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Highly Multiplexed Immunofluorescence Images And Single-cell Data Of Immune Markers In Tonsil And Lung Cancer

Rumana Rashid, Giorgio Gaglia, Y. Chen, Jia-Ren Lin, Z. Du, Z. Maliga, D. Schapiro, C. Yapp, Jeremy L. Muhlich, A. Sokolov, P. Sorger, S. Santagata
Published 2019 · Computer Science, Biology

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In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools. METADATA SUMMARY
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