Computational workflow for design of dose-response experiments

Installation

• The repository can be installed from command line as shown below

  $ git clone https://github.com/datarail/datarail.git
  

• 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')