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650 results for “multiomics”
Data for "A unified model-based framework for doublet/multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet/Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
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>
Integrated multiomics (scATACseq and scRNAseq) of 2D10 cells treated with small molecules for 24hrs
GEO Series GSE210146. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
One-pot method for preparing DNA, RNA, and protein for multiomics analysis II
GEO Series GSE241433. Homo sapiens. 12 samples. Type: Other.
Osteogenic Differentiation Potential of Mesenchymal Stem Cells using Single Cell Multiomic Analysis
GEO Series GSE255646. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Integrated single cell multiomic profiling and functional validation reveal distinct cellular routes to human plasma cell differentiation.
GEO Series GSE318326. Homo sapiens. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Single-cell multiome uncovers differences in glycogen metabolism underlying species-specific speed of development [scRNAseq and scATACseq]
GEO Series GSE276058. Homo sapiens; Mus musculus; Macaca fascicularis. 6 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Multiomics Analyses Unveil Immune Landscape in Pediatric and Adult Sepsis [CITE-seq]
GEO Series GSE279451. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing; Other.
Single Cell Multiomics Across Nine Mammals Reveals Cell Type Specific Regulatory Conservation in the Brain.
GEO Series GSE303187. Oryctolagus cuniculus; Homo sapiens; Danio rerio; Felis catus; Equus caballus; Bos taurus; Rattus norvegicus. 196 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Spatial and Single-cell Transcriptomic Characterization of WIF1-induced Normal-tension Glaucoma [Single Cell Multiome ATAC + Gene Expression]
GEO Series GSE293381. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Single-nucleus Multiome Profiling of Infant and Adult Human Spleen
GEO Series GSE311423. Homo sapiens. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomics
GEO Series GSE230109. Mus musculus. 152 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other; Methylation profiling by high throughput sequencing.
Integrated multiomic analysis identifies functional epigenetic signatures regulating nucleotide excision repair
GEO Series GSE161793. Homo sapiens. 36 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Other.
Multiomic Analysis of Adult Diapause in Drosophila melanogaster Identifies Hallmarks of Cellular Quiescence
GEO Series GSE212864. Drosophila melanogaster. 40 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
A multiomics approach reveals RNA dynamics promote cellular sensitivity to DNA hypomethylation
GEO Series GSE222886. Homo sapiens. 75 samples. Type: Expression profiling by high throughput sequencing; Other.
Integrated multiomic analysis identifies functional epigenetic signatures regulating nucleotide excision repair (ATAC-seq)
GEO Series GSE161790. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Multiomics Analyses Unveil Immune Landscape in Pediatric and Adult Sepsis [bulk RNA-seq]
GEO Series GSE279448. Homo sapiens. 207 samples. Type: Expression profiling by high throughput sequencing.
Integrated multiomic analysis identifies functional epigenetic signatures regulating nucleotide excision repair (RRBS-seq)
GEO Series GSE161791. Homo sapiens. 8 samples. Type: Methylation profiling by high throughput sequencing.
TNBC organoid sc-multiome seq data treated with epigenetic drugs
GEO Series GSE288798. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
ScienceDex guides
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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)
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.