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86 results for “cell annotation”
Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)
<p>This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software.</p> <p>Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.</p>
Automated cell annotation in scRNA-seq data using unique marker gene sets
<p>Single-cell RNA sequencing has revolutionized the study of cellular heterogeneity, yet accurate cell type annotation remains a significant challenge. Inconsistent labels, technological variability, and limitations in transferring annotations from reference datasets hinder precise annotation. This study presents a novel approach for accurate cell type annotation in scRNA-seq data using unique marker gene sets. By manually curating cell type names and markers from 280 publications, we verified marker expression profiles across these datasets and unified nomenclatures to consistently identify 166 cell types and subtypes. Our customized algorithm, which builds on the AUCell method, achieves accurate cell labeling at single-cell resolution and surpasses the performance of reference-based tools like Azimuth, especially in distinguishing closely related subtypes. To enhance accessibility and practical utility for researchers, we have also developed a user-friendly application that automates the cell typing process, enabling efficient verification and supporting comprehensive downstream analyses. The desktop application can be accessed at <a href="https://omnibusx.com/">https://omnibusx.com</a>.</p>
Annotated quantitative phase microscopy cell dataset of various adherent cell lines for segmentation purposes
<p>This dataset contains microscopic images of multiple cell lines captured by quantitative phase microscopy (QPI) without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p>Our data consist of quantitative phase microscopy images:</p> <ul> <li>244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), 25 labelled images of G361, 25 labelled images of A2050 and 25 labelled images of HOB cells , in the paper designated as "<em>labelled"</em>, and</li> <li>1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "<em>unlabelled"</em>.</li> </ul> <ul> <li>See Vicar et al. XXXX 2021 DOI XXX (TBA after publishing)</li> <li>Code using this dataset is available at <a href="https://github.com/tomasvicar/Deep-QPI-Cell-Segmentation">github.com/tomasvicar/Deep-QPI-Cell-Segmentation</a></li> </ul> <p><strong>Materials and methods </strong></p> <p>A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5% CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic microscope (Telight, Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart.</p> <p><strong>Folder structure and file and filename description</strong></p> <p><strong>labelled </strong><em>(labelled)</em>: cells with segmentation labels, e.g.<br> 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values)<br> 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV.</p> <p><strong>unlabelled </strong><em>(unlabelled)</em>: 11 varying cell lines, total 1819 FOVs, 32bit tiff image (in pg/um2 values)</p> <p> </p>
Nontuberculous mycobacteria persistence in a cell model mimicking alveolar macrophages (genome assembly and annotation dataset)
<p>This dataset includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the prediction transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files) and the respective amino acid sequences of the translated CDS sequences (.faa files) for the following Nontuberculous mycobacteria (NTM) strains: <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 (reference strain), <em>Mycobacterium avium</em> ATCC25921 (reference strain), <em>M. avium </em>60/08 (clinical strain), <em>Mycobacterium fortuitum</em> ATCC6841 (reference strain) and <em>M. fortuitum</em> 747/08 (clinical strain).</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB30455).</p> <p>The associated article can be found here: <a href="https://www.ncbi.nlm.nih.gov/pubmed/31035520">https://www.ncbi.nlm.nih.gov/pubmed/31035520</a></p> <p> </p>
Fluoro-Forest CODEX data for random Forest-based cell type annotation
Open the record for dataset details and reuse information.
2D bright field yeast cell images with ground truth annotations
<p>Dataset used to evaluate the method described in "Yeast cell detection and segmentation in bright field microscopy", ISBI 2014 (<strong>DOI: </strong>10.1109/ISBI.2014.6868107).</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Hierarchical Planar Correlation Clustering for Cell Segmentation", EMMCVPR 2015. (DOI: 10.1007/978-3-319-14612-6_36)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
2D phase contrast HeLa cells images with ground truth annotations
<p>Original images are from http://www.robots.ox.ac.uk/~vgg/software/cell_detection/. This software is associated with the publication "Learning to Detect Cells Using Non-overlapping Extremal Regions", MICCAI 2012. (DOI: 10.1007/978-3-642-33415-3_43)</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
2D bright field Fission yeast cell images with ground truth annotations
<p>Original images are from http://www-bcf.usc.edu/~forsburg/pombeX.html. This software is associated with the publication "PombeX: robust cell segmentation for fission yeast transillumination images", PLoS One 2013. (doi: 10.1371/journal.pone.0081434)</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
Data for MAPS: Pathologist-level Cell Type Annotation from Tissue Images through Machine Learning
<p>Extracted datasets and processed images used to generate figures in manuscript <i>"MAPS: Pathologist-level Cell Type Annotation from Tissue Images through Machine Learning".</i></p>
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Hela Cell dataset with annotations for dividing and non dividing cells (TYX)
<p>This dataset was created to be used for training ONEAT networks. Each movie is accompanied by 2 csv files, one containing the location of dividing cells and other one containing the location of non-dividing cells. The training data was created using yoloneat-viz repo: https://github.com/Kapoorlabs-paris/yoloneat-viz/blob/main/examples/TrainingDataMaker.ipynb. The notebook can be modified to include N types of cell events and the program is in no way limited to creating classification of just 2 type of cell events. </p>
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
<p>Single <span>cell RNA sequencing (scRNAseq) is a powerful technique that continues to expand across various biological applications. However, incomplete 3' UTR annotations can impede single cell analysis resulting in genes that are partially or completely uncounted. Performing scRNAseq with incomplete 3' UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single cell isoform sequencing (ScISOr-Seq) in tandem with scRNAseq can rapidly improve 3' UTR annotations. Using threespine stickleback fish (</span><em>Gasterosteus aculeatus</em><span>), we show that gene models resulting from a minimal embryonic ScISOr-Seq dataset retained 26.1% greater scRNAseq reads than gene models from Ensembl alone. Furthermore, pooling our ScISOr-Seq isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the ScISOr-Seq only dataset. In addition, isoforms identified by ScISOr-Seq included thousands of new splicing variants. The improved gene models obtained using ScISOr-Seq lead to successful identification of cell types and increased the reads identified of many genes in our scRNAseq stickleback dataset. Our work illuminates ScISOr-Seq as a cost-effective and efficient mechanism to rapidly annotate genomes for scRNAseq.</span></p>
CODEX multiplexed imaging cell datasets used for using STELLAR to transfer cell type annotations to other tissues and donors
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on 24 sections of the human intestine from 3 donors (B004, B005, B006) using a panel of 47 oligonucleotide-barcoded antibodies. We also performed CODEX imaging on both human tonsil and Barrett's esophagus (BE) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. Output of this process were dataframes of 870,000 cells and 220,000 cells respectively with fluorescence values quantified from each marker.</p>
Annotation for human genomic variation during the BMP4-induced conversion from embryonic stem cells to trophoblast by bone
<p>Whole genomic data of three cell lines were sequenced. These three cell lines are two hESC lines (H1 & H9) by invasion assays and iPSC cell line MRucR. These three cell lines are two hESC lines (H1 & H9) by invasion assays and iPSC cell line MRucR. Paired-end DNA libraries were prepared according to manufacturer’s instructions (Illumina Truseq Library Construction). And then, valid sequencing data is mapped to the reference genome (UCSC hg19) by Burrows-Wheeler Aligner (BWA) software. Reads that aligned to genomic regions were collected for mutation identification and subsequent analysis. Samtools mpileup and bcftools are used to do variant calling and identify SNP, indels. Control-free (Boeva V et al.2012) is utilized to do CNV detection. And BreakDancer (Chen K et al.2009) is applied to detect SV information. And, Single nucleotide variant (SNV) and small somatic insertions and deletions (indels) were identified using Strelka2.</p>
Automated Cell type Annotation Testing with Clear Cell Renal Cell Carcinoma
Open the record for dataset details and reuse information.
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-ERG (endothelial cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-MNDA (myeloid cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD235a (red blood cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-αSMA (smooth muscle cells / cancer associated firbroblasts)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-panCK (epithelial cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
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.