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Dataset results
895 results for “Immunology”
Unraveling the immunological roles of murine serum amyloid A3 in aortic immune cell subsets during atherosclerosis progression
GEO Series GSE270922. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Monocytes as endogenous immunologic biosensors: identification of inflammatory, adhesion, and mTOR-related signatures in psoriasis
GEO Series GSE161906. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Whole genome analysis of skin from three immunologically diverse mouse models
GEO Series GSE119202. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Analysis of time-resolved immunological phases in the NIF mouse model for liver fibrosis.
GEO Series GSE255261. Mus musculus. 31 samples. Type: Expression profiling by high throughput sequencing.
Polarization of human iPSC-derived macrophages directs their immunological response to secondary pro-inflammatory stimuli
GEO Series GSE221272. Homo sapiens. 8 samples. Type: Expression profiling by array.
Twin study identifies early immunological and metabolic dysregulation of CD8+ T cells in multiple sclerosis
GEO Series GSE276167. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Comparative evaluation of metabolic, electrophilic, and immunologic effects of itaconate and its ester derivatives on macrophage activation
GEO Series GSE145950. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers
GEO Series GSE306117. Homo sapiens. 74 samples. Type: Expression profiling by high throughput sequencing.
Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes
<p><span>The uploaded data contains the following files for the coronary syndrome (CS) dataset:</span></p> <p><span><span>1)<span> </span></span></span><span>Sample Meta Data Information</span></p> <p><span><span>a.<span> </span></span></span><span>Merged_Sample_Data.csv: contains meta-information about the samples (age, gender, clinical data, sc-data library)</span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>‘Subject’: identifier of a subject</span></p> <p><span><span>o<span> </span></span></span><span>‘measurement’: specifies the timepoint of measurement (TP0 – TP4)</span></p> <p><span><span>o<span> </span></span></span><span>‘library’: specifies the single-cell library in which the scRNA-seq data of the sample was prepared</span></p> <p><span><span>o<span> </span></span></span><span>‘sequence’: specifies the sequence of the hashtag for the demultiplexing</span></p> <p><span><span>o<span> </span></span></span><span>‘hashtag’: HTO hashtag used for the demultiplexing of the scRNA-seq data</span></p> <p><span><span>o<span> </span></span></span><span>‘sc_rna_seq_data’: specifies whether scRNA-seq data for this sample is available</span></p> <p><span><span>o<span> </span></span></span><span>‘age’: age of the subject</span></p> <p><span><span>o<span> </span></span></span><span>‘sex’: gender of the subject</span></p> <p><span><span>o<span> </span></span></span><span>‘classification’: clinical classification of the subject, either: </span></p> <p><span><span>§<span> </span></span></span><span>‘acs_subacute’ = ‘acs_with delayed recanalization after vessel occlusion’ </span></p> <p><span><span>§<span> </span></span></span><span><span> </span>‘acs_w_infection’ = ‘acs acquiring hospital infection’</span></p> <p><span><span>§<span> </span></span></span><span>‘acs_w_o_infection’ = ‘sterile acs’</span></p> <p><span><span>§<span> </span></span></span><span>‘ccs’ =’coronary vessel disease’</span></p> <p><span><span>§<span> </span></span></span><span>‘koronarsklerose’ = ‘coronary sclerosis’</span></p> <p><span><span>§<span> </span></span></span><span>‘vollstaendiger_ausschluss’ = ‘healthy coronaries’</span></p> <p><span><span>o<span> </span></span></span><span>‘group’: clinical classification of the subject (parent categories: ‘ccs’, ‘no_ccs’, ‘acs’ of ‘classification’)</span></p> <p><span><span>o<span> </span></span></span><span>‘delta_ef_value_group: classification of the subject based on the ef value</span></p> <p><span><span>o<span> </span></span></span><span>‘delta_ef_value’: delta ef (ejection fraction) value of the subject</span></p> <p><span><span>o<span> </span></span></span><span>‘delta_ef_value_class’: classification of the subject in ‘good’ , ‘intermediate’ or ‘bad’ outcome based on the ef value</span></p> <p><span><span>o<span> </span></span></span><span>‘CK’: measured CK value of the subject</span></p> <p><span><span>o<span> </span></span></span><span>‘CK_MB’: measured CK_MB value of the subject</span></p> <p><span><span>o<span> </span></span></span><span>‘Troponin’: measured Troponin value of the subject</span></p> <p><span><span>o<span> </span></span></span><span>‘CRP’: measured CRP value of the subject</span></p> <p><span> </span></p> <p><span><span>2)<span> </span></span></span><span>Single-Cell Data:</span></p> <p><span><span>a.<span> </span></span></span><span>For each library (XX ; libraries 01-14) the count output of cellranger including barcodes and features:</span></p> <p><span><span>-<span> </span></span></span><span>L00XX_matrix.mtx</span></p> <p><span><span>-<span> </span></span></span><span>L00XX_features.csv</span></p> <p><span><span>-<span> </span></span></span><span>L00XX_barcodes.tsv</span></p> <p><span><span>b.<span> </span></span></span><span>Prepared_sc_Data.h5ad: preprocessed scRNA-seq data after QC used as input for the MOFA model, contains normalized, log transformed and scaled values for highly variable genes and raw counts on all genes (= input for MOFA model). </span></p> <p><span>Annotation of cells (.obs) includes:</span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>‘Subject’: identifier of a subject</span></p> <p><span><span>o<span> </span></span></span><span>‘B2_Scanorama_Singlet_rb_mt_cluster’: cell-type cluster resulting from the clustering based on the Scanorama embedding (as shown in UMAP in manuscript)</span></p> <p><span><span>o<span> </span></span></span><span>‘cluster_cell_type_Scanorama’: cell type clusters including annotations (as shown in UMAP in manuscript)</span></p> <p><span><span>o<span> </span></span></span><span>‘cell_type_Scanorama’: higher level annotation of cell-types</span></p> <p><span><span>o<span> </span></span></span><span>‘library’: sequencing library the cell was included in (L1-L14)</span></p> <p><span><span>o<span> </span></span></span><span>‘in_sample’: dummy column (used in the pseudobulk aggregation)</span></p> <p><span><span>o<span> </span></span></span><span>‘classification’: clinical classification of the sample (see: Sample Meta Data information)</span></p> <p><span><span>3)<span> </span></span></span><span>Other Omic Data</span></p> <p><span><span>a.<span> </span></span></span><span>Prepared_Neutrophil_Data.csv: contains the neutrophil counts that were used as input for the MOFA model and further downstream analysis</span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>Gene columns (‘ENSG…’): specifies the gene that was measured</span></p> <p><span><span>b.<span> </span></span></span><span>Prepared_Cytokine_Data.csv: contains the cytokine measurements that were used as input for the MOFA model and further downstream analysis </span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>‘Cytokine columns’: each column specifies the name of the cytokine that was measured</span></p> <p><span><span>c.<span> </span></span></span><span>Prepared_Proteomic_Data.csv: contains the proteomic measurements that were used as input for the MOFA model and further downstream analysis</span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>‘Proteomic columns’ each column specifies the name of the protein that was measured</span></p> <p><span><span>4)<span> </span></span></span><span>Cell-Type Annotations</span></p> <p><span><span>a.<span> </span></span></span><span>Cell_Type_Annotation.csv: includes Scanorama based manual annotation and Azimuth based automatic annotation of cells</span></p> <p><a name="_Hlk161153910"></a><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>‘cell_library’: barcode and library of the cell</span></p> <p><span><span>o<span> </span></span></span><span>‘B2_Scanorama_Singlet_rb_mt_cluster’:: cell-type cluster resulting from the clustering based on the Scanorama embedding (as shown in UMAP in manuscript)</span></p> <p><span><span>o<span> </span></span></span><span>‘cluster_cell_type_Scanorama’: cell type clusters including annotations (as shown in UMAP in manuscript)</span></p> <p><span><span>o<span> </span></span></span><span>‘cell_type_Scanorama’: higher level annotation of cell-types</span></p> <p><span><span>o<span> </span></span></span><span>‘library’: sequencing library the cell was included in (L1-L14)</span></p> <p><span><span>o<span> </span></span></span><span>‘predicted.celltype.l2’ : azimuth based cell-type prediction</span></p> <p><span><span>o<span> </span></span></span><span>‘predicted.celltype.l2.score’: score of the the azimuth cell-type prediction</span></p> <p><span><span>b.<span> </span></span></span><span>‘Cell_Type_Annotation_Levels.csv’: includes a mapping of different levels of cell-types</span></p> <p><span><span>5)<span> </span></span></span><span>FACS data:</span></p> <p><span><span>a.<span> </span></span></span><span><span> </span>Prepared_FACS_data.csv: includes cell type percentages of the FACS assay</span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>‘TP’: specifies the timepoint of measurement (TP0 – TP4)</span></p> <p><span><span>o<span> </span></span></span><span>‘cell_type_facs’: FACS cell-type</span></p> <p><span><span>o<span> </span></span></span><span>‘percentage’: percentage of cells for the cell-type of the sample given as character string</span></p> <p><span><span>o<span> </span></span></span><span>‘percentage_numeric’: percentage of cells for the cell-type of the sample given as numeric value</span></p> <p><span><span>6)<span> </span></span></span><span>Grace-Score:</span></p> <p><span><span>a.<span> </span></span></span><span>Grace_Score.csv: contains the computed GRACE scores for each sample (used for evaluation of the prediction of the MOFA factors)</span></p> <p><span><span>o<span> </span></span></span><span>‘sample_id’: identifier of sample (concatenation of subject-id and timepoint)</span></p> <p><span><span>o<span> </span></span></span><span>Grace_Score: calculated grace-score for the sample (for more details refer to the manuscript)</span></p> <p><span><span>7)<span> </span></span></span><span>Pathway Selection:</span></p> <p><span><span>a.<span> </span></span></span><span>REACTOME_Immune_System_Pathways.csv: contains all the immune System pathways that were extracted from REACTOME</span></p> <p><span><span>b.<span> </span></span></span><span>KEGG_pathways_categorized.csv: contains all the KEGG pathways with their categories </span></p> <p><span><span>8)<span> </span></span></span><span>Plot Configuration Files: contain specifications for the violin plots of the manuscript loaded within the scripts to define which genes will be ploted</span></p> <p><span><span>a.<span> </span></span></span><span>Plot_Config_Violin.csv: for violin plots included in main figures</span></p> <p><span><span>b.<span> </span></span></span><span>Plot_Config_Violin_Supp.csv: for violin plots included in supplementary figures</span></p>
A systems immunology approach reveals distinct roles of genetic and non-genetic factors in shaping variation of immune responses in cattle
<p>The VCF file contains 11486822 SNPs from 246 Belgian blue bulls utilized in a genome-wide association study focusing on immunophenotypes.</p>
Epidemiology and Immunology of Hemophilia A Inhibitors
ClinicalTrials.gov study NCT00005518. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Immunological characteristics of non-tuberculous mycobacterial pulmonary disease
GEO Series GSE290289. Homo sapiens. 24 samples. Type: Expression profiling by array.
Acute Immunological Phenotypes in Individuals with Traumatic Spinal Cord Injury.
GEO Series GSE293559. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
Iodide effect on mRNA expression levels of genes related to immunological function in cultured human thyroid follicles
GEO Series GSE16957. Homo sapiens. 2 samples. Type: Expression profiling by array.
CART-19 Responses in Human CD19 Transgenic Mice [immunology platform]
GEO Series GSE102617. Mus musculus domesticus. 48 samples. Type: Expression profiling by array.
Integration of tumor-specific dendritic cell and antibody therapy within tumor drives innate and adaptive anti-cancer immunity [Immunology_NanoString]
GEO Series GSE302764. Mus musculus. 18 samples. Type: Expression profiling by array.
Aryl hydrocarbon receptor dynamically regulates immunological and secretory gene pathways in decidual endometrial stromal cells
GEO Series GSE114552. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Immunological fingerprint of 4CMenB recombinant antigens via protein microarray
GEO Series GSE152785. Homo sapiens; Neisseria meningitidis. 219 samples. Type: Protein profiling by protein array.
Tumor suppressors in Sox2-mediated lung cancers promote distinct cell-intrinsic and immunologic remodeling
GEO Series GSE295887. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
Transplant tissue specfic exosome platform for noninvasive monitoring of immunologic rejection [miRNA]
GEO Series GSE88845. Homo sapiens; synthetic construct. 3 samples. Type: Non-coding RNA profiling by array.
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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)
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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.