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829 results for “Drug resistance”
Elucidating cisplatin resistance mechanisms in squamous cervical cancer: Proteomic insights into intracellular DNA repair pathways while exploring the horizons of omics-driven drug repurposing
<p>The following dataset consists of mass spectrometry-based-DIA global proteomics data of HPV16+ cervical cancer cell lines.</p>
Magnetic Bead Processing Enables Sensitive Ligation-based Detection of HIV Drug Resistance Mutations [Data Release]
<p>Raw data files for the manuscript "Magnetic Bead Processing Enables Sensitive Ligation-based Detection of HIV Drug Resistance Mutations".</p>
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>
A Comparison of Two Tests for Anti-HIV Drug Resistance
ClinicalTrials.gov study NCT00006490. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Study of Cannabidiol for Drug-Resistant Epilepsies
ClinicalTrials.gov study NCT03014440. IPD Sharing: Not stated. Countries: 0. Publications: 0.
HIV cell-to-cell spread slows evolution of drug resistance
GEO Series GSE157511. Homo sapiens. 143 samples. Type: Other.
Identification of Differentially Expressed Circular RNAs between the RNA of Drug Resistant HCT-116 Colorectal Cancer cells and its Extracellular Vesicle- Derivative
GEO Series GSE120985. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by array.
IL-6 promotes drug resistance through formation of polyploid giant cancer cells and stromal fibroblast reprogramming
GEO Series GSE178745. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional characterization of hippocampi from novel preclinical model of drug-resistant epilepsy
GEO Series GSE157797. Mus musculus. 30 samples. Type: Expression profiling by array.
DNA structure-specific endonuclease FEN1 as a novel drug target in tamoxifen resistant breast cancer.
GEO Series GSE95302. Homo sapiens. 33 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Single-cell transcriptome reveals drug-resistance signature and immunosuppressive microenvironment in lung adenocarcinoma harboring EGFR mutation
GEO Series GSE253718. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Mechanisms of drugs-resistance in small cell lung cancer II
GEO Series GSE205522. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ATAC-seq]
GEO Series GSE155916. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Telomerase-mediated Strategy for Overcoming Non-Small Cell Lung Cancer Targeted Therapy and Multi-drug Chemotherapy Resistance
GEO Series GSE109821. Homo sapiens. 54 samples. Type: Expression profiling by high throughput sequencing.
TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes [ChIP-seq]
GEO Series GSE171098. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Gemcitabine Drug resistance
GEO Series GSE3344. Homo sapiens. 3 samples. Type: Expression profiling by array.
Augmented Drug Resistance of Osteosarcoma Cells within Decalcified Bone Matrix Scaffold: the role of Glutamine Metabolism
GEO Series GSE234632. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
EZH2 reads DNA 6mA to regulate survival and drug resistance of human head and neck squamous carcinoma by suppressing RIOX2-CDKN1A-NRF2 signaling axis
GEO Series GSE212452. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Variability within rare cell states enables multiple paths towards drug resistance.
GEO Series GSE161300. Homo sapiens. 53 samples. Type: Expression profiling by high throughput sequencing.
Mechanistic insights into cancer drug resistance through optogenetic PI3K signaling hyperactivation
GEO Series GSE212748. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
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.