meta2d: Detect rhythmic signals from time-series datasets with...
In MetaCycle: Evaluate Periodicity in Large Scale Data
Description
Usage
Arguments
Details
Value
References
Examples
Description
This is a function that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to
detect rhythmic signals from time-series datasets.
Usage
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meta2d(infile, outdir = "metaout", filestyle, timepoints, minper = 20,
maxper = 28, cycMethod = c("ARS", "JTK", "LS"),
analysisStrategy = "auto", outputFile = TRUE,
outIntegration = "both", adjustPhase = "predictedPer",
combinePvalue = "fisher", weightedPerPha = FALSE, ARSmle = "auto",
ARSdefaultPer = 24, outRawData = FALSE, releaseNote = TRUE,
outSymbol = "", parallelize = FALSE, nCores = 1, inDF = NULL)
Arguments
infile
a character string. The name of input file containing
time-series data.
outdir
a character string. The name of directory used to store
output files.
filestyle
a character vector(length 1 or 3). The data format of
input file, must be "txt", or "csv", or a character vector
containing field separator character(sep), quoting character
(quote), and the character used for decimal points(dec,
for details see read.table).
timepoints
a numeric vector corresponding to sampling time points
of input time-series data; if sampling time points are in the first line
of input file, it could be set as a character sting-"Line1" or "line1".
minper
a numeric value. The minimum period length of interested
rhythms. The default is 20 for circadian rhythms.
maxper
a numeric value. The maximum period length of interested
rhythms. The default is 28 for circadian rhythms.
cycMethod
a character vector(length 1 or 2 or 3). User-defined
methods for detecting rhythmic signals, must be selected as any one, any
two or all three methods(default) from "ARS"(ARSER),
"JTK"(JTK_CYCLE) and "LS"(Lomb-Scargle).
analysisStrategy
a character string. The strategy used to select
proper methods from cycMethod for analyzing input time-series
data, must be "auto"(default), or "selfUSE". See
Details part for more information.
outputFile
logical. If TRUE, analysis results will be wrote
in the output files. If FALSE, analysis results will be returned
as an R list.
outIntegration
a character string. This parameter controls what
kinds of analysis results will be outputted, must be one of "both"
(default), "onlyIntegration"(only output integration file), or
"noIntegration"(not output integration file).
adjustPhase
a character string. The method used to adjust original
phase calculated by each method in integration file, must be one of
"predictedPer"(adjust phase with predicted period length) or
"notAdjusted"(not adjust phase).
combinePvalue
a character string. The method used to integrate
multiple p-values, must be one of "bonferroni"(Bonferroni
correction), or "fisher"(Fisher's method).
weightedPerPha
logical. If TRUE, weighted scores based on
p-value given by each method will be used to calculate the integrated
period length and phase.
ARSmle
a character string. The strategy of using MLE method in
ar fit of "ARS", must be one of
"auto"(use MLE depending the number of time points), "mle"
(always use MLE), or "nomle"(never use MLE).
ARSdefaultPer
a numeric value. The expected period length of
interested rhythm, which is a necessary parameter for ARS. The
default is 24(for circadian rhythms). Set it to another proper
numeric value for other rhythms.
outRawData
logical. If TRUE, raw time-series data will be
added in the output files.
releaseNote
logical. If TRUE, reminding or warning notes
during the analysis will be released on the screen.
outSymbol
a character string. A common prefix exists in the names of
output files.
parallelize
logical. If TRUE, computation will be done in paralleL
Doesn't work in windows machine.
nCores
a integer. Bigger or equal to one, number of cores to use.
inDF
data.frame. If !is.null(inDF) and timepoints is a numeric meta2d will use this data.frame instead of loading from infile.
Details
ARSER(Yang, 2010),
JTK_CYCLE(
Hughes, 2010), and
Lomb-Scargle(Glynn, 2006) are three popular methods of detecting
rhythmic signals. ARS can not analyze unevenly sampled datasets,
or evenly sampled datasets but with missing values, or with replicate
samples, or with non-integer sampling interval. JTK is not
suitable to analyze unevenly sampled datasets or evenly sampled datasets
but with non-integer sampling interval. If set analysisStrategy
as "auto"(default), meta2d will automatically select
proper method from cycMethod for each input dataset. If the user
clearly know that the dataset could be analyzed by each method defined
by cycMethod and do not hope to output integrated values,
analysisStrategy can be set as "selfUSE".
ARS used here is translated from its python version which always
uses "yule-walker", "burg", and "mle" methods(see
ar) to fit autoregressive models to time-series
data. Fitting by "mle" will be very slow for datasets
with many time points. If ARSmle = "auto" is used,
meta2d will only include "mle" when number of time points
is smaller than 24. In addition, one evaluation work(Wu, 2014) indicates
that ARS shows relative high false positive rate in analyzing
high-resolution datasets (1h/2days and 2h/2days). JTK(version 3)
used here is the latest version, which improves its p-value calculation
in analyzing datasets with missing values.
The power of detecting rhythmic signals for an algorithm is associated
with the nature of data and interested periodic pattern(Deckard, 2013),
which indicates that integrating analysis results from multiple methods
may be helpful to rhythmic detection. For integrating p-values,
Bonferroni correction("bonferroni") and Fisher's method(
"fisher") (Fisher, 1925; implementation code from MADAM)
could be selected, and "bonferroni" is usually more conservative
than "fisher". The integrated period is arithmetic mean of
multiple periods. For integrating phase, meta2d takes use of
mean of circular quantities. Integrated period and phase is further
used to calculate the baseline value and amplitude through fitting a
constructed periodic model.
Phases given by JTK and LS need to be adjusted with their
predicted period (adjustedPhase = "predictedPer") before
integration. If adjustedPhas = "notAdjusted" is selected, no
integrated phase will be calculated. If set weightedPerPha as
TRUE, weighted scores will be used in averaging periods and
phases. Weighted scores for one method are based on all its reported
p-values, which means a weighted score assigned to any one profile will
be affected by all other profiles. It is always a problem of averaging
phases with quite different period lengths(eg. averaging two phases
with 16-hours' and 30-hours' period length). Currently, setting
minper, maxper and ARSdefaultPer to a same value
may be the only way of completely eliminating such problem.
This function is originally aimed to analyze large scale periodic data(
eg. circadian transcriptome data) without individual information.
Please pay attention to data format of input file(see Examples
part). Except the first column and first row, others are time-series
experimental values(setting missing values as NA).
Value
meta2d will write analysis results in different files under
outdir if set outputFile = TRUE. Files named with
"ARSresult", "JTKresult" and "LSreult" store analysis results from
ARS, JTK and LS respectively. The file named with
"meta2d" is the integration file, and it stores integrated values in
columns with a common name tag-"meta2d". The integration file also
contains p-value, FDR value, period, phase(adjusted phase if
adjustedPhase = "predictedPer") and amplitude values calculated
by each method.
If outputFile = FALSE is selected, meta2d will return a
list containing the following components:
ARS analysis results from ARS method
JTK analysis results from JTK method
LS analysis results from LS method
meta the integrated analysis results as mentioned above
References
Yang R. and Su Z. (2010). Analyzing circadian expression data by
harmonic regression based on autoregressive spectral estimation.
Bioinformatics, 26(12), i168–i174.
Hughes M. E., Hogenesch J. B. and Kornacker K. (2010). JTK_CYCLE: an
efficient nonparametric algorithm for detecting rhythmic components in
genome-scale data sets. Journal of Biological Rhythms,
25(5), 372–380.
Glynn E. F., Chen J. and Mushegian A. R. (2006). Detecting periodic
patterns in unevenly spaced gene expression time series using
Lomb-Scargle periodograms. Bioinformatics, 22(3),
310–316.
Wu G., Zhu J., Yu J., Zhou L., Huang J. Z. and Zhang Z. (2014). Evaluation
of five methods for genome-wide circadian gene identification.
Journal of Biological Rhythms, 29(4), 231–242.
Deckard A., Anafi R. C., Hogenesch J. B., Haase S.B. and Harer J. (2013).
Design and analysis of large-scale biological rhythm studies:
a comparison of algorithms for detecting periodic signals in biological
data. Bioinformatics, 29(24), 3174–3180.
Fisher, R.A. (1925). Statistical methods for research workers.
Oliver and Boyd (Edinburgh).
Kugler K. G., Mueller L.A. and Graber A. (2010). MADAM - an open source
toolbox for meta-analysis. Source Code for Biology and Medicine,
5, 3.
Examples
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# write 'cycSimu4h2d', 'cycMouseLiverRNA' and 'cycYeastCycle' into three
# 'csv' files
write.csv(cycSimu4h2d, file="cycSimu4h2d.csv", row.names=FALSE)
write.csv(cycMouseLiverRNA, file="cycMouseLiverRNA.csv", row.names=FALSE)
write.csv(cycYeastCycle, file="cycYeastCycle.csv", row.names=FALSE)
# write 'cycMouseLiverProtein' into a 'txt' file
write.table(cycMouseLiverProtein, file="cycMouseLiverProtein.txt",
sep="\t", quote=FALSE, row.names=FALSE)
# analyze 'cycMouseLiverRNA.csv' with JTK_CYCLE
# this is masked for keeping the total running time within 10s required by CRAN check
# meta2d(infile="cycMouseLiverRNA.csv", filestyle="csv", outdir="example",
# timepoints=18:65, cycMethod="JTK", outIntegration="noIntegration")
# analyze 'cycMouseLiverProtein.txt' with JTK_CYCLE and Lomb-Scargle
meta2d(infile="cycMouseLiverProtein.txt", filestyle="txt",
outdir="example", timepoints=rep(seq(0, 45, by=3), each=3),
cycMethod=c("JTK","LS"), outIntegration="noIntegration")
# analyze 'cycSimu4h2d.csv' with ARSER, JTK_CYCLE and Lomb-Scargle and
# output integration file with analysis results from each method
meta2d(infile="cycSimu4h2d.csv", filestyle="csv", outdir="example",
timepoints="Line1")
# analyze 'cycYeastCycle.csv' with ARSER, JTK_CYCLE and Lomb-Scargle to
# detect transcripts associated with cell cycle, and only output
# integration file
meta2d(infile="cycYeastCycle.csv",filestyle="csv", outdir="example",
minper=80, maxper=96, timepoints=seq(2, 162, by=16),
outIntegration="onlyIntegration", ARSdefaultPer=85,
outRawData=TRUE)
# return analysis results instead of output them into files
cyc <- meta2d(infile="cycYeastCycle.csv",filestyle="csv",
minper=80, maxper=96, timepoints=seq(2, 162, by=16),
outputFile=FALSE, ARSdefaultPer=85, outRawData=TRUE)
head(cyc$ARS)
head(cyc$JTK)
head(cyc$LS)
head(cyc$meta)
Example output
The JTK is in process from 11:48:35 08-07-2019
Warning: the input 'minper' is not suitable for JTK, it was reset as 21
Warning: the input 'maxper' is not suitable for JTK, it was reset as 27
The analysis by JTK is finished at 11:48:38 08-07-2019
The LS is in process from 11:48:38 08-07-2019
The analysis by LS is finished at 11:48:38 08-07-2019
DONE! The analysis about ' cycMouseLiverProtein.txt ' has been finished.
user.self sys.self elapsed user.child sys.child
"Time used:" "2.475" "0.02" "2.51" "0" "0"
The ARS is in process from 11:48:38 08-07-2019
The analysis by ARS is finished at 11:48:39 08-07-2019
The JTK is in process from 11:48:39 08-07-2019
The analysis by JTK is finished at 11:48:39 08-07-2019
The LS is in process from 11:48:39 08-07-2019
The analysis by LS is finished at 11:48:39 08-07-2019
DONE! The analysis about ' cycSimu4h2d.csv ' has been finished.
user.self sys.self
"Time used:" "1.006" "0.00800000000000001"
elapsed user.child sys.child
"1.027" "0" "0"
The ARS is in process from 11:48:39 08-07-2019
The analysis by ARS is finished at 11:48:39 08-07-2019
The JTK is in process from 11:48:39 08-07-2019
The analysis by JTK is finished at 11:48:39 08-07-2019
The LS is in process from 11:48:39 08-07-2019
The analysis by LS is finished at 11:48:39 08-07-2019
DONE! The analysis about ' cycYeastCycle.csv ' has been finished.
user.self sys.self
"Time used:" "0.335" "0.00399999999999999"
elapsed user.child sys.child
"0.339" "0" "0"
The ARS is in process from 11:48:40 08-07-2019
The analysis by ARS is finished at 11:48:40 08-07-2019
The JTK is in process from 11:48:40 08-07-2019
The analysis by JTK is finished at 11:48:40 08-07-2019
The LS is in process from 11:48:40 08-07-2019
The analysis by LS is finished at 11:48:40 08-07-2019
DONE! The analysis about ' cycYeastCycle.csv ' has been finished.
user.self sys.self elapsed
"Time used:" "0.225000000000001" "0" "0.225"
user.child sys.child
"0" "0"
CycID filter_type ar_method period_number period
1 YLR072W_1777968_at 0 mle 1 87.7362637362637
2 YBR275C_1771064_at 0 yule-walker 1 89.7078651685393
3 YCL024W_1779753_at 1 yule-walker 1 86.3135135135135
4 YGR177C_1771190_at 0 default 1 85
5 YEL069C_1775358_at 1 mle 1 89.7078651685393
6 YLR125W_1769482_at 0 yule-walker 1 91.7701149425287
amplitude phase mean R_square R2_adjust coef_var
1 94.5793636593827 61.4128785798212 996.0288 0.6673638 0.5842048 0.10218618
2 101.952188023999 7.15399100874371 567.2834 0.5396428 0.4245535 0.26405373
3 452.102904641171 1.57365902889558 1187.0017 0.5867804 0.4834755 0.40121892
4 150.64941279979 49.6491302047991 1842.6738 0.7439124 0.6798906 0.07935373
5 9.59969195724396 82.549797941594 199.5306 0.8345810 0.7932263 0.03683684
6 26.1892698123898 6.54455912941449 425.5334 0.6511591 0.5639488 0.06605052
pvalue fdr_BH recov_2min recov_18min recov_34min recov_50min
1 0.012242720 0.02155702 869.8609 797.2953 942.6339 1069.1362
2 0.044913802 0.04592298 879.3746 849.6005 518.1937 477.9088
3 0.029155702 0.03644463 2448.7403 1560.6675 745.9879 599.9138
4 0.004300846 0.01433615 1664.7032 1605.5524 1817.0391 2066.6323
5 0.000748758 0.00748758 211.1860 192.3690 191.4977 194.5959
6 0.014808455 0.02155702 487.0184 454.4303 405.9583 408.3755
recov_66min recov_82min recov_98min recov_114min recov_130min recov_146min
1 1124.1407 1070.2156 903.6072 960.5258 1049.3727 1080.8489
2 501.3832 557.3082 589.2522 563.5682 465.7285 415.0808
3 1061.5011 1398.0423 1247.4566 1059.1671 967.4813 902.5316
4 1915.4511 1675.6887 1724.0113 1942.5318 1989.9812 1955.0953
5 204.7598 208.4046 203.6591 196.5309 192.7436 191.1770
6 402.5615 422.6579 455.0661 439.8246 408.4970 399.6859
recov_162min
1 1088.6792
2 422.7184
3 1065.5292
4 1912.7260
5 207.9130
6 396.7915
CycID BH.Q ADJ.P PER LAG AMP recov_2min
1 YLR072W_1777968_at 0.009169698 0.001455026 96 64 141.67214 869.8609
2 YBR275C_1771064_at 0.009169698 0.003667879 96 8 234.34002 879.3746
3 YCL024W_1779753_at 0.013924713 0.008354828 96 0 576.06547 2448.7403
4 YGR177C_1771190_at 0.042711778 0.034169422 80 56 177.30558 1664.7032
5 YEL069C_1775358_at 0.069754311 0.062778880 96 88 11.33888 211.1860
6 YLR125W_1769482_at 0.013924713 0.008354828 96 8 34.27492 487.0184
recov_18min recov_34min recov_50min recov_66min recov_82min recov_98min
1 797.2953 942.6339 1069.1362 1124.1407 1070.2156 903.6072
2 849.6005 518.1937 477.9088 501.3832 557.3082 589.2522
3 1560.6675 745.9879 599.9138 1061.5011 1398.0423 1247.4566
4 1605.5524 1817.0391 2066.6323 1915.4511 1675.6887 1724.0113
5 192.3690 191.4977 194.5959 204.7598 208.4046 203.6591
6 454.4303 405.9583 408.3755 402.5615 422.6579 455.0661
recov_114min recov_130min recov_146min recov_162min
1 960.5258 1049.3727 1080.8489 1088.6792
2 563.5682 465.7285 415.0808 422.7184
3 1059.1671 967.4813 902.5316 1065.5292
4 1942.5318 1989.9812 1955.0953 1912.7260
5 196.5309 192.7436 191.1770 207.9130
6 439.8246 408.4970 399.6859 396.7915
CycID PhaseShift PhaseShiftHeight PeakIndex PeakSPD Period
1 YLR072W_1777968_at 66.20825 1097.1808 16 4.082555 89.73913
2 YBR275C_1771064_at 97.10063 576.7867 8 3.056345 92.97297
3 YCL024W_1779753_at 92.04248 1273.4348 13 3.125249 90.92511
4 YGR177C_1771190_at 51.04197 1941.0634 30 3.876712 84.59016
5 YEL069C_1775358_at 82.30036 206.3205 6 4.300112 93.81818
6 YLR125W_1769482_at 99.51891 441.0674 1 4.096129 96.00000
p N Nindependent Nyquist BH.Q recov_2min recov_18min
1 0.09701453 11 6 0.034375 0.2360983 869.8609 797.2953
2 0.25114957 11 6 0.034375 0.2790551 879.3746 849.6005
3 0.23625389 11 6 0.034375 0.2790551 2448.7403 1560.6675
4 0.11804913 11 6 0.034375 0.2360983 1664.7032 1605.5524
5 0.07869067 11 6 0.034375 0.2360983 211.1860 192.3690
6 0.09576088 11 6 0.034375 0.2360983 487.0184 454.4303
recov_34min recov_50min recov_66min recov_82min recov_98min recov_114min
1 942.6339 1069.1362 1124.1407 1070.2156 903.6072 960.5258
2 518.1937 477.9088 501.3832 557.3082 589.2522 563.5682
3 745.9879 599.9138 1061.5011 1398.0423 1247.4566 1059.1671
4 1817.0391 2066.6323 1915.4511 1675.6887 1724.0113 1942.5318
5 191.4977 194.5959 204.7598 208.4046 203.6591 196.5309
6 405.9583 408.3755 402.5615 422.6579 455.0661 439.8246
recov_130min recov_146min recov_162min
1 1049.3727 1080.8489 1088.6792
2 465.7285 415.0808 422.7184
3 967.4813 902.5316 1065.5292
4 1989.9812 1955.0953 1912.7260
5 192.7436 191.1770 207.9130
6 408.4970 399.6859 396.7915
CycID ARS_pvalue ARS_BH.Q ARS_period ARS_adjphase
1 YLR072W_1777968_at 0.012242720 0.02155702 87.73626 61.412879
2 YBR275C_1771064_at 0.044913802 0.04592298 89.70787 7.153991
3 YCL024W_1779753_at 0.029155702 0.03644463 86.31351 1.573659
4 YGR177C_1771190_at 0.004300846 0.01433615 85.00000 49.649130
5 YEL069C_1775358_at 0.000748758 0.00748758 89.70787 82.549798
6 YLR125W_1769482_at 0.014808455 0.02155702 91.77011 6.544559
ARS_amplitude JTK_pvalue JTK_BH.Q JTK_period JTK_adjphase JTK_amplitude
1 94.579364 0.001455026 0.009169698 96 66 141.67214
2 101.952188 0.003667879 0.009169698 96 10 234.34002
3 452.102905 0.008354828 0.013924713 96 2 576.06547
4 150.649413 0.034169422 0.042711778 80 58 177.30558
5 9.599692 0.062778880 0.069754311 96 90 11.33888
6 26.189270 0.008354828 0.013924713 96 10 34.27492
LS_pvalue LS_BH.Q LS_period LS_adjphase LS_amplitude meta2d_pvalue
1 0.09701453 0.2360983 89.73913 66.208254 1097.1808 0.0001767816
2 0.25114957 0.2790551 92.97297 4.127657 576.7867 0.0025662522
3 0.23625389 0.2790551 90.92511 1.117369 1273.4348 0.0033620108
4 0.11804913 0.2360983 84.59016 51.041968 1941.0634 0.0012498505
5 0.07869067 0.2360983 93.81818 82.300358 206.3205 0.0003392889
6 0.09576088 0.2360983 96.00000 3.518911 441.0674 0.0009084778
meta2d_BH.Q meta2d_period meta2d_phase meta2d_Base meta2d_AMP meta2d_rAMP
1 0.001696444 91.15846 64.575261 997.4851 107.517320 0.10778839
2 0.003666075 92.89361 7.071532 569.6488 118.435669 0.20790997
3 0.003876404 91.07954 1.559115 1200.1148 483.025552 0.40248278
4 0.002499701 83.19672 52.945684 1848.1603 165.485553 0.08954069
5 0.001696444 93.17535 84.947692 200.1210 9.952018 0.04973000
6 0.002271194 94.59004 6.689105 426.2096 31.079671 0.07292110
recov_2min recov_18min recov_34min recov_50min recov_66min recov_82min
1 869.8609 797.2953 942.6339 1069.1362 1124.1407 1070.2156
2 879.3746 849.6005 518.1937 477.9088 501.3832 557.3082
3 2448.7403 1560.6675 745.9879 599.9138 1061.5011 1398.0423
4 1664.7032 1605.5524 1817.0391 2066.6323 1915.4511 1675.6887
5 211.1860 192.3690 191.4977 194.5959 204.7598 208.4046
6 487.0184 454.4303 405.9583 408.3755 402.5615 422.6579
recov_98min recov_114min recov_130min recov_146min recov_162min
1 903.6072 960.5258 1049.3727 1080.8489 1088.6792
2 589.2522 563.5682 465.7285 415.0808 422.7184
3 1247.4566 1059.1671 967.4813 902.5316 1065.5292
4 1724.0113 1942.5318 1989.9812 1955.0953 1912.7260
5 203.6591 196.5309 192.7436 191.1770 207.9130
6 455.0661 439.8246 408.4970 399.6859 396.7915
MetaCycle documentation built on May 2, 2019, 9:14 a.m.
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Description Usage Arguments Details Value References Examples
Description
This is a function that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to detect rhythmic signals from time-series datasets.
Usage
1 2 3 4 5 6 7 | meta2d(infile, outdir = "metaout", filestyle, timepoints, minper = 20,
maxper = 28, cycMethod = c("ARS", "JTK", "LS"),
analysisStrategy = "auto", outputFile = TRUE,
outIntegration = "both", adjustPhase = "predictedPer",
combinePvalue = "fisher", weightedPerPha = FALSE, ARSmle = "auto",
ARSdefaultPer = 24, outRawData = FALSE, releaseNote = TRUE,
outSymbol = "", parallelize = FALSE, nCores = 1, inDF = NULL)
|
Arguments
infile |
a character string. The name of input file containing time-series data. |
outdir |
a character string. The name of directory used to store output files. |
filestyle |
a character vector(length 1 or 3). The data format of
input file, must be |
timepoints |
a numeric vector corresponding to sampling time points of input time-series data; if sampling time points are in the first line of input file, it could be set as a character sting-"Line1" or "line1". |
minper |
a numeric value. The minimum period length of interested
rhythms. The default is |
maxper |
a numeric value. The maximum period length of interested
rhythms. The default is |
cycMethod |
a character vector(length 1 or 2 or 3). User-defined
methods for detecting rhythmic signals, must be selected as any one, any
two or all three methods(default) from |
analysisStrategy |
a character string. The strategy used to select
proper methods from |
outputFile |
logical. If |
outIntegration |
a character string. This parameter controls what
kinds of analysis results will be outputted, must be one of |
adjustPhase |
a character string. The method used to adjust original
phase calculated by each method in integration file, must be one of
|
combinePvalue |
a character string. The method used to integrate
multiple p-values, must be one of |
weightedPerPha |
logical. If |
ARSmle |
a character string. The strategy of using MLE method in
|
ARSdefaultPer |
a numeric value. The expected period length of
interested rhythm, which is a necessary parameter for |
outRawData |
logical. If |
releaseNote |
logical. If |
outSymbol |
a character string. A common prefix exists in the names of output files. |
parallelize |
logical. If |
nCores |
a integer. Bigger or equal to one, number of cores to use. |
inDF |
data.frame. If |
Details
ARSER(Yang, 2010),
JTK_CYCLE(
Hughes, 2010), and
Lomb-Scargle(Glynn, 2006) are three popular methods of detecting
rhythmic signals. ARS can not analyze unevenly sampled datasets,
or evenly sampled datasets but with missing values, or with replicate
samples, or with non-integer sampling interval. JTK is not
suitable to analyze unevenly sampled datasets or evenly sampled datasets
but with non-integer sampling interval. If set analysisStrategy
as "auto"(default), meta2d will automatically select
proper method from cycMethod for each input dataset. If the user
clearly know that the dataset could be analyzed by each method defined
by cycMethod and do not hope to output integrated values,
analysisStrategy can be set as "selfUSE".
ARS used here is translated from its python version which always
uses "yule-walker", "burg", and "mle" methods(see
ar) to fit autoregressive models to time-series
data. Fitting by "mle" will be very slow for datasets
with many time points. If ARSmle = "auto" is used,
meta2d will only include "mle" when number of time points
is smaller than 24. In addition, one evaluation work(Wu, 2014) indicates
that ARS shows relative high false positive rate in analyzing
high-resolution datasets (1h/2days and 2h/2days). JTK(version 3)
used here is the latest version, which improves its p-value calculation
in analyzing datasets with missing values.
The power of detecting rhythmic signals for an algorithm is associated
with the nature of data and interested periodic pattern(Deckard, 2013),
which indicates that integrating analysis results from multiple methods
may be helpful to rhythmic detection. For integrating p-values,
Bonferroni correction("bonferroni") and Fisher's method(
"fisher") (Fisher, 1925; implementation code from MADAM)
could be selected, and "bonferroni" is usually more conservative
than "fisher". The integrated period is arithmetic mean of
multiple periods. For integrating phase, meta2d takes use of
mean of circular quantities. Integrated period and phase is further
used to calculate the baseline value and amplitude through fitting a
constructed periodic model.
Phases given by JTK and LS need to be adjusted with their
predicted period (adjustedPhase = "predictedPer") before
integration. If adjustedPhas = "notAdjusted" is selected, no
integrated phase will be calculated. If set weightedPerPha as
TRUE, weighted scores will be used in averaging periods and
phases. Weighted scores for one method are based on all its reported
p-values, which means a weighted score assigned to any one profile will
be affected by all other profiles. It is always a problem of averaging
phases with quite different period lengths(eg. averaging two phases
with 16-hours' and 30-hours' period length). Currently, setting
minper, maxper and ARSdefaultPer to a same value
may be the only way of completely eliminating such problem.
This function is originally aimed to analyze large scale periodic data(
eg. circadian transcriptome data) without individual information.
Please pay attention to data format of input file(see Examples
part). Except the first column and first row, others are time-series
experimental values(setting missing values as NA).
Value
meta2d will write analysis results in different files under
outdir if set outputFile = TRUE. Files named with
"ARSresult", "JTKresult" and "LSreult" store analysis results from
ARS, JTK and LS respectively. The file named with
"meta2d" is the integration file, and it stores integrated values in
columns with a common name tag-"meta2d". The integration file also
contains p-value, FDR value, period, phase(adjusted phase if
adjustedPhase = "predictedPer") and amplitude values calculated
by each method.
If outputFile = FALSE is selected, meta2d will return a
list containing the following components:
| ARS | analysis results from ARS method |
| JTK | analysis results from JTK method |
| LS | analysis results from LS method |
| meta | the integrated analysis results as mentioned above |
References
Yang R. and Su Z. (2010). Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation. Bioinformatics, 26(12), i168–i174.
Hughes M. E., Hogenesch J. B. and Kornacker K. (2010). JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. Journal of Biological Rhythms, 25(5), 372–380.
Glynn E. F., Chen J. and Mushegian A. R. (2006). Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms. Bioinformatics, 22(3), 310–316.
Wu G., Zhu J., Yu J., Zhou L., Huang J. Z. and Zhang Z. (2014). Evaluation of five methods for genome-wide circadian gene identification. Journal of Biological Rhythms, 29(4), 231–242.
Deckard A., Anafi R. C., Hogenesch J. B., Haase S.B. and Harer J. (2013). Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data. Bioinformatics, 29(24), 3174–3180.
Fisher, R.A. (1925). Statistical methods for research workers. Oliver and Boyd (Edinburgh).
Kugler K. G., Mueller L.A. and Graber A. (2010). MADAM - an open source toolbox for meta-analysis. Source Code for Biology and Medicine, 5, 3.
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 32 33 34 35 36 37 38 39 40 | # write 'cycSimu4h2d', 'cycMouseLiverRNA' and 'cycYeastCycle' into three
# 'csv' files
write.csv(cycSimu4h2d, file="cycSimu4h2d.csv", row.names=FALSE)
write.csv(cycMouseLiverRNA, file="cycMouseLiverRNA.csv", row.names=FALSE)
write.csv(cycYeastCycle, file="cycYeastCycle.csv", row.names=FALSE)
# write 'cycMouseLiverProtein' into a 'txt' file
write.table(cycMouseLiverProtein, file="cycMouseLiverProtein.txt",
sep="\t", quote=FALSE, row.names=FALSE)
# analyze 'cycMouseLiverRNA.csv' with JTK_CYCLE
# this is masked for keeping the total running time within 10s required by CRAN check
# meta2d(infile="cycMouseLiverRNA.csv", filestyle="csv", outdir="example",
# timepoints=18:65, cycMethod="JTK", outIntegration="noIntegration")
# analyze 'cycMouseLiverProtein.txt' with JTK_CYCLE and Lomb-Scargle
meta2d(infile="cycMouseLiverProtein.txt", filestyle="txt",
outdir="example", timepoints=rep(seq(0, 45, by=3), each=3),
cycMethod=c("JTK","LS"), outIntegration="noIntegration")
# analyze 'cycSimu4h2d.csv' with ARSER, JTK_CYCLE and Lomb-Scargle and
# output integration file with analysis results from each method
meta2d(infile="cycSimu4h2d.csv", filestyle="csv", outdir="example",
timepoints="Line1")
# analyze 'cycYeastCycle.csv' with ARSER, JTK_CYCLE and Lomb-Scargle to
# detect transcripts associated with cell cycle, and only output
# integration file
meta2d(infile="cycYeastCycle.csv",filestyle="csv", outdir="example",
minper=80, maxper=96, timepoints=seq(2, 162, by=16),
outIntegration="onlyIntegration", ARSdefaultPer=85,
outRawData=TRUE)
# return analysis results instead of output them into files
cyc <- meta2d(infile="cycYeastCycle.csv",filestyle="csv",
minper=80, maxper=96, timepoints=seq(2, 162, by=16),
outputFile=FALSE, ARSdefaultPer=85, outRawData=TRUE)
head(cyc$ARS)
head(cyc$JTK)
head(cyc$LS)
head(cyc$meta)
|
Example output
The JTK is in process from 11:48:35 08-07-2019
Warning: the input 'minper' is not suitable for JTK, it was reset as 21
Warning: the input 'maxper' is not suitable for JTK, it was reset as 27
The analysis by JTK is finished at 11:48:38 08-07-2019
The LS is in process from 11:48:38 08-07-2019
The analysis by LS is finished at 11:48:38 08-07-2019
DONE! The analysis about ' cycMouseLiverProtein.txt ' has been finished.
user.self sys.self elapsed user.child sys.child
"Time used:" "2.475" "0.02" "2.51" "0" "0"
The ARS is in process from 11:48:38 08-07-2019
The analysis by ARS is finished at 11:48:39 08-07-2019
The JTK is in process from 11:48:39 08-07-2019
The analysis by JTK is finished at 11:48:39 08-07-2019
The LS is in process from 11:48:39 08-07-2019
The analysis by LS is finished at 11:48:39 08-07-2019
DONE! The analysis about ' cycSimu4h2d.csv ' has been finished.
user.self sys.self
"Time used:" "1.006" "0.00800000000000001"
elapsed user.child sys.child
"1.027" "0" "0"
The ARS is in process from 11:48:39 08-07-2019
The analysis by ARS is finished at 11:48:39 08-07-2019
The JTK is in process from 11:48:39 08-07-2019
The analysis by JTK is finished at 11:48:39 08-07-2019
The LS is in process from 11:48:39 08-07-2019
The analysis by LS is finished at 11:48:39 08-07-2019
DONE! The analysis about ' cycYeastCycle.csv ' has been finished.
user.self sys.self
"Time used:" "0.335" "0.00399999999999999"
elapsed user.child sys.child
"0.339" "0" "0"
The ARS is in process from 11:48:40 08-07-2019
The analysis by ARS is finished at 11:48:40 08-07-2019
The JTK is in process from 11:48:40 08-07-2019
The analysis by JTK is finished at 11:48:40 08-07-2019
The LS is in process from 11:48:40 08-07-2019
The analysis by LS is finished at 11:48:40 08-07-2019
DONE! The analysis about ' cycYeastCycle.csv ' has been finished.
user.self sys.self elapsed
"Time used:" "0.225000000000001" "0" "0.225"
user.child sys.child
"0" "0"
CycID filter_type ar_method period_number period
1 YLR072W_1777968_at 0 mle 1 87.7362637362637
2 YBR275C_1771064_at 0 yule-walker 1 89.7078651685393
3 YCL024W_1779753_at 1 yule-walker 1 86.3135135135135
4 YGR177C_1771190_at 0 default 1 85
5 YEL069C_1775358_at 1 mle 1 89.7078651685393
6 YLR125W_1769482_at 0 yule-walker 1 91.7701149425287
amplitude phase mean R_square R2_adjust coef_var
1 94.5793636593827 61.4128785798212 996.0288 0.6673638 0.5842048 0.10218618
2 101.952188023999 7.15399100874371 567.2834 0.5396428 0.4245535 0.26405373
3 452.102904641171 1.57365902889558 1187.0017 0.5867804 0.4834755 0.40121892
4 150.64941279979 49.6491302047991 1842.6738 0.7439124 0.6798906 0.07935373
5 9.59969195724396 82.549797941594 199.5306 0.8345810 0.7932263 0.03683684
6 26.1892698123898 6.54455912941449 425.5334 0.6511591 0.5639488 0.06605052
pvalue fdr_BH recov_2min recov_18min recov_34min recov_50min
1 0.012242720 0.02155702 869.8609 797.2953 942.6339 1069.1362
2 0.044913802 0.04592298 879.3746 849.6005 518.1937 477.9088
3 0.029155702 0.03644463 2448.7403 1560.6675 745.9879 599.9138
4 0.004300846 0.01433615 1664.7032 1605.5524 1817.0391 2066.6323
5 0.000748758 0.00748758 211.1860 192.3690 191.4977 194.5959
6 0.014808455 0.02155702 487.0184 454.4303 405.9583 408.3755
recov_66min recov_82min recov_98min recov_114min recov_130min recov_146min
1 1124.1407 1070.2156 903.6072 960.5258 1049.3727 1080.8489
2 501.3832 557.3082 589.2522 563.5682 465.7285 415.0808
3 1061.5011 1398.0423 1247.4566 1059.1671 967.4813 902.5316
4 1915.4511 1675.6887 1724.0113 1942.5318 1989.9812 1955.0953
5 204.7598 208.4046 203.6591 196.5309 192.7436 191.1770
6 402.5615 422.6579 455.0661 439.8246 408.4970 399.6859
recov_162min
1 1088.6792
2 422.7184
3 1065.5292
4 1912.7260
5 207.9130
6 396.7915
CycID BH.Q ADJ.P PER LAG AMP recov_2min
1 YLR072W_1777968_at 0.009169698 0.001455026 96 64 141.67214 869.8609
2 YBR275C_1771064_at 0.009169698 0.003667879 96 8 234.34002 879.3746
3 YCL024W_1779753_at 0.013924713 0.008354828 96 0 576.06547 2448.7403
4 YGR177C_1771190_at 0.042711778 0.034169422 80 56 177.30558 1664.7032
5 YEL069C_1775358_at 0.069754311 0.062778880 96 88 11.33888 211.1860
6 YLR125W_1769482_at 0.013924713 0.008354828 96 8 34.27492 487.0184
recov_18min recov_34min recov_50min recov_66min recov_82min recov_98min
1 797.2953 942.6339 1069.1362 1124.1407 1070.2156 903.6072
2 849.6005 518.1937 477.9088 501.3832 557.3082 589.2522
3 1560.6675 745.9879 599.9138 1061.5011 1398.0423 1247.4566
4 1605.5524 1817.0391 2066.6323 1915.4511 1675.6887 1724.0113
5 192.3690 191.4977 194.5959 204.7598 208.4046 203.6591
6 454.4303 405.9583 408.3755 402.5615 422.6579 455.0661
recov_114min recov_130min recov_146min recov_162min
1 960.5258 1049.3727 1080.8489 1088.6792
2 563.5682 465.7285 415.0808 422.7184
3 1059.1671 967.4813 902.5316 1065.5292
4 1942.5318 1989.9812 1955.0953 1912.7260
5 196.5309 192.7436 191.1770 207.9130
6 439.8246 408.4970 399.6859 396.7915
CycID PhaseShift PhaseShiftHeight PeakIndex PeakSPD Period
1 YLR072W_1777968_at 66.20825 1097.1808 16 4.082555 89.73913
2 YBR275C_1771064_at 97.10063 576.7867 8 3.056345 92.97297
3 YCL024W_1779753_at 92.04248 1273.4348 13 3.125249 90.92511
4 YGR177C_1771190_at 51.04197 1941.0634 30 3.876712 84.59016
5 YEL069C_1775358_at 82.30036 206.3205 6 4.300112 93.81818
6 YLR125W_1769482_at 99.51891 441.0674 1 4.096129 96.00000
p N Nindependent Nyquist BH.Q recov_2min recov_18min
1 0.09701453 11 6 0.034375 0.2360983 869.8609 797.2953
2 0.25114957 11 6 0.034375 0.2790551 879.3746 849.6005
3 0.23625389 11 6 0.034375 0.2790551 2448.7403 1560.6675
4 0.11804913 11 6 0.034375 0.2360983 1664.7032 1605.5524
5 0.07869067 11 6 0.034375 0.2360983 211.1860 192.3690
6 0.09576088 11 6 0.034375 0.2360983 487.0184 454.4303
recov_34min recov_50min recov_66min recov_82min recov_98min recov_114min
1 942.6339 1069.1362 1124.1407 1070.2156 903.6072 960.5258
2 518.1937 477.9088 501.3832 557.3082 589.2522 563.5682
3 745.9879 599.9138 1061.5011 1398.0423 1247.4566 1059.1671
4 1817.0391 2066.6323 1915.4511 1675.6887 1724.0113 1942.5318
5 191.4977 194.5959 204.7598 208.4046 203.6591 196.5309
6 405.9583 408.3755 402.5615 422.6579 455.0661 439.8246
recov_130min recov_146min recov_162min
1 1049.3727 1080.8489 1088.6792
2 465.7285 415.0808 422.7184
3 967.4813 902.5316 1065.5292
4 1989.9812 1955.0953 1912.7260
5 192.7436 191.1770 207.9130
6 408.4970 399.6859 396.7915
CycID ARS_pvalue ARS_BH.Q ARS_period ARS_adjphase
1 YLR072W_1777968_at 0.012242720 0.02155702 87.73626 61.412879
2 YBR275C_1771064_at 0.044913802 0.04592298 89.70787 7.153991
3 YCL024W_1779753_at 0.029155702 0.03644463 86.31351 1.573659
4 YGR177C_1771190_at 0.004300846 0.01433615 85.00000 49.649130
5 YEL069C_1775358_at 0.000748758 0.00748758 89.70787 82.549798
6 YLR125W_1769482_at 0.014808455 0.02155702 91.77011 6.544559
ARS_amplitude JTK_pvalue JTK_BH.Q JTK_period JTK_adjphase JTK_amplitude
1 94.579364 0.001455026 0.009169698 96 66 141.67214
2 101.952188 0.003667879 0.009169698 96 10 234.34002
3 452.102905 0.008354828 0.013924713 96 2 576.06547
4 150.649413 0.034169422 0.042711778 80 58 177.30558
5 9.599692 0.062778880 0.069754311 96 90 11.33888
6 26.189270 0.008354828 0.013924713 96 10 34.27492
LS_pvalue LS_BH.Q LS_period LS_adjphase LS_amplitude meta2d_pvalue
1 0.09701453 0.2360983 89.73913 66.208254 1097.1808 0.0001767816
2 0.25114957 0.2790551 92.97297 4.127657 576.7867 0.0025662522
3 0.23625389 0.2790551 90.92511 1.117369 1273.4348 0.0033620108
4 0.11804913 0.2360983 84.59016 51.041968 1941.0634 0.0012498505
5 0.07869067 0.2360983 93.81818 82.300358 206.3205 0.0003392889
6 0.09576088 0.2360983 96.00000 3.518911 441.0674 0.0009084778
meta2d_BH.Q meta2d_period meta2d_phase meta2d_Base meta2d_AMP meta2d_rAMP
1 0.001696444 91.15846 64.575261 997.4851 107.517320 0.10778839
2 0.003666075 92.89361 7.071532 569.6488 118.435669 0.20790997
3 0.003876404 91.07954 1.559115 1200.1148 483.025552 0.40248278
4 0.002499701 83.19672 52.945684 1848.1603 165.485553 0.08954069
5 0.001696444 93.17535 84.947692 200.1210 9.952018 0.04973000
6 0.002271194 94.59004 6.689105 426.2096 31.079671 0.07292110
recov_2min recov_18min recov_34min recov_50min recov_66min recov_82min
1 869.8609 797.2953 942.6339 1069.1362 1124.1407 1070.2156
2 879.3746 849.6005 518.1937 477.9088 501.3832 557.3082
3 2448.7403 1560.6675 745.9879 599.9138 1061.5011 1398.0423
4 1664.7032 1605.5524 1817.0391 2066.6323 1915.4511 1675.6887
5 211.1860 192.3690 191.4977 194.5959 204.7598 208.4046
6 487.0184 454.4303 405.9583 408.3755 402.5615 422.6579
recov_98min recov_114min recov_130min recov_146min recov_162min
1 903.6072 960.5258 1049.3727 1080.8489 1088.6792
2 589.2522 563.5682 465.7285 415.0808 422.7184
3 1247.4566 1059.1671 967.4813 902.5316 1065.5292
4 1724.0113 1942.5318 1989.9812 1955.0953 1912.7260
5 203.6591 196.5309 192.7436 191.1770 207.9130
6 455.0661 439.8246 408.4970 399.6859 396.7915
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