rain: Detection of rhythmic behavior in time series.
In rain: Rhythmicity Analysis Incorporating Non-parametric Methods
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
rain detects rhythms in time-series using non parametric methods.
It uses an extension of the rank test for Umbrella Alternatives
(Mack & Wolfe, 1981), based on on the Jonckheere-Terpstra test, which tests
whether sets of groups have a trend or not. The Umbrella method extends
this to independent rising and falling sets.
Usage
1
2
3
Arguments
x
numeric array, containing the data. One row per time point,
one column per sample. If more than one replicate is done, see
nr.series for formatting.
deltat
numeric: sampling interval.
period
numeric: Period to search for.
The given period is mapped to the best matching number of measurements.
A set of periods can be defined by period and period.delta.
period.delta
numeric: width of period interval.
A interval of different period-length to evaluate is defined by
period $-$ period.delta and period $+$ period.delta.
In this interval all possible numbers of time points according to the
deltat are tested.
peak.border
vector c(min,max): defines the different form of the
peak. min and max have to be >0 and <1. The concrete interpretation depends
on the chosen method (see Details). (default = c(0.3,0.7))
nr.series
numeric: Number of replicates of the whole time series.
If using nr.series all series have to have the same length.
These multiple time series contained in x must be organized timepoint by
timepoint using the following format
[r1t1, r2t1, r1t2, r2t2, ..., r1tn, r2tn]
where ritj is the i'th repeat of the j'th time-point.
measure.sequence
numeric array: Numbers of replicates for each time
point.
By using 'measure.sequence', irregular time courses may be evaluated.
A value of 0 is possible and handeled correctly. The array determines how
many values are present for each time piont.
The values are ordered in the same format as specified above.
measure.sequence overwrites nr.series if both are set.
method
string ('independent', 'longitudinal'): identify the method
to use (see Details).
na.rm
boolean: calculate individual statistics for time series
containign NAs. The time series of a sample containing NAs is treated
as if the time points with NA are not measured. Using this option increases
calculation time.
adjp.method
string (see mt.rawp2adjp):
select the method wich is used for the multiple testing correction for the
different phases and periods and shapes tested
verbose
status output
Details
The method tests whether a the time course consists of alternating rising
and falling slopes, repeated with a distinct period. The partitions of the
rising part with respect to the whole period are given by
peak.border = c(min, max). The value peak.shape specifies this partition in
the best matching model. The phase is defined as the time point with the
peak. There are two versions of umbrella:
- independent
Multiple periods are interpreted as repeats of one
period.
- logitudinal
The whole time series remains unaffected. Partial slopes
in the beginning and end of the time series are evaluated as shorter
slopes. This method implicitly rejects underlying trends.This should be
used only with longitudinal samples, hat may contain strong trends
Value
An array containing p-values and the description of the best
matching model.
Each row apply to a sample in x.
pVal
The p-Values
phase
The phase of the peak (see Details)
peak.shape
The shape of the curve depending on the method used
(see Details)
period
The period length, same unit as deltat
Author(s)
Paul F. Thaben
References
Mack, G. A., & Wolfe, D. A. (1981). K-Sample Rank Tests for Umbrella
Alternatives.
Journal of the American Statistical Association,
76(373), 175–181.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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21
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30
31
# create a dataset with different noise levels
noise.levels <- c(1, 0.5, 0.2, 0.1, 0.05, 0.02)
period <- 15
testset <- apply(matrix(noise.levels, nrow = 1), 2, function(noise){
timecourse = 1 + 0.4 * cos((1:30) / period * 2 * pi) +
rnorm(30, 0, noise)
})
results <- rain(testset, period=15, deltat=1, method='independent')
plot(-log(results$pVal) ~ noise.levels)
## Not run:
# testing a biological dataset
data(menetRNASeqMouseLiver)
menet.ossc <- rain(t( menetRNASeqMouseLiver ), deltat = 4, period = 24,
nr.series = 2, peak.border = c(0.3, 0.7), verbose=TRUE)
require('lattice')
best <- order(results$pVal)[1:10]
xyplot(as.matrix(menetRNASeqMouseLiver
[best, (0:5 * 2 + rep(c(1, 2), each = 6))])
~rep(0:11 * 4 + 2, each = 10) |rownames(menetRNASeqMouseLiver)[best],
scales = list(y = list(relation = 'free')),
layout = c(2, 5), type = 'b', pch = 16, xlab = 'time',
ylab = 'expression value', cex.lab = 1)
## End(Not run)
Example output
Loading required package: gmp
Attaching package: ‘gmp’
The following objects are masked from ‘package:base’:
%*%, apply, crossprod, matrix, tcrossprod
Loading required package: multtest
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:gmp’:
which.max, which.min
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
calculating distributions (30):***...........................
Done
Evaluating Datasets:
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Done
Loading required package: lattice
rain documentation built on Nov. 8, 2020, 6:56 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) References Examples
Description
rain detects rhythms in time-series using non parametric methods. It uses an extension of the rank test for Umbrella Alternatives (Mack & Wolfe, 1981), based on on the Jonckheere-Terpstra test, which tests whether sets of groups have a trend or not. The Umbrella method extends this to independent rising and falling sets.
Usage
1 2 3 |
Arguments
x |
numeric array, containing the data. One row per time point, one column per sample. If more than one replicate is done, see nr.series for formatting. |
deltat |
numeric: sampling interval. |
period |
numeric: Period to search for. The given period is mapped to the best matching number of measurements. A set of periods can be defined by period and period.delta. |
period.delta |
numeric: width of period interval. A interval of different period-length to evaluate is defined by period $-$ period.delta and period $+$ period.delta. In this interval all possible numbers of time points according to the deltat are tested. |
peak.border |
vector c(min,max): defines the different form of the peak. min and max have to be >0 and <1. The concrete interpretation depends on the chosen method (see Details). (default = c(0.3,0.7)) |
nr.series |
numeric: Number of replicates of the whole time series. If using nr.series all series have to have the same length. These multiple time series contained in x must be organized timepoint by timepoint using the following format [r1t1, r2t1, r1t2, r2t2, ..., r1tn, r2tn] where ritj is the i'th repeat of the j'th time-point. |
measure.sequence |
numeric array: Numbers of replicates for each time point. By using 'measure.sequence', irregular time courses may be evaluated. A value of 0 is possible and handeled correctly. The array determines how many values are present for each time piont. The values are ordered in the same format as specified above. measure.sequence overwrites nr.series if both are set. |
method |
string ('independent', 'longitudinal'): identify the method to use (see Details). |
na.rm |
boolean: calculate individual statistics for time series containign NAs. The time series of a sample containing NAs is treated as if the time points with NA are not measured. Using this option increases calculation time. |
adjp.method |
string (see |
verbose |
status output |
Details
The method tests whether a the time course consists of alternating rising and falling slopes, repeated with a distinct period. The partitions of the rising part with respect to the whole period are given by peak.border = c(min, max). The value peak.shape specifies this partition in the best matching model. The phase is defined as the time point with the peak. There are two versions of umbrella:
- independent
Multiple periods are interpreted as repeats of one period.
- logitudinal
The whole time series remains unaffected. Partial slopes in the beginning and end of the time series are evaluated as shorter slopes. This method implicitly rejects underlying trends.This should be used only with longitudinal samples, hat may contain strong trends
Value
An array containing p-values and the description of the best matching model. Each row apply to a sample in x.
pVal |
The p-Values |
phase |
The phase of the peak (see Details) |
peak.shape |
The shape of the curve depending on the method used (see Details) |
period |
The period length, same unit as deltat |
Author(s)
Paul F. Thaben
References
Mack, G. A., & Wolfe, D. A. (1981). K-Sample Rank Tests for Umbrella Alternatives. Journal of the American Statistical Association, 76(373), 175–181.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # create a dataset with different noise levels
noise.levels <- c(1, 0.5, 0.2, 0.1, 0.05, 0.02)
period <- 15
testset <- apply(matrix(noise.levels, nrow = 1), 2, function(noise){
timecourse = 1 + 0.4 * cos((1:30) / period * 2 * pi) +
rnorm(30, 0, noise)
})
results <- rain(testset, period=15, deltat=1, method='independent')
plot(-log(results$pVal) ~ noise.levels)
## Not run:
# testing a biological dataset
data(menetRNASeqMouseLiver)
menet.ossc <- rain(t( menetRNASeqMouseLiver ), deltat = 4, period = 24,
nr.series = 2, peak.border = c(0.3, 0.7), verbose=TRUE)
require('lattice')
best <- order(results$pVal)[1:10]
xyplot(as.matrix(menetRNASeqMouseLiver
[best, (0:5 * 2 + rep(c(1, 2), each = 6))])
~rep(0:11 * 4 + 2, each = 10) |rownames(menetRNASeqMouseLiver)[best],
scales = list(y = list(relation = 'free')),
layout = c(2, 5), type = 'b', pch = 16, xlab = 'time',
ylab = 'expression value', cex.lab = 1)
## End(Not run)
|
Example output
Loading required package: gmp
Attaching package: ‘gmp’
The following objects are masked from ‘package:base’:
%*%, apply, crossprod, matrix, tcrossprod
Loading required package: multtest
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:gmp’:
which.max, which.min
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
calculating distributions (30):***...........................
Done
Evaluating Datasets:
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Done
Loading required package: lattice
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