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11,105 results for “single cells”
Supplementary Material for the paper entitled "Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data"
<p>This repo contain supplementary tables from the manuscript entitled: "Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data". Clustering is a common way to identify cell types in single-cell RNA-sequencing (scRNA-seq) data. Unfortunately, current methods (i) require users to make human-in-the-loop decisions, which adds significant runtime to bioinformatic analyses, and (ii) reuse the same data twice when testing for differentially expressed genes, which can lead to an increased number of false discoveries. In this work, we overcome these limitations with NCLUSION: a Bayesian nonparametric method that simultaneously clusters cells and selects marker genes. NCLUSION operates without user-defined heuristics to set model parameters and leverages variational expectation-maximization (EM) for posterior inference which allows it to scale well up to 1 million cells. By analyzing publicly available datasets, we illustrate that NCLUSION matches the state-of-the-art clustering performance of competing approaches, achieves improved computational efficiency, and directly enables identification of biologically relevant gene sets driving cluster definitions.</p>
Datasets associated with the manuscript "Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques"
<p>meta_data_TCR-pMHC_from_STCRDab.tsv, TCR-pMHC structures used for contacts analysis.</p><p>tcr_gliph_input_sars2.tsv, input files (TCR sequences and related information) used for clustering TCRs targeting SARS-CoV-2 epitopes and epitope-unknown TCRs.</p><p>tcr_gliph_input_non-sars2.tsv, input files used for clustering TCRs targeting non-SARS-CoV-2 epitopes and epitope-unknown TCRs.</p><p>tcr_gliph_output*, output files from the GLIPH software, including the recognized TCR clusters by GLIPH (convergence-group.txt), the linkage information of TCR clusters (clone-network.txt), and the recognized motif in TCR clusters (kmer.txt).</p><p>md_trajs.tar, structures and MD simulation trajectories of TCR-614-pMHC and TCR-204-pMHC complexes.</p>
A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution
<p>The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (<a href="https://urldefense.com/v3/__http:/harvard.heavy.ai:6273/__;!!AIv8Mrc!-cGoARlBC8C7UnXDMr7w17gn4sMqIB-ToOkvqcj__zFp6n1lf2JlwGy84WzhhDRhY_jiL2y3k7ficQnBaHYX8TPF473zKP64$">http://harvard.heavy.ai:6273/</a>) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data. </p>
Single-cell RNA-seq dataset and code for human colorectal cancer liver metastasis study
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Single cell RNA-seq transcriptomic profile of circulating immune and progenitor cells in a mouse model of neonatal hypoxic/ischemic (HI) brain damage.
<p>Hematopoietic cells play a pivotal role in regulating the inflammatory and reparative immune responses triggered after ischemic tissue damage. The response initiated within the injured tissue leads to compositional and transcriptional changes in circulating hematopoietic and progenitor cells, which have been utilized as biomarkers. While the importance of different immune and progenitor cell subtypes in the development of ischemic damage has been extensively researched in adult tissue injuries, there has been limited investigation in neonates. This is a critical developmental stage where ischemic damage can result in severe and irreversible health consequences if not promptly treated. To determine how ischemic damage could affect circulating cells in neonates, we have induced hypoxic-ischemic (HI) brain damage in seven-day-old mice, characterized by focal white and gray-matter injury (Rice-Vannucci model). Brain damage and circulating cells were analyzed at 48h post-HI, the intermediate reparative/inflammatory response phase post-injury. We applied scRNAseq to dissect the transcriptional and cellular composition changes in the peripheral blood of HI-treated and SHAM control neonates. This study provides the first scRNAseq dataset for immune and progenitor circulating cells in newborns with cerebral HI damage. It may help to identify biomarkers and selective therapeutic approaches aimed at modulating inflammatory and reparative pathways. </p><p>CD1 postnatal day 7 (P7) mice were subjected to hypoxic/ischemic (HI) brain injury by permanent ligation of the left common carotid artery followed, after 2h recover, by relocation to a hypoxia chamber for 90 minutes. Sham control mice (SHAM) underwent a skin incision and wound closure followed by hypoxia exposure. Circulating blood cells were collected from SHAM and HI mice at P9. After red blood cells (RBC) lysis, 7AAD-Ter119- cells were FACS sorted and analysed using sc RNAseq. Other samples were FACS sorted for CD45+CD11+ and CD45-CD31+ cells and mixed.</p>
Supplementary information to "ScRNA-IMM: Single-cell RNA-Seq Imputation method using Mean/Median Imputation"
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Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
<p><strong>Datasets for PILOT</strong></p> <p>Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods for the analysis of single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be compared with other samples. We propose here patient Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories. We evaluate PILOT and competing approaches in disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, and genes from large and complex single-cell and pathomics data.</p>
The impacts of active and self-supervised learning on efficient annotation of single-cell expression data - source data
<p>Source data used to create all figures in the manuscript.</p>
Integrative single-cell multi-modal analyses reveal detailed spatial cellular organization directing human heart morphogenesis
<div> <p><span>The heart, which is the first organ to develop, is highly dependent on its form to function. However, how diverse cardiac cell types spatially coordinate to create complex morphological structures critical for heart function remains to be elucidated. Here, we show that integration of single cell RNA-sequencing with high-resolution multiplexed error-robust fluorescent <em>in situ </em>hybridization (MERFISH) not only resolves the identity of cardiac cell types developing the human heart but also provides a spatial mapping of individual cells that enables illumination of their organization into cellular communities forming distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, supporting their specialization according to cellular ecosystem and anatomic region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities revealed signaling pathways orchestrating the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. <em>In vivo </em>conditional genetic mouse models and in vitro human pluripotent stem cell studies confirmed an intricate multicellular PLXN-SEMA crosstalk among specific ventricular cardiomyocyte, fibroblast and endothelial cell subpopulations that directs the compaction of the ventricular wall layers. Thus, these detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodeling the human heart offer new insights into structural heart diseases as well as engineering complex multi-cellular tissues for human heart repair.</span></p> </div>
Integration of Single-Cell Analysis and Mendelian Randomization Reveals NET1 as a Potential Key Player in Lung Cancer Pathogenesis
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An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and function types
<p>The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic<br> regulation through neuroendocrine signalling, outflow of the autonomic nervous system (ANS), and projections<br> to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely<br> to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been<br> adequately classified functionally, its molecular classification is currently still insufficient.</p> <p>To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN<br> single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we<br> functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data<br> and existing single-cell data of a PVN-projection target region.</p>
Single Cell Preterm Dataset
<div> <p>The dataset is a CyTOF preterm women dataset, which collected 42 thousand cells across 36 dimensions from 42 samples\cite{peterson2021single}. The patients ranged from 17 preterm women and 25 term women, which is defined as the predicted label. The covariate of the preterm dataset includes gestational age, history of miscarriage, and the record of preeclampsia. </p> <p>Here is the file structure that fits in CytoCo-set input format (symbol "X(____)X" defined as file name describe):</p> <p>data folder</p> <ul> <li>csv_info.csv</li> <li>filenames_X(trials_number)X.json</li> <li>preterm_csv <ul> <li>all <ul> <li>X(sample_file_name)X.csv</li> <li>test_labels_X(trials_number)X.csv</li> <li>train_labels_X(trials_number)X.csv</li> </ul> </li> <li>marker.csv</li> </ul> </li> <li>tripletlists_X(covariate)X_X(trials_number)X <ul> <li>X(covariate)X_tripletlist_subpick_test_rffX(medianpooling_or_maxpooling)X_sameX(same_threhold_percange)X_diffX(diff_threhold_percange)X.txt</li> <li>X(covariate)X_tripletlist_subpick_trainval_rffX(medianpooling_or_maxpooling)X_sameX(same_threhold_percange)X_diffX(diff_threhold_percange)X.txt</li> </ul> </li> </ul> <p> </p> <p>The dataset uploaded all the sample's raw features data (csv file in "all" file) and some file examples. csv_info.csv records all sample's true labels and covariates.</p> <p> </p> </div>
UNRAVELLING CELL TYPE SPECIFIC RESPONSE TO PARKINSON'S DISEASE AT SINGLE CELL RESOLUTION
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Healthy woodchuck genome with viral sequences appended used for single-cell RNA-seq analysis
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Single-cell multi-omics analysis identifies context specific gene regulatory gates and mechanisms
<p>There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. <br>In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g., AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. <br>We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omic data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.</p>
Supplementary data to BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell RNA-seq data imputation
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Unified Cross-modality Integration and Inference of Single-Cell Multiomics Data with Deep Contrastive Learning
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Single cell gene expression data and gene regulatory network
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Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases
<p>BPNet models for scoring SNPs in cell types of the human retina</p>
MOJITOO: a fast and universal method for integration of multimodal single cell data
<p>MOJITOO benchmarking seurat Robjects.</p>
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.