Fitting the Hill Equation to Modeled or Experimental Data
Background
The Hill equation is defined as follows:
y = bottom + ((top - bottom) * xnH) / (EC50nH+ xnH)
where bottom is the minimum activity; top is maximum activity; EC50 is the half-maximum effective dose; and nH is the Hill coefficient. The variables x & y are the stimuli dose and the cellular or tissue response. The hillfit module applies this biological equation, and is available with the MIT License.
Usage
installation
The following command are executed in a command prompt/terminal environment:
pip install hillfit
__init__
The data environment, in a Python IDE, is defined:
import hillfit
hf = hillfit.HillFit(x_data, y_data)
- x_data & y_data
list
orndarray
: specifies the x-values & y-values, respectively, of the raw data that will be fitted with the Hill equation. - bottom_param
bool
: specifies whether the accessorybottom
parameter of the Hill equation will be employed in the regression.
fitting()
The parameterized data is fitted to the Hill equation, with the following arguments and their default values:
hf.fitting(x_label = 'x', y_label = 'y', title = 'Fitted Hill equation', sigfigs = 6,
log_x = False, bottom_param = True, print_r_sqr = True, view_figure = True)
- x_label & y_label
str
: specifies the x-axis & y-axis labels, respectively, that will be applied to the regression plot for the raw data points and the fitted Hill equation. - title
str
: specifies the title of the regression plot for the raw data points and the fitted Hill equation. - sigfigs
int
: specifies the number of significant figures that will be used in printed instances of the fitted Hill equation. - log_x
bool
: specifies whether the x-axis of the regression plot will be converted into a logarithmic scale. - view_figure
bool
: specifies whether the regression plot will be printed in the Python environment. - print_r_sqr
bool
: specifies whether the coefficient of determination (R2) regression plot will be printed in the Python environment.
Accessible content
Many data sets and exported components of the fitted information are accessible through the hillfit
model object.
- top, bottom, ec50, & nH
float
: The fitted parameters of the Hill equation are accessible viahf.top
,hf.bottom
,hf.ec50
, &hf.nH
, respectively. - R2
float
: The coefficient of determination of the regression figure is available viahf.r_2
. - fitted_xs & fitted_ys
list
: The x- and y-values of the fitted Hill equation are accessible viahf.x_fit
&hf.y_fit
, respectively. - fitted_equation
str
: The fitted Hill equation, in an amenable string format for the eval() built-in function of Python that allows the user to directly execute the string as a function for an "x" variable, is accessible viahf.equation
. - figure & ax
matplotlib.figure
: The fig and ax objects of the regression figure are available viahf.figure
&hf.ax
, respectively. This allows users to edit the figures after the simulation is conducted. - x_data & y_data
ndarray
: The arrays of original data are available viahf.x_data
&hf.y_data
, respectively.
export()
The fitted Hill equation, with its data points and parameters, and the regression information are exported to a designated folder through the following syntax and arguments:
hf.export(export_directory = None, export_name = None)
- export_directory
str
: optionally specifies a path to where the content will be exported, where None selects the current working directory. - export_name
str
: optionally specifies a name for the folder of exported content, where None enables the code to design a unique folder name for the information.
Execution
Hillfit is executed through the following sequence of the aforementioned functions, which is exemplified in the example Notebook of our GitHub repository:
import hillfit
hf = hillfit.HillFit(x_data, y_data)
hf.fitting(x_label = 'test_x', y_label = 'test_y', title = 'Fitted Hill equation', sigfigs = 6, log_x = False, view_figure = True, print_r_sqr = True)
hf.export(export_directory = None, export_name = None)