IWTomicsTest: Interval-Wise Testing

Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/IWTomicsTest.R

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 April 3, 2018, 10:26 p.m.