The notebooks are built in a sequence and gradually introduce concepts of experimental design, QC, and data analysis of different biological assays.
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01a_enzyme_kinetics
Topics: enzyme kinetics, enzyme assays, fluorometry, assay variability and confidence intervals, Z-factor, Z-score based normalization, plate heatmap, hit extraction, molecule visualization, importing molecule bioactivity data. -
01b_enzyme_kinetics_in_chain
Topics: Running enzymatic assay for a number of plates, generating screen hit matrix, plot for all the plates in the screen. -
02a_ion_channel_development
Topics: Introduction to ion channels and assay development, ion flux assay normalization, ion channel kinetics time-series. -
02b_ion_channel_cherry_picking
Topics: Calcium influx assay, cherry picking, percent of activation or inhibition. -
02c_ion_channel_dose_response
Topics: Introduction to dose-response, Hill equation. -
03a_yeast_growth_screen
Topics: Running yeast growth assay, growth curve, growth score, filtering out aberrant curves. -
03b_yeast_growth_in_chain
Topics: Running yeast growth assay for a number of plates, filtering, generating screen hit matrix, plotting all the plates in the screen. -
03c_yeast_cherry_picking
Topics: Running yeast growth assay with different doses of the compounds. Generation of automatic ppt report. -
04a_imaging_screen
Topics: High-content screening and image analysis, reporter system, cell viability, systematic errors detection and correction. -
4b_imaging_assay_development
Topics: Exploration data analysis, PCA, batch effect. -
04c_imaging_dose_response
Topics: Activity versus viability, fitting dose-response for imaging data. -
05_xtt_assay
Dose-response assay for compound toxicity.
There are several options:
- Run the notebooks from Binder
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Run > conda install -c conda forge rdkit, and then run > pip install simplydrug
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Clone this repository: git clone https://github.com/disc04/simplydrug
The codebase relies on the following dependencies (tested version provided in parentheses):
- python (3.6.1)
- pubchempy (1.0.4)
- scipy (1.4.1)
- seaborn (0.10.0)
- python-pptx (0.6.18)
- wget(3.2)
- xlrd (1.2.0)
- rdkit (2019.09.3)
import pandas as pd
import simplydrug as sd
data = pd.DataFrame(pd.ExcelFile('hts_notebooks//test_data//enzyme_kinetics_data1.xlsx').parse(0))[['Well','0s','120s','240s', '360s']]
layout_path = 'hts_notebooks//test_data//enzyme_kinetics_layout.xlsx'
chem_path = 'hts_notebooks//test_data//compounds//example_chemicals.csv'
chem_plate = 'ex_plate1'
results = sd.add_layout(data, layout_path, chem_path = chem_path, chem_plate = chem_plate)
display(results.head())
To check our 384 well plate for systematic errors, we can use plate heatmap representation:
sd.heatmap_plate(df = results, layout_path = layout_path, features = ['120s'], path = None, save_as = None)
from IPython.display import Image
Image(filename = 'heatmap.png', width = 400)
Please refer to the documentation page for more information.