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143 results for “deconvolution”
PSF Estimation and deconvolution: models, microscopy images, and datasets
<p>This is the accompanying dataset for the publication Adrian Shajkofci, Michael Liebling, “Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy,” IEEE Transactions on Image Processing, vol. 29, pp. 5848-5861, 2020.</p> <p>Publications based on this data must cite the above paper.<br> <br> BibTeX Citation:<br> @ARTICLE{shajkofci.liebling:20,<br> author={A. Shajkofci and M. Liebling},<br> journal={IEEE Trans. Image Proces.}, <br> title={Spatially-Variant {CNN}-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy},<br> year={2020},<br> volume={29},<br> number={},<br> pages={5848-5861},<br> doi={10.1109/TIP.2020.2986880}}<br> </p> <p>In the archive, you will find :</p> <ul> <li>- Trained models for PSF estimation and deconvolution</li> <li>- Synthetic training dataset of cells and beads</li> <li>- Stacks of multi-channel fluorescence microscopy images of HeLa cells, rat brain cells, beads and plant cells to test the PSF estimation tool, deconvolution algorithm or auto-focus algorithm.</li> <li>- Stacks of tilted grid (3, 6 and 9 degrees) using astigmatic lenses for depth estimation.<br> </li> </ul> <p>The code for running the models is available here:<br> <a href="https://github.com/idiap/psfestimation">https://github.com/idiap/psfestimation</a></p> <p><br> <strong>Reference paper</strong></p> <p>A. Shajkofci and M. Liebling, "Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy," in IEEE Transactions on Image Processing, vol. 29, pp. 5848-5861, 2020, doi: 10.1109/TIP.2020.2986880.</p> <p> </p> <p><strong>Ethical compliance</strong></p> <p>The post-mortem stained and fixed tissue slices whose images are included in this data set were reused from experiments approved by the EPFL ethics committee.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by the Swiss National Science Foundation under Grants 206021_164022 “Platform for Reproducible Acquisition, Processing, and Sharing of Dynamic, Multi-Modal Data” and 200020_179217 “COMPBIO: Computational biomicroscopy: advanced image processing methods to quantify live biological systems”</p>
3D Deconvolution reference dataset
<p>Paired airy-light-sheet data with fluorescent bead and sample data acquired at NPL.</p>
Fourteen Years of Cellular Deconvolution: Methodology, Applications, Technical Evaluation, and Outstanding Challenges
<p>The CELLxGENE datasets are used to reproduce the benchmarking results in the article "Fourteen Years of Cellular Deconvolution: Applications, Benchmark, Methodology, and Challenges". The instruction to download the data from CELLxGENE can be found at https://github.com/tinnlab/DeconBenchmark-analysis.</p>
Fourteen Years of Cellular Deconvolution: Methodology, Applications, Technical Evaluation, and Outstanding Challenges
<p>The Tabula Sapiens dataset is used to reproduce the benchmarking results in the article "Fourteen Years of Cellular Deconvolution: Applications, Benchmark, Methodology, and Challenges". The data can be downloaded at https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5, by selecting "Tabula Sapiens - All Cells".</p>
Determination of solid fat content in emulsions using TD-NMR FID-CPMG deconvolution
<p>Determination of solid fat content in emulsions using TD-NMR FID-CPMG deconvolution</p>
Machine Learning GAN Deconvolution
<p>We initially benchmarked our GAN against the LUCYD network using a Z-stack of a selection U2OS cells acquired by widefield microscopy from the Li et al dataset (Li et al., 2022) . Following this we ran another benchmark against Deconwolf to gain a direct comparison of the improvements achieved using a Z-stack image from the ChrX-36plex OligoFISSEQ dataset (Nguyen et al., 2020).</p> <p><br>Data is organised as follow:</p> <p>├───Input<br>└───Output<br> ├───Deconwolf<br> ├───GAN<br> └───LUCYD</p> <p></p>
Experiment-742-Deconvolution (defaults)-04 (Turn Around Y)
<p><span>Spatial phenotyping of tryptase in rat lung mast cells. Three-dimensional models of intracellular localization of tryptase. Technique: immunohistochemical tryptase staining. Nuclei are counterstained with DAPI. Group of H<sub>2 </sub>exposure. The cytoplasm is unevenly filled with tryptase-positive granules.</span></p>
Experiment-740-Deconvolution (defaults)-03 (Turn Around Y)
<p><span>Spatial phenotyping of tryptase in rat lung mast cells. Three-dimensional models of intracellular localization of tryptase. Technique: immunohistochemical tryptase staining.</span> <span>Nuclei are counterstained with DAPI. MCT group. Interactions of mast cells with each other and other cells.</span></p>
Experiment-739-Deconvolution (defaults)-02 (Turn Around Y)
<p><span>Spatial phenotyping of tryptase in rat lung mast cells. Three-dimensional models of intracellular localization of tryptase. Technique: immunohistochemical tryptase staining. Nuclei are counterstained with DAPI. Control group. Mast cells are located at a paracrine distance from each other.</span></p>
Experiment-738-Deconvolution (defaults)-01 (Turn Around Y)
<p><span>Spatial phenotyping of tryptase in rat lung mast cells. Three-dimensional models of intracellular localization of tryptase. Technique: immunohistochemical tryptase staining. Nuclei are counterstained with DAPI. Control group. Secretory granules with different trypatase contents are located in separate loci of the mast cell. The peripheral localization of tryptase in secretory granules is clearly visible.</span></p>
Supplementary tables for: CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders
<p>Supplementary tables for: CHAS, a deconvolution tool, infers cell type-specific signatures in bulk brain histone acetylation studies of brain disorders. </p> <p>Supplementary Table 1: S1_AD_H3K27ac_Marzi2018_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions, GO enrichment analysis results using the two sets of differentially acetylated regions. </p> <p>Supplementary Table 2: S2_PD_H3K27ac_Toker2022_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions.</p> <p>Supplementary Table 3: S3_ASD_H3K27ac_Sun2016_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions for the prefrontal cortex and the cerebellum, GO enrichment analysis results using the two sets of differentially acetylated regions in the prefrontal cortex. </p> <p>Supplementary Table 4: S4_SCZ_BPD_H3K27ac_Girdhar2022_CHAS_analysis.xls, includes the sample metadata, differentially acetylated regions when controlling for CHAS scores and CHAS-MF cell type proportions, GO enrichment analysis results using the two sets of differentially acetylated regions. </p> <p> </p> <p> </p> <p> </p> <p> </p>
Constrained Spherical Deconvolution Tractography Reveals Cerebello-Mammillary Connections in Humans
<p>According to the classical view, the cerebellum has long been confined to motor control physiology; however, it has now become evident that it exerts several non-somatic features other than the coordination of movement and is engaged also in the regulation of cognition and emotion. In a previous diffusion-weighted imaging-constrained spherical deconvolution (CSD) tractography study, we demonstrated the existence of a direct cerebellum-hippocampal pathway, thus reinforcing the hypothesis of the cerebellar role in non-motor domains. However, our understanding of limbic-cerebellar interconnectivity in humans is rather sparse, primarily due to the intrinsic limitation in the acquisition of in vivo tracing. Here, we provided tractographic evidences of connectivity patterns between the cerebellum and mammillary bodies by using whole-brain CSD tractography in 13 healthy subjects. We found both ipsilateral and contralateral connections between the mammillary bodies, cerebellar cortex, and dentate nucleus, in line with previous studies performed in rodents and primates. These pathways could improve our understanding of cerebellar role in several autonomic functions, visuospatial orientation, and memory and may shed new light on neurodegenerative diseases in which clinically relevant impairments in navigational skills or memory may become manifest at early stages.</p>
Mixed model-based deconvolution of cell-state abundances along a one-dimensional trajectory [csd-eQTL]
<p><strong>README:</strong></p> <p>The full summary data of the cell-state-dependent eQTLs for GTEx Esophagus Mucosa (n=497) are stored in the .parquet format.</p> <p>An example of the file name:</p> <p><strong>"GTEx_Esophagus_Mucosa_bin1.cis_qtl_pairs.1.parquet.gz"</strong> means the summary data of csd-eQTLs for bin1 of chromosome 1.</p>
Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution: Data
<p>The data was simulated in GATEv8.0. The macro's and other files for the simulation can be found in the folders: Sim_50_40, Sim80_60 and Sim90_70. Here, the different files to perform the simulation are given, as well as the output data, found in the folders Flats and Projs (flatfields and projections) in the form of a csv-file. The csv-file contains 5000 rows (one for each image), and each row contains a flattened 450x450 image.</p> <p>The files for visualization of the fiber directions are given in the folder MRtrix. This folder contains the reconstructed scatter magnitudes and their respective directions in a mat-file. On this set of directions the constrained spherical deconvolution is applied using the sf_dirs.mif, sf_mask.mif, sf_response.mif and data.mif files. This returns the orientation density function in each voxel as output (odf_csd_14.mif). An alternative approach is performing a Funk-Radon transform on the coefficients of the scatter function (data_coeffs_10.mif) to obtain odf_frt_10.mif.</p> <p>Using a peak finding algorithm the fiber orientations are extracted from these odf-files and written to peaks_x.mif files, with or without a threshold (10% of largest amplitude).</p>
CimpleG DNAm benchmarking datasets for cell-type classification and deconvolution
<p><strong>Two large DNAm benchmarking datasets</strong> specifically gathered and <strong>curated for cell-type classification and deconvolution problems.</strong></p> <p>It includes <strong>a leukocytes dataset</strong> and <strong>a somatic cells dataset</strong> in the GenomicRatioSet format from the minfi package.</p> <p>These can be easily <strong>loaded into R with the readRDS function:</strong></p> <blockquote> <p>my_data <- readRDS("CimpleG_benchmarking_datasets_2/leukocytes/tidy_leuk_data.rds")</p> </blockquote> <p>Each dataset includes therein sample data like GEO accession numbers, sample name or ID in their original dataset, cell-type label, one-hot encoded data for each cell-type, preferred train/test splits, and others.</p> <p>Alternatively, <strong>you can also load the individual .csv files</strong>. If you choose this option, I recommend using the function fread from the package data.table. Below I briefly describe these (.csv and .txt) files for the leukocytes dataset, the same logic applies to the somatic cells dataset:</p> <ul> <li> <div> <div>tidy_leuk_data_beta-values.csv</div> <div> <ul> <li>Methylation Beta values matrix</li> </ul> </div> </div> </li> <li> <div> <div> <div>tidy_leuk_data_m-values.csv</div> <div> <ul> <li>Methylation M values matrix</li> </ul> </div> </div> </div> </li> <li> <div> <div> <div>tidy_leuk_data_probe-annotation.txt</div> <div> <ul> <li>Note regarding probe annotation</li> </ul> </div> </div> </div> </li> <li> <div> <div> <div>tidy_leuk_data_probe-metadata.csv</div> <div> <ul> <li>Probe metadata matrix (chr and location)</li> </ul> </div> </div> </div> </li> <li> <div> <div> <div>tidy_leuk_data_sample-metadata.csv</div> <div> <ul> <li>Sample metadata matrix (sample ID, cell type labels, one-hot encoded labels, etc.)</li> </ul> </div> </div> </div> </li> </ul>
STCGAN: a novel Cycle-Consistent Generative Adversarial Network for Spatial Transcriptomics Cellular Deconvolution
Open the record for dataset details and reuse information.
benchmarking data for cell deconvolution
<p>benchmarking data for cell deconvolution</p>
Datasets collected for Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
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Datasets for Deconvolution with MuSiC Tutorial
<p>These datasets are used in the MuSiC deconvolution tutorial in Galaxy. They were retrieved from the EBI's Array Express platform, originally published by:</p> <p>Segerstolpe Å, Palasantza A, Eliasson P, Andersson EM, Andréasson AC, Sun X, Picelli S, Sabirsh A, Clausen M, Bjursell MK, Smith DM, Kasper M, Ämmälä C, Sandberg R. Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes. Cell Metab. 2016 Oct 11;24(4):593-607. doi: 10.1016/j.cmet.2016.08.020. Epub 2016 Sep 22. PMID: 27667667; PMCID: PMC5069352.</p>
Datasets for MuSiC Deconvolution benchmarking tutorial suite.
<p>These are the datasets for the MuSiC deconvolution benchmarking tutorial suite.</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.