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373
datasets available to search
ShareScore release 0.9.0
Dataset results
373 results for “Chronic myeloid leukemia”
Targeting Integrated Stress Response by ISRIB combined with Imatinib attenuates STAT5 signaling and eradicates therapy-resistant Chronic Myeloid Leukemia cells
GEO Series GSE171853. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiling of chronic myeloid leukemia with variant t(9;22) reveals a different signature from cases with classic translocation.
GEO Series GSE41375. Homo sapiens. 20 samples. Type: Expression profiling by array.
Oxidation Resistance 1 (Oxr1) Dysregulation Promotes TKI Resistance and Blast Crisis in Chronic Myeloid Leukemia [dataset 3]
GEO Series GSE281955. Mus musculus. 20 samples. Type: Other.
Selective translational control by PABPC1 phase separation regulates blast crisis and target therapy resistance of chronic myeloid leukemia
GEO Series GSE198142. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
A Unique Rearrangement of PDGFRα and ETV6 in a new t(4;12;6) in a Patient with Acute Myeloid Leukemia (M4) Progressed from Chronic Myelomonocytic Leukemia.
GEO Series GSE80263. Homo sapiens. 1 samples. Type: Genome variation profiling by array.
Sequencing of chronic myeloid leukemia (CML) cell lines upon upregulation or downregulation of G0/G1 switch gene (G0S2)
GEO Series GSE171945. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Phosphoproteomics reveals a crucial role of FLT3 in BCR::ABL1 independent resistant chronic myeloid leukemia
GEO Series GSE280476. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Selective translational control by PABPC1 phase separation regulates blast crisis and target therapy resistance of chronic myeloid leukemia [RIP-seq]
GEO Series GSE280877. Homo sapiens. 12 samples. Type: Other.
Immune Escape of Chronic Myeloid Leukemia in Blast Crisis: The Cross-over of T cell microRNAs, Metabolism and Exhaustion
GEO Series GSE261798. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
Intracellular angiopoietin-1 promotes TKI-resistance via activation of JAK/STAT5 pathway in chronic myeloid leukemia [RNA-seq_14]
GEO Series GSE208312. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Oxidation Resistance 1 (Oxr1) Dysregulation Promotes TKI Resistance and Blast Crisis in Chronic Myeloid Leukemia.
GEO Series GSE281956. Homo sapiens; Mus musculus. 59 samples. Type: Genome variation profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other.
Oxidation Resistance 1 (Oxr1) Dysregulation Promotes TKI Resistance and Blast Crisis in Chronic Myeloid Leukemia [dataset 2]
GEO Series GSE281954. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Selective translational control by PABPC1 phase separation regulates blast crisis and target therapy resistance of chronic myeloid leukemia [RNA-seq]
GEO Series GSE280879. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome of chronic myeloid leukemia blood in early disease based on RNA-seq method
GEO Series GSE36522. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Ybx1 is required for Survival of Leukemia Stem Cells and the Development of Chronic Myeloid Leukemia
GEO Series GSE199304. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Oxidation Resistance 1 (Oxr1) Dysregulation Promotes TKI Resistance and Blast Crisis in Chronic Myeloid Leukemia [dataset 1]
GEO Series GSE281856. Mus musculus. 22 samples. Type: Genome variation profiling by high throughput sequencing.
Single-cell Atlas Reveals Diagnostic Features Predicting Progressive Drug Resistance in Chronic Myeloid Leukemia
<p>This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation.</p> <p>A summary of the content is provided in the following.</p> <p><strong>R scripts</strong></p> <p>Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R<br> Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R<br> Script to run inferCNV analysis: inferCNV_Zenodo.R Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R</p> <p>Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R</p> <p><strong>RDS files</strong></p> <ul> </ul> <p><em>General scRNA-seq Seurat objects:</em></p> <ul> <li>scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS</li> <li>scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds</li> <li>Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS </li> <li>UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS</li> </ul> <p><em>SCENIC files:</em></p> <ul> <li>Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds</li> <li>AUC values computed for regulons: 3.4_regulonAUC.Rds</li> <li>MetaData used in SCENIC cellInfo.Rds</li> <li>Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS</li> <li>Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS</li> <li>Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS</li> <li>Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS</li> </ul> <p><em>BCR-ABL1 inference:</em></p> <ul> <li>HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS</li> <li>UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS</li> <li>HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS</li> </ul> <p><em>NK sub-clustering and filtering:</em></p> <ul> <li>NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS</li> <li>Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR</li> <li>NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS</li> <li>NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS</li> </ul> <p><em>txt and csv files:</em></p> <ul> <li>Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt</li> <li>Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt</li> <li>Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt</li> <li>GSEA results: <ul> <li>HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt</li> <li>NK: NK_For_Plotting.txt</li> </ul> </li> <li>TFRC and HLA expression: TFRC_and_HLA_Values.txt</li> <li>NATMI result files: <ul> <li>UP-regulated_mean.csv</li> <li>DOWN-regulated_mean.csv</li> </ul> </li> <li>Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt</li> <li>Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv</li> </ul> <p>Compressed folders:</p> <ul> <li>All CyTOF raw data files: CyTOF_Data_raw.zip</li> <li>Results of the patient-based classifier: PatientwiseClassifier.zip</li> <li>Results of the single-cell based classifier: SingleCellClassifierResults.zip</li> </ul> <p> </p> <p>For general new data analysis approaches, we recommend the readers to use the Seruat object stored in <a href="https://zenodo.org/api/files/fef7a797-4b53-4473-ba55-17c36557e318/DUKE_final_for_Shiny_App.rds?versionId=5986649e-349d-4ed3-ac3e-7673ab988bf2">DUKE_final_for_Shiny_App.rds</a> or to use the shiny app(<a href="http://scdbm.ddnetbio.com/">http://scdbm.ddnetbio.com/</a>) and perform further analysis from there.</p> <p>RAW data is available at EGA upon request using Study ID: EGAS00001005509</p> <p><strong>Revision</strong></p> <p>The for_CML_manuscript_revision.tar.gz folder contains scripts and data for the paper revision including 1) Detection of the BCR-ABL fusion with long read sequencing; 2) Identification of BCR-ABL junction reads with scRNAseq; 3) Detection of expressed mutations using scRNAseq.</p>
Donor Lymphocytes to Prevent Graft-Versus-Host Disease in Patients With Chronic Myeloid Leukemia
ClinicalTrials.gov study NCT00004878. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Exosomal miRNA expression profiling in patients with imatinib-resistant chronic myeloid leukemia
GEO Series GSE290959. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Selective translational control by PABPC1 phase separation regulates blast crisis and target therapy resistance of chronic myeloid leukemia [Polysome-seq]
GEO Series GSE280878. Homo sapiens. 8 samples. Type: Other.
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.