IWTomicsTest: Interval-Wise Testing
In marziacremona/IWTomics: Interval-Wise Testing for Omics Data
Description
Usage
Arguments
Value
Note
Author(s)
References
See Also
Examples
Description
The function implements the Interval-Wise Testing for omics data (both one sample
and two sample tests), an extended version of the Interval-Wise Testing for functional
data presented in Pini and Vantini (2017). This inferential procedure tests for
differences in feature measurements between two region datasets (two sample test)
or between a region dataset and a reference curve (one sample test).
Usage
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IWTomicsTest(regionsFeatures,
id_region1=idRegions(regionsFeatures)[1], id_region2=NULL,
id_features_subset=idFeatures(regionsFeatures), mu=0,
statistics="mean", probs=0.5, max_scale=NULL, paired=FALSE, B=1000)
Arguments
regionsFeatures
"IWTomicsData" object.
id_region1
identifier(s) of the region dataset(s) to be tested. If a vector
is provided, a test will be performed for each element of the vector.
id_region2
identifier(s) of the region dataset(s) to be tested for two sample
test. If NULL or empty string, one sample test is performed.
id_features_subset
vector with the identifiers of the features to be tested.
mu
the reference curve (center of symmetry) under the null hypothesis
in one sample test, or the difference between the two populations in two
sample test. Can be either a constant (the same constant curve for all features),
a vector of constants (a constant curve for each feature), or a list of vectors
containing its measurements for each feature (on the same grid as the features).
Default mu=0.
statistics
test statistics to be used in the test. Possible test statistics
are "mean", "median", "variance" and "quantile".
probs
probabilities corresponding to the quantiles in test statistics
"quantile". If multiple quantiles are selected, the test statistics is the sum
of the statistics on the different quantiles.
max_scale
the maximum interval length to be used for the p-value adjustment,
i.e. the maximum number of consecutive windows to be employed (can range from 1 to
the maximum number of consecutive measurements present for the feature tested).
Can be either a scalar (the same length for all features) or a vector (a length
for each feature) or a list of vectors (a vector for each test). If NULL,
the maximum possible interval length is used.
paired
if TRUE, a paired (two sample) test is performed.
B
number of iterations of the MC algorithm to evaluate the p-values of the
permutation tests. If B is greater than the number of possible permutations,
exact p-values are computed.
Value
The function IWTomicsTest returns an object of class "IWTomicsData"
containing the region datasets and feature datasets tested, and with the test
input and result in the optional slot test.
Note
In this implementation of Interval-Wise Testing, the smallest scale considered
corresponds to the measurement resolution, i.e. the univariate (unadjusted) tests
are done on each measurement window. To change the smallest scale considered the
method smooth can be employed.
If the region alignment is "scale", the function
smooth must be used before applying the
Interval-Wise Testing in order to measure the features over the grid in all the regions.
Author(s)
Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini
References
A Pini and S Vantini (2017). Interval-Wise Testing for functional data.
Journal of Nonparametric Statistics.
See Also
IWTomicsData for "IWTomicsData" class, constructors, accessors and methods;
plot method to plot "IWTomicsData" objects;
smooth to smooth curves before testing;
plotTest to plot the test results;
plotSummary to draw a summary plot of the test results.
Examples
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## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## EXAMPLE ON REAL DATA
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## ETn Recombination hotspots data
## Two region datasets:
## ETns fixed 64-kb flanking regions and 64-kb control regions in mouse
## One feature measured in 1-kb windows:
## Recombination hotspots content
## ?ETn_example for details on the dataset
data(ETn_example)
ETn_example
## Two sample test to compare recombination hotspots
## in ETn regions vs control regions
ETn_test=IWTomicsTest(ETn_example,
id_region1='ETn_fixed',id_region2='Control')
## Adjusted p-value
adjusted_pval(ETn_test)
## Adjusted p-value lowering the scale of the test
adjusted_pval(ETn_test,scale_threshold=10)
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## EXAMPLE ON SIMULATED DATA
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## CURVE ALIGNMENT CENTER
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
data(regionsFeatures_center)
## One sample test for 'control' regions and feature 'ftr1'
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1='control',id_features_subset='ftr1')
adjusted_pval(regionsFeatures_test)
## Plotting the results of the one sample test
plotTest(regionsFeatures_test,col=5)
## Two sample test for 'elem1', 'elem2' and 'elem3' vs 'control' regions and feature 'ftr1'
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3'),
id_region2=c('control','control','control'),
id_features_subset='ftr1')
adjusted_pval(regionsFeatures_test)
## Plotting the results of the two sample test
plotTest(regionsFeatures_test)
## Summary plot of the two sample test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',align_lab='Center')
#############################################################################################
## Not run:
#############################################################################################
## Using 'quantile' test statistics with multiple quantiles
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3'),
id_region2=c('control','control','control'),
id_features_subset='ftr1',
statistics='quantile',probs=c(0.25,0.75))
adjusted_pval(regionsFeatures_test)
## Plotting the results of the two sample test
plotTest(regionsFeatures_test)
## Summary plot of the two sample test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',align_lab='Center')
#############################################################################################
## End(Not run)
#############################################################################################
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## CURVE ALIGNMENT SCALE
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
data(regionsFeatures_scale)
## Smooth the curves to have measurements on the same grid
regionsFeatures_smooth=smooth(regionsFeatures_scale,type='locpoly',scale_grid=30)
## Two sample test for 'elem1', 'elem2' and 'elem3' vs 'control' regions and feature 'ftr2'
regionsFeatures_test=IWTomicsTest(regionsFeatures_smooth,
id_region1=c('elem1','elem2','elem3'),
id_region2=c('control','control','control'),
id_features_subset='ftr2')
adjusted_pval(regionsFeatures_test)
## Plotting the results of the two sample test
plotTest(regionsFeatures_test)
## Summary plot of the two sample test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature')
marziacremona/IWTomics documentation built on May 21, 2019, 12:39 p.m.
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Description Usage Arguments Value Note Author(s) References See Also Examples
Description
The function implements the Interval-Wise Testing for omics data (both one sample and two sample tests), an extended version of the Interval-Wise Testing for functional data presented in Pini and Vantini (2017). This inferential procedure tests for differences in feature measurements between two region datasets (two sample test) or between a region dataset and a reference curve (one sample test).
Usage
1 2 3 4 | IWTomicsTest(regionsFeatures,
id_region1=idRegions(regionsFeatures)[1], id_region2=NULL,
id_features_subset=idFeatures(regionsFeatures), mu=0,
statistics="mean", probs=0.5, max_scale=NULL, paired=FALSE, B=1000)
|
Arguments
regionsFeatures |
|
id_region1 |
identifier(s) of the region dataset(s) to be tested. If a vector is provided, a test will be performed for each element of the vector. |
id_region2 |
identifier(s) of the region dataset(s) to be tested for two sample
test. If |
id_features_subset |
vector with the identifiers of the features to be tested. |
mu |
the reference curve (center of symmetry) under the null hypothesis
in one sample test, or the difference between the two populations in two
sample test. Can be either a constant (the same constant curve for all features),
a vector of constants (a constant curve for each feature), or a list of vectors
containing its measurements for each feature (on the same grid as the features).
Default |
statistics |
test statistics to be used in the test. Possible test statistics
are |
probs |
probabilities corresponding to the quantiles in test statistics
|
max_scale |
the maximum interval length to be used for the p-value adjustment,
i.e. the maximum number of consecutive windows to be employed (can range from 1 to
the maximum number of consecutive measurements present for the feature tested).
Can be either a scalar (the same length for all features) or a vector (a length
for each feature) or a list of vectors (a vector for each test). If |
paired |
if |
B |
number of iterations of the MC algorithm to evaluate the p-values of the permutation tests. If B is greater than the number of possible permutations, exact p-values are computed. |
Value
The function IWTomicsTest returns an object of class "IWTomicsData"
containing the region datasets and feature datasets tested, and with the test
input and result in the optional slot test.
Note
In this implementation of Interval-Wise Testing, the smallest scale considered
corresponds to the measurement resolution, i.e. the univariate (unadjusted) tests
are done on each measurement window. To change the smallest scale considered the
method smooth can be employed.
If the region alignment is "scale", the function
smooth must be used before applying the
Interval-Wise Testing in order to measure the features over the grid in all the regions.
Author(s)
Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini
References
A Pini and S Vantini (2017). Interval-Wise Testing for functional data. Journal of Nonparametric Statistics.
See Also
IWTomicsData for "IWTomicsData" class, constructors, accessors and methods;
plot method to plot "IWTomicsData" objects;
smooth to smooth curves before testing;
plotTest to plot the test results;
plotSummary to draw a summary plot of the test results.
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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | ## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## EXAMPLE ON REAL DATA
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## ETn Recombination hotspots data
## Two region datasets:
## ETns fixed 64-kb flanking regions and 64-kb control regions in mouse
## One feature measured in 1-kb windows:
## Recombination hotspots content
## ?ETn_example for details on the dataset
data(ETn_example)
ETn_example
## Two sample test to compare recombination hotspots
## in ETn regions vs control regions
ETn_test=IWTomicsTest(ETn_example,
id_region1='ETn_fixed',id_region2='Control')
## Adjusted p-value
adjusted_pval(ETn_test)
## Adjusted p-value lowering the scale of the test
adjusted_pval(ETn_test,scale_threshold=10)
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## EXAMPLE ON SIMULATED DATA
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## CURVE ALIGNMENT CENTER
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
data(regionsFeatures_center)
## One sample test for 'control' regions and feature 'ftr1'
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1='control',id_features_subset='ftr1')
adjusted_pval(regionsFeatures_test)
## Plotting the results of the one sample test
plotTest(regionsFeatures_test,col=5)
## Two sample test for 'elem1', 'elem2' and 'elem3' vs 'control' regions and feature 'ftr1'
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3'),
id_region2=c('control','control','control'),
id_features_subset='ftr1')
adjusted_pval(regionsFeatures_test)
## Plotting the results of the two sample test
plotTest(regionsFeatures_test)
## Summary plot of the two sample test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',align_lab='Center')
#############################################################################################
## Not run:
#############################################################################################
## Using 'quantile' test statistics with multiple quantiles
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3'),
id_region2=c('control','control','control'),
id_features_subset='ftr1',
statistics='quantile',probs=c(0.25,0.75))
adjusted_pval(regionsFeatures_test)
## Plotting the results of the two sample test
plotTest(regionsFeatures_test)
## Summary plot of the two sample test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',align_lab='Center')
#############################################################################################
## End(Not run)
#############################################################################################
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
## CURVE ALIGNMENT SCALE
## -------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------
data(regionsFeatures_scale)
## Smooth the curves to have measurements on the same grid
regionsFeatures_smooth=smooth(regionsFeatures_scale,type='locpoly',scale_grid=30)
## Two sample test for 'elem1', 'elem2' and 'elem3' vs 'control' regions and feature 'ftr2'
regionsFeatures_test=IWTomicsTest(regionsFeatures_smooth,
id_region1=c('elem1','elem2','elem3'),
id_region2=c('control','control','control'),
id_features_subset='ftr2')
adjusted_pval(regionsFeatures_test)
## Plotting the results of the two sample test
plotTest(regionsFeatures_test)
## Summary plot of the two sample test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature')
|
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