| Title: | Identification of Periodically Expressed Genes |
|---|---|
| Description: | The GeneCycle package implements the approaches of Wichert et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from gene expression time series data. |
| Authors: | Miika Ahdesmaki, Konstantinos Fokianos, and Korbinian Strimmer. |
| Maintainer: | Miika Ahdesmaki <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.5 |
| Built: | 2024-12-18 06:34:36 UTC |
| Source: | CRAN |
avgp calculates and plots the average periodogram as described in
Wichert, Fokianos and Strimmer (2004).
avgp(x, title = deparse(substitute(x)), plot = TRUE, angular = FALSE, ...)avgp(x, title = deparse(substitute(x)), plot = TRUE, angular = FALSE, ...)
x |
multiple (genetic) time series data. Each column of this matrix corresponds to a separate variable/time series |
title |
name of the data set (default is the name of the data object) |
plot |
plot the average periodogram? |
angular |
convert frequencies to angular frequencies? |
... |
arguments passed to |
The average periodogram is simply the frequency-wise average of the spectral density (as estimated
by the Fourier transform) over all times series.
To calculate the average periodogram the function periodogram
is used. See Wichert, Fokianos and Strimmer (2004)
for more details.
A list object with the following components:
freq |
A vector with the discrete Fourier frequencies (see |
avg.spec |
A vector with the average power spectral density at each frequency. |
title |
Name of the data set underlying the average periodogram. |
The result is returned invisibly if plot is true.
Konstantinos Fokianos and Korbinian Strimmer (https://www.strimmerlab.org/).
Wichert, S., Fokianos, K., and Strimmer, K. (2004). Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20:5-20.
# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and how many genes? dim(caulobacter) # average periodogram avgp.caulobacter <- avgp(caulobacter, "Caulobacter") avgp.caulobacter # just compute and don't plot avgp(caulobacter, "Caulobacter", plot=FALSE)# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and how many genes? dim(caulobacter) # average periodogram avgp.caulobacter <- avgp(caulobacter, "Caulobacter") avgp.caulobacter # just compute and don't plot avgp(caulobacter, "Caulobacter", plot=FALSE)
This data set describes the temporal expression of 1444 genes (open reading frames) in the cell cycle of the bacterium Caulobacter crescentus.
data(caulobacter)data(caulobacter)
caulobacter is a longitudinal object
containing the data from the Laub et al. (2000) experiment.
Essentially, this is a matrix with with 1444 columns (=genes)
and 11 rows (=time points)
This data is described in Laub et al. (2000).
Laub, M.T., McAdams, H.H., Feldblyum, Fraser, C.M., and Shapiro, L. (2000) Global analysis of the genetic network controlling a bacterial cell cycle. Science, 290, 2144–1248.
# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) is.longitudinal(caulobacter) # how many samples and how many genes? dim(caulobacter) summary(caulobacter) get.time.repeats(caulobacter) # plot first nine time series plot(caulobacter, 1:9)# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) is.longitudinal(caulobacter) # how many samples and how many genes? dim(caulobacter) summary(caulobacter) get.time.repeats(caulobacter) # plot first nine time series plot(caulobacter, 1:9)
dominant.freqs returns the m dominant frequencies (highest peaks)
in each of the periodogram computed for the individual time series.
dominant.freqs(x, m=1, ...)dominant.freqs(x, m=1, ...)
x |
multivariate (genetic) time series (each column of this matrix corresponds to a separate variable/time series), or a vector with a single time series |
m |
number of dominant frequences |
... |
arguments passed to |
A matrix (or vector, if only 1 time series is considered) with the dominant frequencies. In a matrix, each column corresponds to one time series.
Konstantinos Fokianos and Korbinian Strimmer (https://www.strimmerlab.org/).
# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and how many genes? dim(caulobacter) # first three dominant frequencies for each gene dominant.freqs(caulobacter, 3) # first four dominant frequencies for gene no. 1000 dominant.freqs(caulobacter[,1000], 4)# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and how many genes? dim(caulobacter) # first three dominant frequencies for each gene dominant.freqs(caulobacter, 3) # first four dominant frequencies for gene no. 1000 dominant.freqs(caulobacter[,1000], 4)
fisher.g.test calculates the p-value(s) according to Fisher's
exact g test for one or more time series. This test is useful to detect hidden
periodicities of unknown frequency in a data set. For an application to
microarray data see Wichert, Fokianos, and Strimmer (2004).
fisher.g.test(x, ...)fisher.g.test(x, ...)
x |
vector or matrix with time series data (one time series per column). |
... |
arguments passed to |
Fisher (1929) devised an exact procedure to test the null hypothesis of Gaussian
white noise against the alternative of an added deterministic periodic component
of unspecified frequency. The basic idea behind the test is to reject the
null hypothesis if the periodogram contains a value significantly larger
than the average value (cf. Brockwell and Davis, 1991).
This test is useful in the context of microarray genetic time series
analysis as a gene selection method - see Wichert, Fokianos and Strimmer (2004)
for more details. Note that in the special case of a constant time series
the p-value returned by fisher.g.test is
exactly 1 (i.e. the null hypothesis is not rejected).
A vector of p-values (one for each time series). Multiple testing
may then be done using the the false discover rate approach
(function fdrtool).
Konstantinos Fokianos and Korbinian Strimmer (https://www.strimmerlab.org/).
Fisher, R.A. (1929). Tests of significance in harmonic analysis. Proc. Roy. Soc. A, 125, 54–59.
Brockwell, P.J., and Davis, R.A. (1991). Time Series: Theory and Methods (2nd ed). Springer Verlag. (the g-test is discussed in section 10.2).
Wichert, S., Fokianos, K., and Strimmer, K. (2004). Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20:5-20.
# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and and how many genes? dim(caulobacter) # p-values from Fisher's g test pval.caulobacter <- fisher.g.test(caulobacter) pval.caulobacter # compute Fdr and fdr values fdr.out <- fdrtool(pval.caulobacter, statistic="pvalue") # how many significant? sum(fdr.out$qval < 0.05) # tail area-based Fdr sum(fdr.out$lfdr < 0.2) # density-based local fdr# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and and how many genes? dim(caulobacter) # p-values from Fisher's g test pval.caulobacter <- fisher.g.test(caulobacter) pval.caulobacter # compute Fdr and fdr values fdr.out <- fdrtool(pval.caulobacter, statistic="pvalue") # how many significant? sum(fdr.out$qval < 0.05) # tail area-based Fdr sum(fdr.out$lfdr < 0.2) # density-based local fdr
is.constant is a utility function that
checks whether a time series is constant.
is.constant(x)is.constant(x)
x |
vector or matrix with time series data (one time series per column) |
A vector with a boolean statement (TRUE or FALSE) for each time series.
Korbinian Strimmer (https://www.strimmerlab.org/).
# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # any constant genes? sum(is.constant(caulobacter)) # but here: series.1 <- rep(1, 10) series.2 <- seq(1, 10) is.constant( cbind(series.1, series.2) )# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # any constant genes? sum(is.constant(caulobacter)) # but here: series.1 <- rep(1, 10) series.2 <- seq(1, 10) is.constant( cbind(series.1, series.2) )
periodogram is a wrapper function for spectrum
with some special options set. It
returns the power spectral density, i.e. the
squared modulus of the Fourier coefficient divided by the length
of the series, for multiple time series as well as the corresponding
Fourier frequencies. The frequencies range between
0 and the Nyquist critical frequency fc = frequency(x)/2.
periodogram is used by the functions
avgp and fisher.g.test.
For general periodogram functions
please refer to spectrum.
periodogram(x, method = "builtin")periodogram(x, method = "builtin")
x |
vector or matrix containing the time series data (one time series per column) |
method |
a string that specifies which method should be used to
compute the spectral density: "builtin" employs the function
|
A list object with the following components:
spec |
A vector or matrix with the estimated power spectral densities (one column per time series). |
freq |
A vector with frequencies f ranging from 0 to fc
(if the sampling rate |
Konstantinos Fokianos and Korbinian Strimmer (https://www.strimmerlab.org/).
spectrum, avgp, fisher.g.test.
# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many genes and how many samples? dim(caulobacter) # periodograms of the first 10 genes periodogram(caulobacter[,1:10])# load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many genes and how many samples? dim(caulobacter) # periodograms of the first 10 genes periodogram(caulobacter[,1:10])
robust.g.test calculates the p-value(s) for a robust
nonparametric version of Fisher's g-test (1929). Details
of this approach are described in Ahdesmaki et al. (2005), along with
an extensive discussion of its application to gene expression data.
From GeneCycle 1.1.0 on the robust regression based method published
in Ahdesmaki et al. (2007) is also implemented (using Tukey's biweight
based M-estimation/regression.)
robust.spectrum computes a robust rank-based estimate
of the periodogram/correlogram - see Ahdesmaki et al. (2005)
for details. Alternatively it can also be used (since GeneCycle 1.1.0)
for evaluating the robust regression based spectral estimates,
suitable for processing non-uniformly sampled data (unknown
periodicity time: return spectral estimates, known periodicity
time: return p-values).
robust.g.test(y, index, perm = FALSE, x, noOfPermutations = 300, algorithm=c("rank", "regression"), t) robust.spectrum(x, algorithm = c("rank", "regression"), t, periodicity.time = FALSE, noOfPermutations = 300)robust.g.test(y, index, perm = FALSE, x, noOfPermutations = 300, algorithm=c("rank", "regression"), t) robust.spectrum(x, algorithm = c("rank", "regression"), t, periodicity.time = FALSE, noOfPermutations = 300)
y |
the matrix consisting of the spectral estimates as column vectors |
index |
an index to the spectral estimates (RANK BASED
APPROACH ONLY; for specifying a periodicity time
in the regression approach, see the parameter
periodicity.time) that is to be used in the
testing for periodicity. If |
periodicity.time |
time (same units as in vector |
perm |
if |
x |
a matrix consisting of the time series as column
vectors. In |
noOfPermutations |
number of permutations that are used for each time series (default = 300) |
algorithm |
|
t |
sampling time vector (only for the regression based approach) |
Application of robust.g.test can be very computer intensive,
especially
the production of the distribution of the test statistics may take a
lot
of time. Therefore, this distribution (dependening on the length of
the time series) is stored in an external file to avoid recomputation
(see example below). When applying permutation tests no external file
is
used but the computation time will always be high.
For the general idea behind the Fisher's g test also see
fisher.g.test which implements an analytic approach for
g-testing.
This is faster but not robust and also assumes Gaussian noise.
Note that when using the regression based approach there will regularly be warnings about the non-convergence of the regression (iteration limit default at 20 cycles in rlm).
robust.g.test returns a list of p-values.
robust.spectrum returns a matrix where the column vectors
correspond
to the spectra corresponding to each time series. As an exception, if
the robust regression
based approach (Ahdesmaki et al. 2007) is used with a known periodicity
time, the function
robust.spectrum returns p-values (computation will take a lot of time
depending on how many
permutations are used per time series and time series length).
Miika Ahdesmaki ([email protected]).
Fisher, R.A. (1929). Tests of significance in harmonic analysis. Proc. Roy. Soc. A, 125, 54–59.
Ahdesmaki, M., Lahdesmaki, H., Pearson, R., Huttunen, H., and Yli-Harja O. (2005). BMC Bioinformatics 6:117. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-6-117
Ahdesmaki, M., Lahdesmaki, H., Gracey, A., Shmulevich, I., and Yli-Harja O. (2007). BMC Bioinformatics 8:233. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-233
## Not run: # load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and and how many genes? dim(caulobacter) # robust, rank-based spectral estimator applied to first 5 genes spe5 = robust.spectrum(caulobacter[,1:5]) # g statistics can be computed from the spectrum (internal use mostly # but can be checked here) ## g.statistic(spe5) # robust p-values, use Monte Carlo simulation (not permutation tests) # to estimate the null hypothesis distribution pval = robust.g.test(spe5) # generates a file with the name "g_pop_length_11.txt" pval = robust.g.test(spe5) # second call: much faster.. pval # robust p-values, now look at index 4 (index can be anything from 1 # (DC-level) to N (length of the time series and highest frequency)) pval = robust.g.test(spe5, 4) # generates a file pval = robust.g.test(spe5, 4) # second call: much faster.. pval # delete the external files unlink("g_pop_length_11.txt") unlink("g_pop_length_11indexed.txt") # # Next let us see how the robust regression based approach can be # applied (Ahdesmaki et al. 2007) # First: Unknown frequencies t=c(0,15,30,45,60,75,90,105,120,135,150) y = robust.spectrum(x=caulobacter[,1:5],algorithm="regression", t=t) pvals = robust.g.test(y = y, perm=TRUE, x=caulobacter[,1:5], noOfPermutations = 50, algorithm = "regression", t=t) pvals # # The following example illustrates how to use the regression based # method if we have prior knowledge about the frequency/period time # of periodicity t = 0:9 # time indices t = t + runif(10)-0.5 # make time indices non-uniform A = 0.5 * matrix(rnorm(50),10,5) # create random time series (no outliers) A[,5]=A[,5]+matrix(sin(0.5*pi*t),10,1) # superimpose a sinusoidal periodicity.time=4 # where to look for periodicity # note that now the function robust.spectrum returns the p-values (in # all other cases it will return spectral estimates): pvals=robust.spectrum(x=A,algorithm="regression", t=t,periodicity.time=periodicity.time, noOfPermutations=50) pvals # 5th p-value is smallish, as expected ## End(Not run)## Not run: # load GeneCycle library library("GeneCycle") # load data set data(caulobacter) # how many samples and and how many genes? dim(caulobacter) # robust, rank-based spectral estimator applied to first 5 genes spe5 = robust.spectrum(caulobacter[,1:5]) # g statistics can be computed from the spectrum (internal use mostly # but can be checked here) ## g.statistic(spe5) # robust p-values, use Monte Carlo simulation (not permutation tests) # to estimate the null hypothesis distribution pval = robust.g.test(spe5) # generates a file with the name "g_pop_length_11.txt" pval = robust.g.test(spe5) # second call: much faster.. pval # robust p-values, now look at index 4 (index can be anything from 1 # (DC-level) to N (length of the time series and highest frequency)) pval = robust.g.test(spe5, 4) # generates a file pval = robust.g.test(spe5, 4) # second call: much faster.. pval # delete the external files unlink("g_pop_length_11.txt") unlink("g_pop_length_11indexed.txt") # # Next let us see how the robust regression based approach can be # applied (Ahdesmaki et al. 2007) # First: Unknown frequencies t=c(0,15,30,45,60,75,90,105,120,135,150) y = robust.spectrum(x=caulobacter[,1:5],algorithm="regression", t=t) pvals = robust.g.test(y = y, perm=TRUE, x=caulobacter[,1:5], noOfPermutations = 50, algorithm = "regression", t=t) pvals # # The following example illustrates how to use the regression based # method if we have prior knowledge about the frequency/period time # of periodicity t = 0:9 # time indices t = t + runif(10)-0.5 # make time indices non-uniform A = 0.5 * matrix(rnorm(50),10,5) # create random time series (no outliers) A[,5]=A[,5]+matrix(sin(0.5*pi*t),10,1) # superimpose a sinusoidal periodicity.time=4 # where to look for periodicity # note that now the function robust.spectrum returns the p-values (in # all other cases it will return spectral estimates): pvals=robust.spectrum(x=A,algorithm="regression", t=t,periodicity.time=periodicity.time, noOfPermutations=50) pvals # 5th p-value is smallish, as expected ## End(Not run)