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69,051 results for “Cancer”
Mueller matrix imaging combining optical parameters of mice non-melanoma skin cancer tissue
<p>The dataset consists of the Mueller matrix elements and optical parameters acquired from the backscattered light using a CCD camera and Mueller matrix imaging technique.</p><p>This dataset contains 90 samples including 20 feature vectors for SCC, 33 feature vectors for normal and 37 feature vectors for papilloma.</p>
ColoPola: A dataset of colorectal cancer polarimetric images (Mueller matrix elements) for colorectal cancer detection
<p><strong>ColoPola</strong> dataset is <strong>Colo</strong>rectal cancer <strong>Pola</strong>rimetric images dataset</p> <p>The dataset consists of 572 slices (specimens) with 20,592 images, 284 slices of which were designated as cancer samples and 288 as normal samples.</p> <p>Each sample has 36 polarimetric images (i.e., HH, HV, HP, HM, HR, HL, VH, VV, VP, VM, VR, VL, PH, PV, PP, PM, PR, PL, MH, MV, MP, MM, MR, ML, RH, RV, RP, RM, RR, RL, LH, LV, LP, LM, LR, and LL).</p> <p>Each folder in the <strong>ColoPola</strong> dataset consists of 36 polarimetric images. Each image is 1280x1024 pixels in size and was created in the TIF file format (HH.tif, HV.tif, ..., LL.tif). </p>
MiRoR2 - P1 - Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review
<p>Data set for the study “Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review”, including search strategy, extraction form, extracted data with summary of results, and protocol</p>
Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs
<p>Supplemental data and analysis files for Perron et al.: "Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs" (<a href="https://www.nature.com/articles/s42003-022-03796-w">https://www.nature.com/articles/s42003-022-03796-w</a>). The .tar.gz files contain read counts associated with various RNA-seq analyses. The .rds files are single R object files that contain various analysis results tables. The .csv files also contain analysis results or sample metadata tables. See <a href="http://csg.lab.mcgill.ca/sup/pancancer_stability/">http://csg.lab.mcgill.ca/sup/pancancer_stability/</a> for a full description of the files.</p>
Phindr3D: Test Data Set 2 (human MCF10A breast cancer organoids)
<p>3D confocal image stacks of human MCF10A breast cancer organoids expressing different oncogenes to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP files.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p>
Table of Indications and Regimens from the National Cancer Control Programme, Ireland
<h4>Description:</h4> <p>A table containing all indications published by the National Cancer Control Programme (NCCP), Ireland. Each entry has an indication code, description, and disease; regimen code, name, and URL; and information regarding whether the indication contains molecular diagnostic criteria. Entries were last updated from the NCCP website on 2025-May-27.</p> <h4>Headings:</h4> <ul> <li>IndicationCode: NCCP indication code (ex: 00537a).</li> <li>IndicationDesc: Description of the indication taken from its relevant regimen (ex: "Monotherapy for the treatment of adults with relapsed or refractory CD22-positive B cell precursor acute lymphoblastic leukaemia (ALL). Adult patients with Philadelphia chromosome positive (Ph+) relapsed or refractory B cell precursor ALL should have failed treatment with at least 1 tyrosine kinase inhibitor (TKI).")</li> <li>CancerType: Manual annotation of disease category for the indication (ex: Leukaemia).</li> <li>HasGeneticCriteria: Manual TRUE/FALSE annotation indicating the presence or absence of genetic criteria for the indication, as described in the indication description or regimen document.</li> <li>GeneticCriteria: If HasGeneticCriteria is TRUE, the relevant criteria listed (ex: BCR-ABL1 positive).</li> <li>HasBiomarkerCriteria: Manual TRUE/FALSE annotation indicating the presence or absence of cellular biomarker criteria for the indication, as described in the indication description or regimen document.</li> <li>BiomarkerCriteria: If HasBiomarkerCriteria is TRUE, the relevant criteria listed (ex: CD22+).</li> <li>HasMolecularCriteria: For convenience, column stating TRUE if HasBiomarkerCriteria is True or HasGeneticCriteria is True.</li> <li>RegimenCode: NCCP code for the regimen associated with the indication (ex: 537).</li> <li>RegimenName: NCCP regimen name (ex: Inotuzumab ozogamicin Monotherapy)</li> <li>NCCPRegimenCategories: NCCP disease categories associated with the regimen (ex: Leukaemia/BMT).</li> <li>RegimenURL: URL to the NCCP regimen document.</li> <li>Notes: Miscellaneous notes containing notes from NCCP regimen documents or further explanations.</li> </ul> <p><br> </p>
A dataset of colorectal cancer histopathological images
<p>The dataset contains the histopathological images of the ColoPola dataset (https://doi.org/10.5281/zenodo.10068018).</p> <p>CLCXYYZZNN_Hx</p> <p>CLC: colorectal (cancer) tissue</p> <p>NLC: normal tissue</p> <p>X - Times<br>YY - Sample number<br>ZZ - Serial number<br>NN - Image number<br>H - Magnification</p>
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
Evaluation of PD-L1 expression in vulvar cancer
<p><strong>background & Aims: </strong>Vulvar cancer accounts for about 4% of all female genital malignancies. Squamous cell carcinoma is the most common histological type.PD-L1 is a membranous protein of nucleated cells. By binding to its PD-1 receptor, PD-L1 inactivates T lymphocytes. It plays a key role in the treatment of some cancers. Our aims were to study the expression profile of PD-L1 in vulvar cancer by immunohistochemistry and to correlate its expression with survival rates. <strong>Methods:</strong> A retrospective study was conducted in the Pathology Department of Saleh Azaiez Institute, involving 55 patients followed for vulvar cancer over a period of 13 years from January 2008 to December 2021. Clinicopathologic data were collected from medical records and pathology reports. Immunohistochemical analysis was performed using an automaton (Leica Biosystems™). <strong>Results:</strong> PD-L1 expression in vulvar squamous cell carcinoma was observed in 44% of cases. This expression was noted in 11% of cases at the level of lymphocytes whose dissection was negative in all cases with a maximum tumor size of 40 mm. PDL1 was expressed in tumor cells in 33% of cases where there was a positive inguinal dissection in 22% of cases and a maximum tumor size of 90 mm. <strong>Conclusions:</strong> The immune checkpoint inhibitor PD-1/PD-L1 seems to have an important prognostic effect on the development of some cancers (breast cancer, lymphoma). Indeed, the expression of PD-L1 in tumors is often considered a factor of poor prognosis due to its immunosuppressive activity in tumor tissues. Many studies have attempted to determine the prognostic factors of vulvar cancer in order to optimize therapeutic management. PD-L1 expression in vulvar squamous cell carcinoma has not been studied before, hence the interest of our study which suggested, as in the literature, a prognostic role for this protein.</p>
Cancer screening attendance rates in transgender and gender-diverse patients: a systematic review and meta-analysis
<p>Supplementary Data to support the findings of a systematic review investigating cancer screening rates in transgender and gender-diverse individuals.</p>
Contribution of allelic imbalance to colorectal cancer
<p><strong>Point mutations in cancer have been extensively studied but chromosomal gains and losses have been more challenging to interpret due to their unspecific nature. Here we examine high-resolution allelic imbalance (AI) landscape in 1699 colorectal cancers, 256 of which have been whole genome sequenced (WGSed). The imbalances pinpoint 38 genes as plausible AI targets based on previous knowledge, and unbiased CRISPR-Cas9 knockout and activation screens identified altogether 79 genes within AI peaks regulating cell growth. Genetic and functional data implicates loss of TP53 as a sufficient driver of AI. The WGS highlights an influence of copy number aberrations on the rate of detected somatic point mutations. Importantly, the data reveal several associations between AI target genes, suggesting a role for a network of lineage-determining transcription factors in colorectal tumorigenesis. Overall, the results unravel the contribution of AI in colorectal cancer and provide a plausible explanation why so few genes are commonly affected by point mutations in cancers.</strong></p>
Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation: Confocal and atomic force microscopy
<p><strong>Summary</strong></p> <p>Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. <em>Scientific Reports </em><strong>2019, </strong>9, 1660</p> <p>This dataset includes image data of <em>atomic force microcopy</em> (Young modulus) and <em>confocal microscopy</em>(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. </p> <p><strong>Materials and Methods</strong></p> <p><em>Cells, cell culture conditions</em></p> <p>Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. </p> <p><em>Long-term zinc (II) treatment of cell cultures</em></p> <p>Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. </p> <p><em>Actin and tubulin staining</em></p> <p>β-tubulin was labeled with anti- β tubulin antibody [EPR1330] (ab108342) at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. </p> <p><em>Confocal microscopy</em></p> <p>The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.</p> <p><em>Atomic force microscopy</em></p> <p>We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec).</p> <p>The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal.</p> <p><em>Image analysis</em></p> <p>Fluorescence microscopy data were analyzed in ImageJ 1.52h and Python 3.7.1 as follows: cells were manually segmented using actin fluorescence channel, two regions were created for analysis: whole cell and cell periphery, lining a 4 μm thick region around cell border and including most of periphery actin cytoskeleton. In these two regions following parameters were measured for both actin and tubulin fluorescence: Integrated intensity, median intensity, and following regions were measured to describe cell morphology: Cell area, Maximum caliper (max feret diameter), roundness, and aspect ratio. Moreover, stress fibers were manually segmented in every cell and following parameters were measured: number of fibers per cell, feret angle of fiber, integrated intensity, fiber length, mean intensity. Next, a standard deviation of feret angles of individual fibers was calculated relatively to mean of feret angle using a circstd function from scipy package for Python.</p> <p><strong>Identification of files</strong></p> <p><em>Microscopy data</em></p> <p>Files are separated into individual zip files. The dataset of <em>confocal microscopy </em>is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices.</p> <p>The dataset of <em>atomic force microscopy </em>(AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m.</p> <p><em>Confocal microscopy analysis files</em></p> <p>Following files are csv tables including image analysis of actin/tubulin staining captured by confocal microscope:</p> <p>Cytoskeleton_fluo_analysis_Cell_Cell_periphery_morphology.csv: table includes analyzed data for actin and tubulin staining in following cellular regions: cell, cell periphery. Standard ImageJ parameters regarding intensity and morphology included.</p> <p>Cytoskeleton_fluo_analysis_Fibers.csv: table includes results of manual segmentation and consequent analysis of actin stres fibers in the cells. Apart from standard ImageJ parameters, also number of stress fibers per cell and standard deviation of fiber angle relative to the cell mean angle (for details see methods) are included.</p>
RNA datasets to derive predictors for immune checkpoint inhibitor therapy of non-small cell lung cancer
<p>Nanostring nCounter datasets and corresponding clinical data of tumor samples of patients with advanced NSCLC who received anti-PD-1 immuntherapy. Prospectively divided into a discovery and a validation cohort.</p> <p>Please cite the corresponding publication in Annals of Oncology (10.1093/annonc/mdz049)</p>
Infrared Chemical Image of a Breast Cancer Tissue Microarray
<p>This data set relates to an open access paper published in Analyst <em>Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets</em> by Jiayi Tang, Alex Henderson* and Peter Gardner. <a href="https://doi.org/10.1039/D0AN02155E"> https://doi.org/10.1039/D0AN02155E</a></p> <p>The files in this archive are mid-infrared spectroscopy chemical images of a breast cancer tissue microarray. The tissue microarray is BR20832 from Biomax. <a href="http://www.biomax.us/tissue-arrays/Breast/BR20832">http://www.biomax.us/tissue-arrays/Breast/BR20832</a></p> <p>Processed versions of these data in MATLAB file format can be found in another Zenodo archive at <a href="https://doi.org/10.5281/zenodo.4730312">https://doi.org/10.5281/zenodo.4730312</a></p> <p>This processed data refers to a paper published in Analyst</p>
Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes
<p>Underlying dataset and extended dataset of the results described in the article "Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes".</p>
Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer
<p>A dataset with spatial immune cell information on a lung cancer cohort from Uppsala University Hospital, Sweden, with anonymized clinical data. For more information please refer to the 'readme' file and the original study (https://doi.org/10.1016/j.ejca.2023.02.012).</p>
Systematic investigation of mitochondrial transfer between cancer and T cells at singlecell level
<p>The benmark datasets about MT transfer, including mtSNV profile, coverage information, cell information and expression information.</p>
Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification
<p>Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</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.