| Title: | Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. |
|---|---|
| Description: | The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. |
| Authors: | Luigi Marchionni <[email protected]>, Anuj Gupta <[email protected]> |
| Maintainer: | Luigi Marchionni <[email protected]>, Anuj Gupta <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.49.0 |
| Built: | 2024-10-30 08:40:02 UTC |
| Source: | https://github.com/bioc/matchBox |
The matchBox package allows to annotate and compare ranked vectors (e.g. by differential expression) of genomic features (e.g. genes, or probe sets), using CAT curves. A CAT curve displays the overlap proportion between two ranked vectors of identifiers against the number of considered features. This techiques was used for comparing differential gene expression results obtained from different platforms in different laboratories (see Irizarry et al, Nat Methods (2005))
Enables to filter data.frames containing feature identifiers and ranking statistics;
Enables to identify the common set of features across results from different genomic experiments;
Enables to merge multiple data.frames based on the commons set of features;
Computes the overlap proportion between any pair of ranked features;
Creates plots of the proportion of overlap along with conidence intervals;
Luigi Marchionni [email protected]
Irizarry, R. A.; Warren, D.; Spencer, F.; Kim, I. F.; Biswal, S.; Frank, B. C.; Gabrielson, E.; Garcia, J. G. N.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S. C.; Hoffman, E.; Jedlicka, A. E.; Kawasaki, E.; Martinez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S. Q. and Yu, W. Multiple-laboratory comparison of microarray platforms. Nat Methods, 2005, 2, 345-350
Ross, A. E.; Marchionni, L.; Vuica-Ross, M.; Cheadle, C.; Fan, J.; Berman, D. M.; and Schaeffer E. M. Gene Expression Pathways of High Grade Localized Prostate Cancer. Prostate 2011, 71, 1568-1578
Benassi, B.; Flavin, R.; Marchionni, L.; Zanata, S.; Pan, Y.; Chowdhury, D.; Marani, M.; Strano, S.; Muti, P.; and Blandino, G. c-Myc is activated via USP2a-mediated modulation of microRNAs in prostate cancer. Cancer Discovery, 2012, March, 2, 236-247
The calcHypPI function calculates probability intervals
for a correspondence at the top (CAT) curve using the
hypergeometric distribution. This function, based on the
qhyper quantile function, produces a probability
intervals matrix to be passed as argument to plotCat
in order to add probability intervals shades when plotting CAT curves.
calcHypPI(data, expectedProp = 0.1, prob = c(0.999999,0.999,0.99,0.95))calcHypPI(data, expectedProp = 0.1, prob = c(0.999999,0.999,0.99,0.95))
data |
The same data frame used to compute the CAT curves
with the |
expectedProp |
A single numeric value between 0 and 1. This is the proportion of features expected to be corresponding at the top of the ranking. The "expectedProp" argument can be set to NULL if the number of features expected to be similarly ranked is unknown. |
prob |
A numeric vector specifying the probabiliy intervals for the CAT curves to be computed. |
The calcHypPI uses qhyper quantile function
to compute the proportions of common features between two ordered
vectors for specified quantiles of the hypergeometric distribution.
Such proportions are used to add probability intervals
to CAT curves computed using ranks (see computeCat).
The prob argument is used to specify the desired probability
intervals to be computed. By default this numeric vector is equal to
c(0.999999, 0.999, 0.99, 0.95).
To understand the way this function works we can use
the analogy of repeated drawing of an increasing number
of balls from an urn containing both white and black balls
(see qhyper).
According to this analogy the total number of balls in the urn
corresponds to the total number of common features
between two ordered vectors that are being compared
(e.g. all the genes in common between two genomic studies).
The number of white balls corresponds to the top ranking features that are correctly ordered (successes), while the black balls represent the features that are not correctly ordered (failures).
Finally, according to this analogy, comparing the first top 10 features from each vector will correspond to a first draw of 10 balls from the urn, while comparing the top 20 features to a draw of 20 balls, and so on until all balls are drawn at once.
By default the calcHypPI function expects
that the top 10% of the features of the two vectors
are similarly ordered. This expectation can be modified
by the expectedProp argument. When
expectedProp is set equal to NULL
the number of white balls in the urn
(i.e. the top ranking features in the correct order)
corresponds to the number of balls that are drawn
at each attempt (i.e. the increasing size of top features
from each vector that are being compared).
It returns a numeric matrix containing the probability intervals
for CAT curves based on equal ranks.
The column names of this matrix specifies the quantiles
of the hypergeometric distribution used to compute
the intervals. The values represent the proportions of overlap
associated with the defined quantiles.
The resulting matrix object is used to add the probability
intervals shades when plotting CAT curves by passing it
to the preComputedPI argument of the
plotCat function.
This function will take more and more time to run when more and more features are used. For this reason it is convenient to compute the probability intervals separately and store the probability intervals matrix for re-use when plotting the CAT curves.
Luigi Marchionni [email protected]
Irizarry, R. A.; Warren, D.; Spencer, F.; Kim, I. F.; Biswal, S.; Frank, B. C.; Gabrielson, E.; Garcia, J. G. N.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S. C.; Hoffman, E.; Jedlicka, A. E.; Kawasaki, E.; Martinez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S. Q. and Yu, W. Multiple-laboratory comparison of microarray platforms. Nat Methods, 2005, 2, 345-350
Ross, A. E.; Marchionni, L.; Vuica-Ross, M.; Cheadle, C.; Fan, J.; Berman, D. M.; and Schaeffer E. M. Gene Expression Pathways of High Grade Localized Prostate Cancer. Prostate, 2011, 71, 1568-1578
Benassi, B.; Flavin, R.; Marchionni, L.; Zanata, S.; Pan, Y.; Chowdhury, D.; Marani, M.; Strano, S.; Muti, P.; and Blandino, G. c-Myc is activated via USP2a-mediated modulation of microRNAs in prostate cancer. Cancer Discovery, 2012, March, 2, 236-247
See qhyper, plotCat,
calcHypPI and computeCat.
###load data data(matchBoxExpression) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select t-statistics and merge into a new data.frame using SYMBOL mat <- mergeData(newMatchBoxExpression, idCol=idCol, byCol=byCol) ### compute probability intervals with default values confInt <- calcHypPI(data=mat) ###structure of confInt str(confInt) ### compute probability intervals with "expectedProp" set to NULL confInt2 <- calcHypPI(data=mat, expectedProp=NULL) ###structure of confInt str(confInt2)###load data data(matchBoxExpression) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select t-statistics and merge into a new data.frame using SYMBOL mat <- mergeData(newMatchBoxExpression, idCol=idCol, byCol=byCol) ### compute probability intervals with default values confInt <- calcHypPI(data=mat) ###structure of confInt str(confInt) ### compute probability intervals with "expectedProp" set to NULL confInt2 <- calcHypPI(data=mat, expectedProp=NULL) ###structure of confInt str(confInt2)
computeCat computes the overlap proportions between
pairs of ordered vectors of identifiers.
The input to this function is a data.frame containing non-redundant
identifiers and a number of ranking statistics organized by columns.
This function enables comparing all possible pair combinations,
or selecting one column as the reference ranking for the remaining.
The output of this function can be used as the input to
plotCat, which creates correspondence at the top
curves, as used in Irizarry et al, Nat Methods (2005), for
comparing differential gene expression across platforms and labs.
computeCat(data, size=nrow(data), idCol=1, ref, method = c("equalRank", "equalStat"), decreasing = TRUE)computeCat(data, size=nrow(data), idCol=1, ref, method = c("equalRank", "equalStat"), decreasing = TRUE)
data |
A data.frame produced by |
size |
numeric. The number of top ranking statistics
to be considered in the computation of the overlap proportions.
If omitted all rows in |
idCol |
numeric or character. The index (by default equal to one), or the name of the column containing the common identifiers (e.g. ENTREZID, SYMBOLS, ...). |
ref |
character. The column name corresponding to the ranking statistics to be used as the reference in all pairs of comparisons. |
method |
character. The method used to compute the overlap proportion between two ordered vectors of identifiers: either "equalRank" or "equalStat". The first method computed the overlap based on equal ranks, whereas the latter uses equal statistics. |
decreasing |
logical. This argument defines whether decreasing or increasing ordering should be used |
computeCat computes overlapping proportions
between pairs of ordered vectors of identifiers.
This function first finds all possible pairs of vector combinations,
then it computes the corresponding overlapping
proportions. If a column is selected as the reference,
using the argument ref, only the combinations
involving this column will be returned.
Briefly, for each CAT curve two vectors of identifiers are first ordered by the ranking statistics of choice, then the overlap between the two vectors is computed by considering more and more identifiers (vector size).
This function enables to compute overlapping proportions using two distinct methods: "equalRank" or "equalStat". With "equalRank" the overlap is obtained between vectors of the same size using equal ranks, which in turn can potentially correspond to ranking statistics of different magnitude (e.g. the vectors are of the same size, but might have different ranking statistics). With "equalStat" the overlap is obtained between vectors defined by using equal ranking statistics, which can potentially correspond to different rank, and hence to vectors of different size (e.g. the vectors are of different size, but have similar ranking statistics).
A list of lists in which each element correspond to a
CAT curve. If a specific reference column is provided
through the ref argument, the number of
list elements is equal to the number of combinations
involving the reference group, otherwise all possible
combinations are returned.
When the "equalRank" method is used each list element
contains only the overlapping proportion, while when
the "equalStat" method is used the number of genes
with equal statistics is stored along with the overlapping
proportion.
This output is used to produce CAT curves,
using the plotCat function, as described
in Irizarry et al, Nat Methods (2005).
Given the combinatorial nature of the computation,
a long computational time can be necessary if the input
data contains many columns and many rows
(number of features).
In such a case consider limiting the number of rows
used using the size argument.
Luigi Marchionni [email protected]
Irizarry, R. A.; Warren, D.; Spencer, F.; Kim, I. F.; Biswal, S.; Frank, B. C.; Gabrielson, E.; Garcia, J. G. N.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S. C.; Hoffman, E.; Jedlicka, A. E.; Kawasaki, E.; Martinez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S. Q. and Yu, W. Multiple-laboratory comparison of microarray platforms. Nat Methods, 2005, 2, 345-350
Ross, A. E.; Marchionni, L.; Vuica-Ross, M.; Cheadle, C.; Fan, J.; Berman, D. M.; and Schaeffer E. M. Gene Expression Pathways of High Grade Localized Prostate Cancer. Prostate 2011, 71, 1568-1578
Benassi, B.; Flavin, R.; Marchionni, L.; Zanata, S.; Pan, Y.; Chowdhury, D.; Marani, M.; Strano, S.; Muti, P.; and Blandino, G. c-Myc is activated via USP2a-mediated modulation of microRNAs in prostate cancer. Cancer Discovery, 2012, March, 2, 236-247
###load data data(matchBoxExpression) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select t-statistics and merge into a new data.frame using SYMBOL mat <- mergeData(newMatchBoxExpression, idCol=idCol, byCol=byCol) ###Compute CAT for decreasing t-statistics: all genes cpH2L <- computeCat(mat, idCol=1,decreasing=TRUE, method="equalRank") ###Compute CAT for increasing t-statistics:only the first 300 genes cpL2H <- computeCat(mat, idCol=1, size=300, decreasing=FALSE, method="equalRank") ###Compute CAT for increasing t-statistics:only the first 300 genes ###use the second column as the reference cpL2H.ref <- computeCat(mat, idCol=1, size=300, ref="dataSetA.t", decreasing=FALSE, method="equalRank")###load data data(matchBoxExpression) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select t-statistics and merge into a new data.frame using SYMBOL mat <- mergeData(newMatchBoxExpression, idCol=idCol, byCol=byCol) ###Compute CAT for decreasing t-statistics: all genes cpH2L <- computeCat(mat, idCol=1,decreasing=TRUE, method="equalRank") ###Compute CAT for increasing t-statistics:only the first 300 genes cpL2H <- computeCat(mat, idCol=1, size=300, decreasing=FALSE, method="equalRank") ###Compute CAT for increasing t-statistics:only the first 300 genes ###use the second column as the reference cpL2H.ref <- computeCat(mat, idCol=1, size=300, ref="dataSetA.t", decreasing=FALSE, method="equalRank")
Prior computing proportion of overlap between ranked vector of features
it is necessary to remove the redundant features.
This can be accomplished using a number of methods implemeted
in the filterRedundant function, as explained below.
filterRedundant(object, method=c("maxORmin", "geoMean", "mean", "median","random"), idCol=1, byCol=2, absolute=TRUE, decreasing=TRUE, trim=0, ...)filterRedundant(object, method=c("maxORmin", "geoMean", "mean", "median","random"), idCol=1, byCol=2, absolute=TRUE, decreasing=TRUE, trim=0, ...)
object |
a data.frame from which redundant features (rows) must be removed. |
method |
character. The method used for removing redundancy.
Currently available methods are: |
idCol |
character or numeric. Name or index of the column containing redundant identifiers (e.g. ENTREZID, SYMBOLS, ...). |
byCol |
character or numeric. Name or index of the column
containing the ranking statistics (used only with |
absolute |
logical. Indicates whether the absolute statistics,
as defined by |
decreasing |
logical. Indicates whether reodering should be
decreasing or not (used only with |
trim |
numeric. Indicates whether a trimmed mean should
be computed (used only with |
... |
further arguments to be passed (not currently implemented). |
The maxORmin method removes
redundant features by selecting the rows
that correspond to the maximum or minimum
value of a selected statistics.
With this approach
redundant features are first
ranked in increasing or decreasing order,
as defined by the decreasing argument,
using the ranking statistics defined by byCol,
either in their original or absolute scale,
as defined by absolute argument.
Subsequently data.frame rows corresponding to redundant
identifiers are removed, after these have been identified in
the column defined by the idCol,
using the duplicated function.
The mean, median, geoMean,
and random methods provide alternative ways
for summarizing numerical values corresponding to
redundant features, as defined by the idCol
argument:
mean takes the average,
median the median,
geoMean the geometric mean,
random select a random value.
A data.frame with fewer rows with respect to the input one,
unique by the identifier specified by the idCol argument.
filterRedundant is a utility function providing various
methods to remove redundant rows from a data.frame.
The choice of the method depends on the nature of the values,
and the final goal.
Therefore caution should be used when taking the mean
or the median across few values, or passing the arguments
with the minORmax method (for instance it would
make no sense at all to use a decreasing ordering if the ranking
statistics is a p-value).
Luig Marchionni <[email protected]>
See duplicated.
###load data data(matchBoxExpression) ###check whether there are redundant identifiers sapply(matchBoxExpression,nrow) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###recheck number of rows sapply(newMatchBoxExpression, nrow)###load data data(matchBoxExpression) ###check whether there are redundant identifiers sapply(matchBoxExpression,nrow) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###recheck number of rows sapply(newMatchBoxExpression, nrow)
List of differentially expressed genes from three distinct experiments from which the identifiers and the ranking statistics to be used for computing overlap proportions will be retrieved. This type of object is the starting point of a CAT-plot analysis,
data(matchBoxExpression)data(matchBoxExpression)
This object is a list of data.frames containing at least two common columns, one for the identifiers and one for the ranking statistics. The common columns must have the same column names. In the provided example the following columns are present in each data.frame:
SYMBOL: Gene symbol column;
GENENAME: Gene name column;
ENTREZID: ENTREZ Gene identifier column;
logFC: Log2 fold-change column;
AveExpr: Average expression (A-value) column;
t: moderated t-statistics column;
P.Value: P-value column;
adj.P.Val: adjusted P-value column;
B: B-statitics (log-odds) column;
The statistics were computed using the topTable
function from limma.
Luigi Marchionni [email protected]
Irizarry, R. A.; Warren, D.; Spencer, F.; Kim, I. F.; Biswal, S.; Frank, B. C.; Gabrielson, E.; Garcia, J. G. N.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S. C.; Hoffman, E.; Jedlicka, A. E.; Kawasaki, E.; Martinez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S. Q. and Yu, W. Multiple-laboratory comparison of microarray platforms. Nat Methods, 2005, 2, 345-350
Ross, A. E.; Marchionni, L.; Vuica-Ross, M.; Cheadle, C.; Fan, J.; Berman, D. M.; and Schaeffer E. M. Gene Expression Pathways of High Grade Localized Prostate Cancer. Prostate 2011, 71, 1568-1578
Benassi, B.; Flavin, R.; Marchionni, L.; Zanata, S.; Pan, Y.; Chowdhury, D.; Marani, M.; Strano, S.; Muti, P.; and Blandino, G. c-Myc is activated via USP2a-mediated modulation of microRNAs in prostate cancer. Cancer Discovery, 2012, March, 2, 236-247
This utility function is used for merging specific columns from a set of distinct data.frames based on a specific set of identifiers. For instance this utility function can be used to retrieve from multiple data.frames the ranking statistics and the identifiers that will be used for computing the correspondence at the top curves.
mergeData(listOfDataFrames, idCol=1, byCol=2)mergeData(listOfDataFrames, idCol=1, byCol=2)
listOfDataFrames |
list. This object is a list
of distinct data.frames to be merged based on
common identifiers. The data.frames to be merged
must contain at least two common columns,
one for the identifiers (as specified by |
idCol |
character or numeris. Name or index of the column containing the common identifiers (e.g. ENTREZID, SYMBOLS, ...). |
byCol |
character or numeric . Name of index the column containing the ranking statistics. |
This function first identifies the common set of features
across all the data.frames contained in the listOfDataFrames
object. Subsequently, for this common set of features,
it returns a single data.frame containing the ranking statistics
values of choice collected from each data.frame.
A data.frame containing the identifiers and
the ranking statistics common to all data.frames
in listOfDataFrames to be used for computing
the correspondence at the top
(see Irizarry et al, Nat Methods (2005))
Luigi Marchionni [email protected]
Irizarry, R. A.; Warren, D.; Spencer, F.; Kim, I. F.; Biswal, S.; Frank, B. C.; Gabrielson, E.; Garcia, J. G. N.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S. C.; Hoffman, E.; Jedlicka, A. E.; Kawasaki, E.; Martinez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S. Q. and Yu, W. Multiple-laboratory comparison of microarray platforms. Nat Methods, 2005, 2, 345-350
Ross, A. E.; Marchionni, L.; Vuica-Ross, M.; Cheadle, C.; Fan, J.; Berman, D. M.; and Schaeffer E. M. Gene Expression Pathways of High Grade Localized Prostate Cancer. Prostate 2011, 71, 1568-1578
Benassi, B.; Flavin, R.; Marchionni, L.; Zanata, S.; Pan, Y.; Chowdhury, D.; Marani, M.; Strano, S.; Muti, P.; and Blandino, G. c-Myc is activated via USP2a-mediated modulation of microRNAs in prostate cancer. Cancer Discovery, 2012, March, 2, 236-247
See filterRedundant.
###load data data(matchBoxExpression) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select t-statistics and merge into a new data.frame using SYMBOL mat <- mergeData(listOfDataFrames = newMatchBoxExpression, idCol = idCol, byCol = byCol) ###structure of mat str(mat)###load data data(matchBoxExpression) ###the column name for the identifiers idCol <- "SYMBOL" ###the column name for the ranking statistics byCol <- "t" ###use lapply to remove redundancy from all data.frames ###default method is "maxORmin" newMatchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select t-statistics and merge into a new data.frame using SYMBOL mat <- mergeData(listOfDataFrames = newMatchBoxExpression, idCol = idCol, byCol = byCol) ###structure of mat str(mat)
This function plots corresponding at the top (CAT) curves
using overlap proportions computed by computeCat.
A number of arguments can be used for a pretty display, and for
annotating the plot, and adding the legend
plotCat(catData, whichToPlot = 1:length(catData), preComputedPI, size=500, main="CAT-plot", minYlim=0, maxYlim=1, col, pch, lty, cex=1, lwd=1, spacePts=10, cexPts=1, legend=TRUE, legendText, where="center", legCex=1, plotLayout=layout(matrix(1:2, ncol = 2, byrow = TRUE), widths = c(0.7, 0.3)), ...)plotCat(catData, whichToPlot = 1:length(catData), preComputedPI, size=500, main="CAT-plot", minYlim=0, maxYlim=1, col, pch, lty, cex=1, lwd=1, spacePts=10, cexPts=1, legend=TRUE, legendText, where="center", legCex=1, plotLayout=layout(matrix(1:2, ncol = 2, byrow = TRUE), widths = c(0.7, 0.3)), ...)
catData |
The ouput list obtained from |
whichToPlot |
numeric vector. Indexes corresponding
to the elements of |
preComputedPI |
numeric matrix. Probability intervals
computed using the |
size |
numeric. The number of top ranking features to be displayed in the plot. |
main |
character. The title of the plot, if not provided,
|
minYlim |
numeric. The lower numeric value of the y axis, to be displayed in the plot. |
maxYlim |
numeric. The upper numeric value of the y axis, to be displayed in the plot. |
col |
character or numeric. Vector specifying colors
for CAT curves plotting. |
pch |
graphical parameter. |
lty |
graphical parameter. The type of line for the plot.
If not provided generated by default, recycled id needed.
See |
cex |
numeric. Standard graphical parameter useful
for controlling axes and title annotation size.
See |
lwd |
numeric. Standard graphical parameter useful
for controlling line size. See |
spacePts |
numeric. Specifies the interval to be used for adding point labels on the CAT curves (evenly spaced along the x axis dimention). |
cexPts |
numeric. Graphical parameter useful for controlling points size used for annotating CAT-plot lines. |
legend |
logical. Wheter a legend should be added to the plot. |
legendText |
character. A vector used for legend creation.
|
where |
character. The position of the plot where the legend
will be created; |
legCex |
numeric. Graphical parameter setting the font size for the legend text. |
plotLayout |
A layout matrix to arrange the plot
and the legend. For further details see |
... |
Other graphical parameters, currently passed
only to |
This function uses outputs from computeCat
and calcHypPI to plot the CAT curves and
add grey shades corresponding to probability intervals.
The default plot uses a pre-specified layout
with separate areas for the plot and the legend.
If not specified by the user, different points, colors and line
types are used for the different CAT curves.
If the CAT curves where computed using equal ranks
(e.g. "equalRank" was passed to the method
argument of the computeCat function),
the user has the option of adding probability intervals
to the plot. Such intervals must be pre-computed
using the calcHypPI function.
Produces an annotated CAT plot.
In order to make the "best looking" plot for your needs you must play around with graphical parameters
Luigi Marchionni [email protected]
Irizarry, R. A.; Warren, D.; Spencer, F.; Kim, I. F.; Biswal, S.; Frank, B. C.; Gabrielson, E.; Garcia, J. G. N.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S. C.; Hoffman, E.; Jedlicka, A. E.; Kawasaki, E.; Martinez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S. Q. and Yu, W. Multiple-laboratory comparison of microarray platforms. Nat Methods, 2005, 2, 345-350
Ross, A. E.; Marchionni, L.; Vuica-Ross, M.; Cheadle, C.; Fan, J.; Berman, D. M.; and Schaeffer E. M. Gene Expression Pathways of High Grade Localized Prostate Cancer. Prostate, 2011, 71, 1568-1578
Benassi, B.; Flavin, R.; Marchionni, L.; Zanata, S.; Pan, Y.; Chowdhury, D.; Marani, M.; Strano, S.; Muti, P.; and Blandino, G. c-Myc is activated via USP2a-mediated modulation of microRNAs in prostate cancer. Cancer Discovery, 2012, March, 2, 236-247
See computeCat, calcHypPI,
rainbow, par,
legend, and layout.
###load data data(matchBoxExpression) ###the column name for the identifiers and the ranking statistics idCol <- "SYMBOL" byCol <- "t" ####filter the redundant features using SYMBOL and t-statistics matchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select and merge into a matrix mat <- mergeData(matchBoxExpression, idCol=idCol, byCol=byCol) ###COMPUTE CAT cpH2L <- computeCat(mat, idCol=1, size=round(nrow(mat)/1), decreasing=TRUE, method="equalRank") ###CATplot without probability intervals par(mar=c(3,3,2,1)) plotCat(cpH2L, main="CAT-plot, decreasing t-statistics", cex=1, lwd=2, cexPts=1.5, spacePts=15, legend=TRUE, where="center", legCex=1, ncol=1) ###compute probability intervals confInt <- calcHypPI(data=mat) ###CATplot with probability intervals par(mar=c(3,3,2,1)) plotCat(cpH2L, main="CAT-plot, decreasing t-statistics, probability intevals", cex=1, lwd=2, cexPts=1.5, spacePts=15, legend=TRUE, where="center", legCex=1, ncol=1)###load data data(matchBoxExpression) ###the column name for the identifiers and the ranking statistics idCol <- "SYMBOL" byCol <- "t" ####filter the redundant features using SYMBOL and t-statistics matchBoxExpression <- lapply(matchBoxExpression, filterRedundant, idCol=idCol, byCol=byCol) ###select and merge into a matrix mat <- mergeData(matchBoxExpression, idCol=idCol, byCol=byCol) ###COMPUTE CAT cpH2L <- computeCat(mat, idCol=1, size=round(nrow(mat)/1), decreasing=TRUE, method="equalRank") ###CATplot without probability intervals par(mar=c(3,3,2,1)) plotCat(cpH2L, main="CAT-plot, decreasing t-statistics", cex=1, lwd=2, cexPts=1.5, spacePts=15, legend=TRUE, where="center", legCex=1, ncol=1) ###compute probability intervals confInt <- calcHypPI(data=mat) ###CATplot with probability intervals par(mar=c(3,3,2,1)) plotCat(cpH2L, main="CAT-plot, decreasing t-statistics, probability intevals", cex=1, lwd=2, cexPts=1.5, spacePts=15, legend=TRUE, where="center", legCex=1, ncol=1)