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Introduction

Tissue-based cyclic immunofluorescent microscopy (t-CyCIF) is a simple method for generating highly multiplexed optical images from formalin-fixed paraffin-embedded (FFPE) tissue samples routinely used for histopathological diagnosis of human disease. The method is described in Lin et al. (2018), Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes .

Here we have reproduced several plots from the Figure 5 & 6 in the manuscript as an interactive Jupyter notebook. Here, we provides 3 plots (Figure 6A: Density scatter plot; Figure 6A: Histogram of 4 cycles; Figure 6D: Histogram to compared 2 cycle) Whereas the manuscript only has space for plots of a few antibody combinations from a few samples, this notebook allows replotting of any combination available in the full dataset (including one sets of Tonsil samples and two sets of Melanoma samples).

For more infomration, please refer to the publisher page: https://doi.org/10.7554/eLife.31657

Or visit our website: http://lincs.hms.harvard.edu/lin-elife-2018/

Instructions

To view the data interactively:

  1. First, install Jupyter. If you are new to Python, we recommend installing Anaconda Python (which contains Jupyter). You must get the Python 3.6 version of Anaconda, not the 2.7 version!

    https://www.anaconda.com/download/

  2. Open Jupyter either by clicking on its icon, or by typing jupyter notebook in a terminal. This will open up Jupyter in your web browser.

  3. Navigate to the folder where you downloaded the .ipynb file and the data files.

  4. Click on figure_6.ipynb to open the notebook. From there, click "Cell" and then "Run All" from the menu.

Please refer to Antibody_list_SampleA.csv & Antibody_list_SampleB.csv for detailed information about the antibodies used.