, pro-B, pre-B, Immature B, Transitional B, Naive B CD5-, Naive B CD5+. Cell populations were isolated from healthy adult human bone marrow precursors and peripheral blood (n=13) following a predefined sorting strategy. For each donor, a subset of different cell type populations was obtained. Additionally, to explore the continuous dynamic of RNA and ATAC, we generated a single-cell sample of human CD34+ B cell precursors.

  Study EGAS00001007296

• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776