circacompare

This is a Python package that allows for the statistical analyses and comparison of two circadian rhythms. The Python implementation which you're looking at is an implementation of this R-package. However, it comes with the added functionality of having control over the loss function used (opposed to least squares, which is used in the R-package).

This work is published here and can be cited as:

Rex Parsons, Richard Parsons, Nicholas Garner, Henrik Oster, Oliver Rawashdeh, CircaCompare: A method to estimate and statistically support differences in mesor, amplitude, and phase, between circadian rhythms, Bioinformatics, https://doi.org/10.1093/bioinformatics/btz730

Usage

circa_single and circa_single_plot

circa_single is used when there is only a single group of data being analysed. The result returned includes the point-estimates and 95% confidence intervals for parameters representing:

1. Mesor
2. Amplitude
3. Phase

circa_single_plot is used in the same way as circa_single but rather than returning results, it returns a plot of the data and the curve of best fit (the sinusoidal function constructed from point estimates from circa_single). As this is a simple wrapper of circa_single it requires the same arguments.

Example

import numpy as np
from compare import circa_single
from plot import circa_single_plot
from generator import generate_data


time = np.linspace(0, 2 * np.pi, 100)   # time is given in radian hours 
measure = generate_data(t=time, k=1, a=10, p=4, noise=0.5)

result = circa_single(t0=time, y0=measure)
print(result.x)                         # result.x contains the estimates for the mesor, amplitude, and phase of the rhythm
print(result.confidence_intervals)      # result.confidence_intervals contains the confidence intervals for the above estimates

circa_single_plot(t0=time, y0=measure)

circacompare and circacompare_plot

circacompare is used when there are two groups of rhythmic data to be compared to each other, group 0 and group 1. The result returned includes the point-estimates and 95% confidence intervals for:

1. Mesor of group 0
2. Change in Mesor from group 0 to group 1
3. Amplitude of group 0
4. Change in Amplitude from group 0 to group 1
5. Phase of group 0
6. Change in Phase from group 0 to group 1

circacompare_plot is used in the same way as circacompare but rather than returning results, it returns a plot of the data and the curve of best fit (the sinusoidal function constructed from point estimates from circacompare). As this is a simple wrapper of circacompare it requires the same arguments.

Example

time = np.linspace(0, 2 * np.pi, 100)
measure = np.concatenate(
    (generate_data(t=time, k=1, a=10, p=4, noise=0.4), generate_data(t=time, k=5, a=3, p=1, noise=0.2)))
group = np.concatenate((np.zeros(len(time)), np.ones(len(time))))
time = np.concatenate((time, time))

result = circacompare(t0=time, y0=measure, g0=group)
print(result.x)                     # has estimates for all parameters
print(result.confidence_intervals)  # has confidence intervals for all parameters

circacompare_plot(t0=time, y0=measure, g0=group)

Additional Information

R-package and Shiny App.

If you're having troubles or have suggestions for improvement, please create an issue or email me (rex.parsons94@gmail.com).

Please cite the original paper when used.