plotSummary: Summary plots of Interval-Wise Testing result
In IWTomics: Interval-Wise Testing for Omics Data
Description
Usage
Arguments
Value
Note
Author(s)
See Also
Examples
Description
The function creates a graphical summary of the Interval-Wise Testing results. In particular,
it draws a heatmap of the adjusted p-value curves at the chosen scale thresholds, grouped by
the region datasets tested (test) or by the features tested (feature). Different raws in the heatmap correspond
to different features (when grouping by locations) or to different tests (when grouping by feature).
Color intensity is proportional to -log(p-value), i.e. darker colors correspond to lower
p-values. Red means that the test statistics is higher in the first dataset tested than in the
second one (or is positive in one sample test), while blue means that the test statistics is
lower in the first datset tested than in the second one (or is negative in one sample test).
Usage
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plotSummary(regionsFeatures, alpha=0.05, only_significant=FALSE,
scale_threshold=NULL, nlevel=100, groupby='test',
test=1:nTests(regionsFeatures), gaps_tests=NULL,
id_features_subset=idFeaturesTest(regionsFeatures), gaps_features=NULL,
ask=TRUE, filenames=NA, align_lab=NA, cellwidth=NA, cellheight=NA,
xlab='Windows', ylab=ifelse(groupby=='test','Features','Tests'), ...)
Arguments
regionsFeatures
"IWTomicsData" object with test slot.
alpha
level of the hypothesis test used to select and display significant
results. Default alpha=0.05.
only_significant
if TRUE, only the significant tests (p-value<=alpha in some
position) will be plotted.
scale_threshold
threshold on the test scale (maximum interval length for
the p-value adjustment) to be used in the adjusted p-value plot. 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.
nlevel
number of desired color levels for the adjusted p-value heatmap.
groupby
how tests should be grouped for the summary plot. Possible types are:
-
"test" to draw a summary plot for all the tests about the same region
dataset(s) comparison,
-
"feature" to draw a summary plot for all the tests about the same feature.
test
vector of indices of the tests to be plotted.
gaps_tests
vector of test indices that show where to put gaps in the heatmap
between tests to be plotted. Only used if groupby is "feature".
id_features_subset
vector with the identifiers of the features to be plotted.
gaps_features
vector of id_features_subset indices that show where to put gaps
in the heatmap between features. Only used if groupby is "test".
ask
if TRUE (default) the user is prompted before a new plot is drawn.
filenames
file paths where to save the plots (one for each group of tests as defined
by groupby). Filetypes are decided by the extension in the paths. Currently the
following formats are supported: png, pdf, tiff, bmp, jpeg.
align_lab
a title for the alignment point. Ignored if region alignment is "scale".
cellwidth
individual cell width in points.
cellheight
individual cell height in points.
xlab
a title for the x axis.
ylab
a title for the y axis.
...
additional plot parameters.
Value
No value returned. The function produces a graphical output.
Note
This function uses a modified version of pheatmap package
(https://cran.r-project.org/package=pheatmap).
Author(s)
Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini
See Also
IWTomicsData for "IWTomicsData" class, constructors, accessors and methods;
IWTomicsTest function to perform the Interval-wise Testing;
plotTest to draw detailed plots of the test results.
Examples
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## -------------------------------------------------------------------------------------------
## CURVE ALIGNMENT CENTER
## -------------------------------------------------------------------------------------------
data(regionsFeatures_center)
## One sample test for different regions and features
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3','control'),mu=1)
## Summary plots grouped by feature
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature')
## Set scale thresholds for the different features
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
scale_threshold=c(25,30))
## Add a title for the alignment point
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
scale.threshold=c(25,30),align_lab='Center')
## Plot only significant tests
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
scale.threshold=c(25,30),align_lab='Center',
only_significant=TRUE)
## Summary plots grouped by test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='test')
## Two sample test for all possible region comparisons (mu=0),
## and one sample test for 'elem3' (mu=1) for feature 'ftr1'
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3',
'elem1','elem1','elem2','elem1'),
id_region2=c('control','control','control',
'elem2','elem3','elem3',''),
id_features_subset='ftr1')
## Summary plots grouped by feature
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature')
## Put gaps between different types of test and add a title for the alignment point
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
gaps_tests=c(3,6),align_lab='Center')
## Plot only significant tests
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
gaps_tests=c(3,6),align_lab='Center',only_significant=TRUE)
## Plot only the first three tests
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
test=1:3,align_lab='Center')
## Change scale threshold for the first test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
test=1:3,align_lab='Center',scale_threshold=list(t1=8,t2=50,t3=50))
Example output
Loading required package: GenomicRanges
Loading required package: stats4
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:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
Attaching package: 'IWTomics'
The following object is masked from 'package:stats':
smooth
Performing IWT for 'Elements 1'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 2'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 3'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 1' vs. 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Warning: p-value not fully computable in some points, because of too many NAs present.
Performing IWT for 'Elements 2' vs. 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 3' vs. 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 1' vs. 'Elements 2'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Warning: p-value not fully computable in some points, because of too many NAs present.
Performing IWT for 'Elements 1' vs. 'Elements 3'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 2' vs. 'Elements 3'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 1'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
IWTomics documentation built on Nov. 8, 2020, 5:33 p.m.
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Description Usage Arguments Value Note Author(s) See Also Examples
Description
The function creates a graphical summary of the Interval-Wise Testing results. In particular, it draws a heatmap of the adjusted p-value curves at the chosen scale thresholds, grouped by the region datasets tested (test) or by the features tested (feature). Different raws in the heatmap correspond to different features (when grouping by locations) or to different tests (when grouping by feature). Color intensity is proportional to -log(p-value), i.e. darker colors correspond to lower p-values. Red means that the test statistics is higher in the first dataset tested than in the second one (or is positive in one sample test), while blue means that the test statistics is lower in the first datset tested than in the second one (or is negative in one sample test).
Usage
1 2 3 4 5 6 | plotSummary(regionsFeatures, alpha=0.05, only_significant=FALSE,
scale_threshold=NULL, nlevel=100, groupby='test',
test=1:nTests(regionsFeatures), gaps_tests=NULL,
id_features_subset=idFeaturesTest(regionsFeatures), gaps_features=NULL,
ask=TRUE, filenames=NA, align_lab=NA, cellwidth=NA, cellheight=NA,
xlab='Windows', ylab=ifelse(groupby=='test','Features','Tests'), ...)
|
Arguments
regionsFeatures |
|
alpha |
level of the hypothesis test used to select and display significant
results. Default |
only_significant |
if |
scale_threshold |
threshold on the test scale (maximum interval length for
the p-value adjustment) to be used in the adjusted p-value plot. 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 |
nlevel |
number of desired color levels for the adjusted p-value heatmap. |
groupby |
how tests should be grouped for the summary plot. Possible types are:
|
test |
vector of indices of the tests to be plotted. |
gaps_tests |
vector of |
id_features_subset |
vector with the identifiers of the features to be plotted. |
gaps_features |
vector of id_features_subset indices that show where to put gaps
in the heatmap between features. Only used if |
ask |
if |
filenames |
file paths where to save the plots (one for each group of tests as defined
by |
align_lab |
a title for the alignment point. Ignored if region alignment is |
cellwidth |
individual cell width in points. |
cellheight |
individual cell height in points. |
xlab |
a title for the x axis. |
ylab |
a title for the y axis. |
... |
additional plot parameters. |
Value
No value returned. The function produces a graphical output.
Note
This function uses a modified version of pheatmap package (https://cran.r-project.org/package=pheatmap).
Author(s)
Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini
See Also
IWTomicsData for "IWTomicsData" class, constructors, accessors and methods;
IWTomicsTest function to perform the Interval-wise Testing;
plotTest to draw detailed plots 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 | ## -------------------------------------------------------------------------------------------
## CURVE ALIGNMENT CENTER
## -------------------------------------------------------------------------------------------
data(regionsFeatures_center)
## One sample test for different regions and features
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3','control'),mu=1)
## Summary plots grouped by feature
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature')
## Set scale thresholds for the different features
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
scale_threshold=c(25,30))
## Add a title for the alignment point
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
scale.threshold=c(25,30),align_lab='Center')
## Plot only significant tests
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
scale.threshold=c(25,30),align_lab='Center',
only_significant=TRUE)
## Summary plots grouped by test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='test')
## Two sample test for all possible region comparisons (mu=0),
## and one sample test for 'elem3' (mu=1) for feature 'ftr1'
regionsFeatures_test=IWTomicsTest(regionsFeatures_center,
id_region1=c('elem1','elem2','elem3',
'elem1','elem1','elem2','elem1'),
id_region2=c('control','control','control',
'elem2','elem3','elem3',''),
id_features_subset='ftr1')
## Summary plots grouped by feature
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature')
## Put gaps between different types of test and add a title for the alignment point
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
gaps_tests=c(3,6),align_lab='Center')
## Plot only significant tests
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
gaps_tests=c(3,6),align_lab='Center',only_significant=TRUE)
## Plot only the first three tests
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
test=1:3,align_lab='Center')
## Change scale threshold for the first test
## x11(10,5)
plotSummary(regionsFeatures_test,groupby='feature',
test=1:3,align_lab='Center',scale_threshold=list(t1=8,t2=50,t3=50))
|
Example output
Loading required package: GenomicRanges
Loading required package: stats4
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:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
Attaching package: 'IWTomics'
The following object is masked from 'package:stats':
smooth
Performing IWT for 'Elements 1'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 2'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 3'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for feature 'Feature 2'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 1' vs. 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Warning: p-value not fully computable in some points, because of too many NAs present.
Performing IWT for 'Elements 2' vs. 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 3' vs. 'Controls'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 1' vs. 'Elements 2'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Warning: p-value not fully computable in some points, because of too many NAs present.
Performing IWT for 'Elements 1' vs. 'Elements 3'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 2' vs. 'Elements 3'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
Performing IWT for 'Elements 1'...
Performing IWT for feature 'Feature 1'...
Point-wise tests...
Interval-wise tests...
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