| Title: | Microbiome Oriented Circadian Rhythm Analysis Toolkit |
|---|---|
| Description: | The goal of 'kronos' is to provide an easy-to-use framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood. |
| Authors: | Thomaz Bastiaanssen [aut, cre], Sarah-Jane Leigh [aut] |
| Maintainer: | Thomaz Bastiaanssen <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.0.9000 |
| Built: | 2026-05-12 06:20:05 UTC |
| Source: | https://github.com/thomazbastiaanssen/kronos |
Snippet of example data to demonstrate the functionality of 'kronos' between and among three different groups
bigdatabigdata
A long format data.frame object with three 113 columns, features, and 94 rows, samples.
Dummy data modified from untargeted hippocampal metabolomics over the day to demonstrate rhythmicity between multiple groups in a large dataset.
Descriptional metadata for the 'bigdata' object, for the purpose of demonstration.
bigmetabigmeta
A long format data.frame object with three 113 columns and 94 rows.
Dummy metadata modified from untargeted hippocampal metabolomics over the day
Update 'kronos' formula.
build_kronos_formula(formula, time, verbose)build_kronos_formula(formula, time, verbose)
formula |
A formula. Use the |
time |
A string. Should be the column name containing the time values. |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large data sets. |
These functions provides a unified wrapper to retrieve results
from a list of kronosOut objects.
delistKronos_groupwise(kronos_list, padjust = TRUE)delistKronos_groupwise(kronos_list, padjust = TRUE)
kronos_list |
a list of preferrably named kronosOut objects. |
padjust |
a boolean. Toggles FDR using Benjamini Hochbergs procedure. |
A table with circadian output stats per group per feature.
KronosOut ObjectsThese functions provides a unified wrapper to retrieve results
from a list of kronosOut objects.
delistKronos_pairwise(kronos_list, padjust = TRUE)delistKronos_pairwise(kronos_list, padjust = TRUE)
kronos_list |
a list of preferrably named |
padjust |
a boolean. Toggles FDR using Benjamini Hochbergs procedure. |
ANOVA-like adjusted p-values for how each factor interacts with time.
Fit cosinor model for totality of data
fit_cosinor_model( formula, data, time = NULL, verbose = verbose, for_pw = FALSE )fit_cosinor_model( formula, data, time = NULL, verbose = verbose, for_pw = FALSE )
formula |
A formula. Use the |
data |
Input data |
time |
A string. Should be the column name containing the time values. |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large datasets. |
for_pw |
A boolean. Toggles whether to perform pairwise ANOVAs as a TukeyHSD-like post-hoc. |
Fit cosinor model for one aspect of data. Called by main 'kronos' function.
fit_groupwise_model(data, group, time, period, verbose)fit_groupwise_model(data, group, time, period, verbose)
data |
input data |
group |
A character string. Signifies which group will be assessed. |
time |
A string. Should be the column name containing the time values. |
period |
A numeric. The length of a period, in the same format as the |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large data sets. |
This wrapper applies kronos(), the main workhorse function in the 'kronos' package. It manages the individual functionalities of 'kronos', including rhythmicity analysis and differential rhythmicity.
fw_kronos( x, formula, metadata, time = NULL, period = 24, verbose = FALSE, pairwise = FALSE )fw_kronos( x, formula, metadata, time = NULL, period = 24, verbose = FALSE, pairwise = FALSE )
x |
Input data. A table with rows being features and columns being samples |
formula |
A formula. Use the |
metadata |
A metadata table, with rows being samples and columns being metadata entries |
time |
A string. Should be the column name containing the time values. |
period |
A numeric. The length of a period, in the same format as the |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large datasets. |
pairwise |
A boolean. Toggles whether to perform pairwise ANOVAs as a TukeyHSD-like post-hoc. |
A list of kronosOut S4 objects containing coefficients and all operations.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
Based on 'cosinor' and 'limorhyde' packages
get_cos_sine(data, period, colnamePrefix = NULL)get_cos_sine(data, period, colnamePrefix = NULL)
data |
input data |
period |
A numeric. The length of a period, in the same format as the |
colnamePrefix |
A character string. Typically the name of the "Time" variable. |
Extracts time from the formula and from the time argument. Also handles inconsistencies.
get_vars(formula, time, data, verbose = verbose)get_vars(formula, time, data, verbose = verbose)
formula |
A formula. Use the |
time |
A string. Should be the column name containing the time values. |
data |
input data |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large data sets. |
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos(kronosOut, target)getKronos(kronosOut, target)
kronosOut |
a |
target |
the specific entry of the |
A data.frame of results.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_fit(kronosOut)getKronos_fit(kronosOut)
kronosOut |
a |
The model fit used.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_groupwise(kronosOut)getKronos_groupwise(kronosOut)
kronosOut |
a |
Rhythmicity parameters per group.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_input(kronosOut)getKronos_input(kronosOut)
kronosOut |
a |
The data used as input for the model.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_pairwise(kronosOut)getKronos_pairwise(kronosOut)
kronosOut |
a |
Pairwise comparisons between groups.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_pairwise_p(kronosOut)getKronos_pairwise_p(kronosOut)
kronosOut |
a |
ANOVA-like adjusted p-values for how each factor interacts with time.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_params(kronosOut)getKronos_params(kronosOut)
kronosOut |
a |
The names and values of additional circadian model parameters, mostly for plotting purposes.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
KronosOut ObjectThese functions provides a unified wrapper to retrieve results
from a kronosOut object.
getKronos_trace(kronosOut)getKronos_trace(kronosOut)
kronosOut |
a |
The traces per group for plotting.
data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
Wrapper around 'ggplot2' to make circadian circleplots.
gg_kronos_acrogram(kronosOutList)gg_kronos_acrogram(kronosOutList)
kronosOutList |
A list of KronosOut output objects from the main |
a 'ggplot2' compatible object.
#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
Wrapper around 'ggplot2' to make circadian circleplots.
gg_kronos_circle(kronosOut)gg_kronos_circle(kronosOut)
kronosOut |
an output object from the main |
a 'ggplot2' compatible object.
#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
Wrapper around 'ggplot2' to make figures with a sinusoid trace.
gg_kronos_sinusoid(kronosOut, fill = "unique_group")gg_kronos_sinusoid(kronosOut, fill = "unique_group")
kronosOut |
an output object from the main |
fill |
The name of the variable that should be used to mark different groups. In the case of a single group, leave empty. |
a 'ggplot2' compatible object.
#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
Snippet of example data to demonstrate the functionality of 'kronos' between and among three different groups
groupdatagroupdata
A long format data.frame object with three columns, and 94 rows, samples.
Dummy data modified from PCR analysis of mouse ileum over the day to demonstrate rhythmicity between multiple groups.
This is the main workhorse function in the 'kronos' package. It manages the individual functionalities of 'kronos', including rhythmicity analysis and differential rhythmicity.
kronos( formula, data, time = NULL, period = 24, verbose = TRUE, pairwise = TRUE )kronos( formula, data, time = NULL, period = 24, verbose = TRUE, pairwise = TRUE )
formula |
A formula. Use the |
data |
input data |
time |
A string. Should be the column name containing the time values. |
period |
A numeric. The length of a period, in the same format as the |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large data sets. |
pairwise |
A boolean. Toggles whether to perform pairwise ANOVAs as a TukeyHSD-like post-hoc. |
A kronosOut S4 object containing coefficients and all operations.
#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in Kronos library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
Compute p-values from full fit.
kronos_anova(fit, time)kronos_anova(fit, time)
fit |
A lm model fit. |
time |
A string. Should be the column name containing the time values. |
Generate data needed to plot cosinor trace line.
kronos_predict(fit, period, time, factors, verbose = verbose)kronos_predict(fit, period, time, factors, verbose = verbose)
fit |
A model fit |
period |
A numeric. The length of a period, in the same format as the |
time |
A string. Should be the column name containing the time values. |
factors |
A vector. The names of the independent variables. |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large data sets. |
KronosOut Objects to publication ready table.These functions provides a unified wrapper to retrieve results
from a list of kronosOut objects.
kronosListToTable(kronos_list, padjust = TRUE)kronosListToTable(kronos_list, padjust = TRUE)
kronos_list |
a list of preferrably named |
padjust |
a boolean. Toggles FDR using Benjamini and Hochbergs procedure. |
A table with circadian output stats per group per feature.
#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)#Load prepared data stored in 'kronos' library data("kronos_demo") output <- kronos(formula = Variable_1 ~ time(Timepoint), data = onevariable, period = 24, verbose = TRUE, pairwise = FALSE) #Extracting data from the output object: getKronos_fit(output) getKronos_trace(output) getKronos_groupwise(output) #Plotting: gg_kronos_circle(output) gg_kronos_sinusoid(output) #For high-dimensional data, use fw_kronos: out_list = fw_kronos(x = bigdata[1:50,], formula = ~ Group + time(Timepoint), metadata = bigmeta, period = 24, verbose = FALSE, pairwise = TRUE) #Extracting data from the output object: kronosListToTable(out_list) #Plotting: gg_kronos_acrogram(out_list)
kronosOut is the main output container of the main 'kronos' functions.
inputA data.frame with the data that was fed to the main workhorse function as 'x'
fitAn lm fit for the entire model for the purpose of assessing differential rhytmicity.
to_plotA data.frame with the traces required to plot individual sinusoid curves
ind_fitA data.frame with the parameters from individual rhythmic model fits.
pairwise_tA data.frame with the p.values for pairwise comparisons, if applicable.
Snippet of example data to demonstrate the functionality of 'kronos' in the most simple scenario.
onevariableonevariable
A long format data.frame object with three columns, and 31 rows, samples.
Dummy data modified from metagenomic analysis of mouse caecal contents over the day to demonstrate rhythmicity within one group.
Fit cosinor model for subset of data. Called by main 'kronos' function.
pairwise_cosinor_model(data, formula, time, verbose)pairwise_cosinor_model(data, formula, time, verbose)
data |
input data |
formula |
A formula. Use the |
time |
A string. Should be the column name containing the time values. |
verbose |
A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large data sets. |
kronosOut object.method to print kronosOut object by calling show.
Since kronosOut objects are typically unwieldy, also gives some tips on how to handle it.
## S4 method for signature 'kronosOut' show(object)## S4 method for signature 'kronosOut' show(object)
object |
An |
Does not return anything, for efficiency reasons. The obvious side effect is output to the terminal.
Snippet of example data to demonstrate the functionality of 'kronos' in the two-factor design scenario.
twowaydatatwowaydata
A wide format data.frame object with nine columns, and 150 rows, samples.
Dummy data modified from metagenomic analysis of mouse caecal contents over the day to demonstrate rhythmicity within one group.