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86 results for “cell annotation”
Data to run analyses present in the paper: Cell specificity of human regulatory annotations and their genetic effects on gene expression
<p>Data for the manuscript:</p> <p>Varshney A., H. VanRenterghem, P. Orchard, A. P. Boyle, M. L. Stitzel, <em>et al.</em>, 2019 Cell Specificity of Human Regulatory Annotations and Their Genetic Effects on Gene Expression. Genetics 211: 549–562. <a href="https://doi.org/10.1534/genetics.118.301525">https://doi.org/10.1534/genetics.118.301525</a></p> <p>The code to generate these data are at GitHub: https://github.com/ParkerLab/regulatoryAnnotations_comparisons</p> <p>Also see processed data available in this previous Zenodo record: https://zenodo.org/record/1413623</p>
Supplementary Video 1: Training deep learning models for cell image segmentation with sparse annotations
<p>Supplementary Video 1: Training deep learning models for cell image segmentation with sparse annotations</p> <p><strong>Acknowledgements</strong></p> <p>I am grateful to Michalis Averof (IGFL, CNRS) in whose lab this work was initiated and carried out, and to Shuichi Onami (RIKEN, BDR) in whose lab part of this work was carried out.</p> <p>Applications used in this movie:</p> <p>StarDist: <a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a></p> <p>QuPath: <a href="https://qupath.github.io/">https://qupath.github.io/</a></p>
Integrated and Annotated Single Cell Object
<p>Integrated and annotated Seurat object created with the following script: https://doi.org/10.5281/zenodo.8413883<br> using the following studies: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162577">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162577</a> <br> <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142016">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142016</a><br> <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135779">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135779</a></p>
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
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CODEX multiplexed imaging cell datasets used for using STELLAR to transfer cell type annotations to other tissues and donors
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Data from: Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation
VDJ rearrangement and somatic hypermutation work together to produce antibody-coding B cell receptor (BCR) sequences for a remarkable diversity of antigens. It is now possible to sequence these BCRs in high throughput; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza. A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion). Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites. However, codon frame and other effects have been observed to violate these parametric assumptions for such coding sequences, suggesting that a non-parametric approach to modeling the recombination process could be useful. In our paper, we find that indeed large modern data sets suggest a model using parameter-rich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position mutation probabilities, and that using such a model for inference leads to significantly improved results. We present an accurate and efficient BCR sequence annotation software package using a novel HMM "factorization" strategy. This package, called partis (https://github.com/psathyrella/partis/), is built on a new general-purpose HMM compiler that can perform efficient inference given a simple text description of an HMM.
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>
Raw count data with annotation in h5ad format of scRNAseq of blood and matched temporal artery in Giant Cell Arteritis
<p>Raw count data with annotation in h5ad format of scRNAseq of blood and matched temporal artery in Giant Cell Arteritis</p>
Kludt et al. Cell Reports Medicine - Dataset (manual annotations)
<p>Dataset of patches created from manually annotated whole-slide images of non-small cell lung cancer from the publication:</p> <p>"Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms"</p> <p>in Cell Reports Medicine 2024</p> <p>The pixel-level ground truth information is included (classes.txt).</p> <p><br>The dataset can be used for academic research purposes only.</p> <p> </p> <p>(c) Yuri Tolkach </p>
Jind-Multi: An extended framework to leverage multiple labeled datasets for the automatic annotation of single-cell RNA and ATAC data
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Data from: Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation
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Reproducible single cell annotation of programs underlying T cell subsets, activation states, and functions
GEO Series GSE297814. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing; Other.
GiRAFR improves gRNA detection and annotation in single cell CRISPR screens
GEO Series GSE216040. Homo sapiens. 2 samples. Type: Other.
Leveraging the Epigenomic Landscape to identify Histology-Specific Master transcription factors and to functionally annotate risk loci in renal cell carcinoma
GEO Series GSE188486. Homo sapiens. 144 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Rapid functional annotation of native enhancers in embryonic stem cells with a Neisseria meningitidis CRISPR-Cas9 system (BeadChip)
GEO Series GSE64057. Mus musculus. 16 samples. Type: Expression profiling by array.
Massively parallel reporter assays with multi-layer annotation identified cell-type-specific functional variants and genes associated with melanoma (MPRA)
GEO Series GSE210354. Homo sapiens. 16 samples. Type: Other.
Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex trait variants using single nucleus ATAC-seq
GEO Series GSE199253. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Third-party reanalysis.
Derivation and functional annotation of human hematopoietic subpopulations from single-cell DNA methylation data
GEO Series GSE106957. Homo sapiens. 233 samples. Type: Methylation profiling by high throughput sequencing.
devCellPy: A machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data
GEO Series GSE184943. Homo sapiens; Mus musculus. 1186 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide annotation of small RNAs expressed in HeLa and HepG2 cells
GEO Series GSE14362. Homo sapiens. 40 samples. Type: Non-coding RNA 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.