Code for Harris et al., Nature Methods 2016

doi:10.1038/nmeth.3852

All R code in this repository was written by Darren Tyson. All Python code was written by Leonard Harris.

Code for generating manuscript figures is provided in the code_for_figs directory. All experimental data needed to produce the figures is provided in the data_for_figs directory.

Standalone software for DIP rate estimation from experimental data is provided in the dipDRC directory. An R script that runs an example application of the dipDRC.r code is provided in the example_dipDRC directory.

Instructions for running the R code

The R statistical software package can be obtained free of charge from www.R-project.org.

Required packages are:

deSolve
gplots
grid
drc
MASS

Assuming all required packages are installed, to generate all the graphs shown in the manuscript figures and supplementary information, type the following in the R console:

source("[path_to_download_dir]/DIP_rate_NatMeth2016/code_for_figs/RcodeForFigs.r", chdir=TRUE)

where [path_to_download_dir] is the directory to which you downloaded this repository (e.g., /Users/mycomp/git).

An example application of the dipDRC.r code can be performed without downloading the entire Git repository by copying all the code shown in:

(https://github.com/QuLab-VU/DIP_rate_NatMeth2016/blob/master/example_dipDRC/makeDRCexample.r)

NOTE that running this code will download and install a number of R libraries automatically

Alternatively, the code can be run from a local version using a similar format as for generting the manuscript figures:

source("[path_to_download_dir]/DIP_rate_NatMeth2016/example_dipDRC/makeDRCexample.r", chdir=TRUE)

The output is a graphics window with two plots, one for each cell line and drug condition, and a list of the two drm (dose-response model) objects, described in detail in the package information for the drc library (https://cran.r-project.org/web/packages/drc/drc.pdf).

The data used in this example are the raw breast cancer (MDA-MB-231) cell counts for rotenone and phenformin treatments. They can be found in the file dipDRC_example_data.csv within the example_dipDRC directory. The data are from a single experiment with two technical replicates. The data file is structured as a seven-column matrix with the following headers:

1. time
2. cell.count
3. cell.line
4. drug
5. conc
6. well
7. expt.date

Instructions for running the Python code

The Python code for generating Supplementary Figure 7 ("Theoretical effects of variations around a mean cell seeding density") is in the file makeSuppFig7.py within the code_for_figs/Python directory. Running the code requires installing pysb, a Python-based platform for biological modeling and simulation (see www.pysb.org). PySB version 1.0.1 can be installed (Mac/Linux) by opening a command prompt and typing

sudo pip install pysb

PySB also requires installing BioNetGen (www.bionetgen.org). Please see docs.pysb.org/en/latest/installation.html for detailed installation instructions.

Once PySB and all its dependencies are installed, makeSuppFig7.py can be run by simply typing

python makeSuppFig7.py

The output is three figures: timecourses.pdf (Supplementary Fig. 7a), boxplots.pdf (Supplementary Fig. 7b), and distributions.pdf (Supplementary Fig. 7c,d). Functionally, the code runs 10^5 control simulations and 10^4 drug-treated simulations for each of two scenarios: (i) variations in cell seeding density alone, and (ii) variations in both sampling time and cell seeding density. This amounts to a total of 2*10^5+2*10^4=220,000 simulations (i.e., it may take a while to finish).