Datarail's experimental design repository is a system for aiding the design, analyses, and visualization of high throughput dose response experiments.
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Computational workflow for design of dose-response experiments


  • The repository can be installed from command line as shown below
    $ git clone
  • To install dependencies and enable importing modules from any location on your local machine, cd into the datarail folder, followed by the command below.
    $ pip install -e .

Getting started

  • Set up the well and plate level metadata files as shown in datarail/examples
  • Start a Jupyter notebook or IPython session.
  • The layout of drugs on doses across 96/384 well plates can be constructed using the code below. The pandas dataframe dfm contains the desingned layout. Refer to datarail/examples for a detailed explanation with examples.
    import pandas as pd
    from datarail.experimental_design import process_assay as pa
    dfp = pd.read_csv('plate_level_metadata.csv')
    dfm = pa.randomize_wells(dfp)
    dfm.to_csv('dose_response_layout_metadata.csv', index=False)
  • The metadata file can be exported to a .hpdd file that can be used by the D300 printer. The stock concentraion for the drug also needs to be provided for each drug in the assay.
    from datarail.experimental_design import hpdd_utils as hu
    hu.export_hpdd(dfm, dfs, 'dose_response_layout_metadata.hpdd')
  • The layout can be visualized using the code below
    from datarail.experimental_design import plot_plate_layout as ppl
    ppl.plot_summary(dfr, 'dose_response_layout_metadata.pdf')  
  • If the D300 software was used for designing the experiment, follow the steps below inorder to save the metadata file based on datarail convention for subsequent downstream analysis.
    • Open the .xml from D300 in Excel and save as a .xlsx file.
    • Use the code below to save the metadata in a dataframe dfm
    from datarail.experimental_design import export_D300_xml as edx
    dfm = edx.export2pd('D300_filename.xlsx')