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2 results for “real-time deformability cytometry”
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
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