| Title: | Qualitative biclustering algorithm for expression data analysis in R |
|---|---|
| Description: | This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. |
| Authors: | Jitao David Zhang [aut, cre, ctb]
|
| Maintainer: | Jitao David Zhang <[email protected]> |
| License: | GPL-2 |
| Version: | 1.51.0 |
| Built: | 2024-07-03 05:48:03 UTC |
| Source: | https://github.com/bioc/rqubic |
QUBIC is a qualitative biclustering algorithm for high-throughput
expression data analysis. rqubic package implements this
algorithm in R, partly with the codes contributed by Haibao Tang and Qin Ma (version 0.23 released without
any limitation).
The rqubic package also provides parsers for the command
line tool of qubic written in C.
| Package: | rqubic |
| Type: | Package |
| Version: | 1.5 |
| Date: | 2011-04-11 |
| License: | LGPL-2 |
| LazyLoad: | yes |
Part of the source code in C is modified from the source code of the QUBIC command line tool (in C) provided by Haibao Tang and Qin Ma [email protected], downloaded from http://csbl.bmb.uga.edu/~maqin/bicluster/ on 01.03.2011, version 0.23.
Source code of QUBIC also uses open-source data structure library codes. See the README file included in the QUBIC command line tool source.
Jitao David Zhang <[email protected]>, Laura Badi and Martin Ebeling Maintainer: Jitao David Zhang <[email protected]>
Li et al. (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data Nucleic Acids Research 37:e101
Combining two or more Biclust objects into one object. These
objects must be of same dimension, namely have same numbers of
features and samples, although the numbers of biclusters do not have
to be the same (and usually are not).
signature(x = "Biclust", y = "Biclust")Method for any Biclust objects
signature(x = "QUBICBiclusterSet", y =
"QUBICBiclusterSet")Method for QUBICBiclusterSet
only. Besides combining biclusters, they will also combine parameters
and information stored in each QUBICBiclusterSet into the
returning object.
signature(x = "list", y =
"missing")Method for a list of Biclust or
QUBICBiclusterSet objects.
data(sample.ExpressionSet, package="Biobase") re1.discret <- quantileDiscretize(sample.ExpressionSet, rank=1L) re1.sel.seeds <- generateSeeds(re1.discret, minColWidth=2L) re1.blocks <- quBicluster(re1.sel.seeds, re1.discret, report.no=50L, filter.proportion=0.1) re2.discret <- quantileDiscretize(sample.ExpressionSet, rank=2L) re2.sel.seeds <- generateSeeds(re2.discret, minColWidth=2L) re2.blocks <- quBicluster(re2.sel.seeds, re2.discret, report.no=50L, filter.proportion=0.1) re3.discret <- quantileDiscretize(sample.ExpressionSet, rank=3L) re3.sel.seeds <- generateSeeds(re3.discret, minColWidth=2L) re3.blocks <- quBicluster(re2.sel.seeds, re2.discret, report.no=50L, filter.proportion=0.1) re12.blocks <- combineBiclusts(re1.blocks, re2.blocks) re123.blocks <- combineBiclusts(re1.blocks, re2.blocks, re3.blocks) re123.list.blocks <- combineBiclusts(list(re1.blocks, re2.blocks, re3.blocks)) stopifnot(identical(re123.blocks, re123.list.blocks))data(sample.ExpressionSet, package="Biobase") re1.discret <- quantileDiscretize(sample.ExpressionSet, rank=1L) re1.sel.seeds <- generateSeeds(re1.discret, minColWidth=2L) re1.blocks <- quBicluster(re1.sel.seeds, re1.discret, report.no=50L, filter.proportion=0.1) re2.discret <- quantileDiscretize(sample.ExpressionSet, rank=2L) re2.sel.seeds <- generateSeeds(re2.discret, minColWidth=2L) re2.blocks <- quBicluster(re2.sel.seeds, re2.discret, report.no=50L, filter.proportion=0.1) re3.discret <- quantileDiscretize(sample.ExpressionSet, rank=3L) re3.sel.seeds <- generateSeeds(re3.discret, minColWidth=2L) re3.blocks <- quBicluster(re2.sel.seeds, re2.discret, report.no=50L, filter.proportion=0.1) re12.blocks <- combineBiclusts(re1.blocks, re2.blocks) re123.blocks <- combineBiclusts(re1.blocks, re2.blocks, re3.blocks) re123.list.blocks <- combineBiclusts(list(re1.blocks, re2.blocks, re3.blocks)) stopifnot(identical(re123.blocks, re123.list.blocks))
This function is implemented to automatically validate and choose feature (sample) names from the user input. This function is exported for the purpose of easing other Bioconductor developers performing the similar job, and is not tended to be called by end-user directly.
eSetDimName(eset, input, type = c("feature", "sample"))eSetDimName(eset, input, type = c("feature", "sample"))
eset |
An object of |
input |
The user input, see details below |
type |
Either ‘feature’ or ‘sample’, indicating which dimension should be determined |
The input can be one of the following three possibilities:
Missing. Depending on the type, the results of calling
featureNames (“feature”) or
sampleNames (“sample”) on the eset object
will be returned.
A character string of length 1. Depending on the type, it is
first to be machted to the column names of either
fData or pData results of the
eset object. If found, the values in that column are returned
(coerced to characters if necessary). If not found, the function
stops by raising an error.
A character vector of the length equal to one of the two dimensions of the eset. In this scenario, the function only validates the equality of the length, coerces the input into characters, and return them.
If none of the scenarios above was met, the function stops by raising an error.
A vector of characters, the length of which determined by the dimension of the input object.
A special case arises if one of the dimensions of the eset
object is : In this case, the input value is interpreted as the
new name and returned. No column name match will take place in this case.
Jitao David Zhang <[email protected]>
sampleNames, featureNames,
fData, pData
writeQubicInputFile calls the function.
data(sample.ExpressionSet, package="Biobase") sub.eset <- sample.ExpressionSet[1:3, 1:3] ## usage one: eSetDimName(sub.eset, type="feature") eSetDimName(sub.eset, type="sample") ## usage two ## "sex" is one column in the pData(sub.eset) eSetDimName(sub.eset, input="sex", type="sample") ## Not run: eSetDimName(sub.eset, input="foo", type="sample") ## usage three eSetDimName(sub.eset, input=paste("Sample", 1:3), type="sample") ## Not run: eSetDimName(sub.eset, input=paste("Sample", 1:4), type="sample") ## End(Not run) ## special case: dim equals to one eSetDimName(sub.eset[,1], input="foo", type="sample")data(sample.ExpressionSet, package="Biobase") sub.eset <- sample.ExpressionSet[1:3, 1:3] ## usage one: eSetDimName(sub.eset, type="feature") eSetDimName(sub.eset, type="sample") ## usage two ## "sex" is one column in the pData(sub.eset) eSetDimName(sub.eset, input="sex", type="sample") ## Not run: eSetDimName(sub.eset, input="foo", type="sample") ## usage three eSetDimName(sub.eset, input=paste("Sample", 1:3), type="sample") ## Not run: eSetDimName(sub.eset, input=paste("Sample", 1:4), type="sample") ## End(Not run) ## special case: dim equals to one eSetDimName(sub.eset[,1], input="foo", type="sample")
Filter Biclusters by feature and concition counts. Biclusters with fewer features or conditions than specified thresholds are removed.
fcFilter(object, ...)fcFilter(object, ...)
object |
A |
... |
Two parameters are accepted: |
A Biclust object.
Jitao David Zhang <[email protected]>
data(sample.ExpressionSet, package="Biobase") rqubic.example.discret <- quantileDiscretize(sample.ExpressionSet, rank=2L) rqubic.example.sel.seeds <- generateSeeds(rqubic.example.discret, minColWidth=2L) rqubic.example.blocks <- quBicluster(rqubic.example.sel.seeds, rqubic.example.discret, report.no=200L, filter.proportion=0.1) print(rqubic.example.blocks) print(fcFilter(rqubic.example.blocks,feat.min=10)) print(fcFilter(rqubic.example.blocks,cond.min=2)) print(fcFilter(rqubic.example.blocks,feat.min=10, cond.min=2))data(sample.ExpressionSet, package="Biobase") rqubic.example.discret <- quantileDiscretize(sample.ExpressionSet, rank=2L) rqubic.example.sel.seeds <- generateSeeds(rqubic.example.discret, minColWidth=2L) rqubic.example.blocks <- quBicluster(rqubic.example.sel.seeds, rqubic.example.discret, report.no=200L, filter.proportion=0.1) print(rqubic.example.blocks) print(fcFilter(rqubic.example.blocks,feat.min=10)) print(fcFilter(rqubic.example.blocks,cond.min=2)) print(fcFilter(rqubic.example.blocks,feat.min=10, cond.min=2))
Feature-Condition Filter for biclusters
signature(object = "Biclust")Use help("fcFilter") see help and examples
Generic function features and conditions, as well as auxillary
count functions, are implemented for
Biclust objects.
They can be used in one of the following forms:
Used on a Biclust, and without
specifying index, features or conditions returns the unique and ordered
features or conditions involved in at least one bicluster, and
featureCount or conditionCount returns the length of
repsective vectors. To get the feature/condition numbers in each
bicluster of the set, use
BCfeatureCount/BCconditionCount.
Used on a Biclust and provided
index (indices), the features/conditions or their counts are
returned for specified biclusters.
In addition, featureNames and sampleNames
are of the same implementation as features and
conditions.
signature(object = "QUBICBicluster")Information about all the biclusters.
signature(object = "Biclust", index = "missing")Information about all the biclusters in the set.
signature(object = "Biclust", index = "ANY")Information about selected biclusters in the set, the index can be integers or logical variables for subsetting.
Jitao David Zhang <[email protected]>
Guojun Li, Qin Ma, Haibao Tang, Andrew H. Paternson and Ying Xu (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Research, 37:e101
See other methods implemented for the Biclust
class in the biclust package. And the methods specific for
QUBICBiclusterSet.
library(Biobase) library(biclust) example.file <- system.file("extdata", "sampleExpressionSet.blocks", package="rqubic") example.block <- parseQubicBlocks(example.file) head(features(example.block)) featureCount(example.block) head(conditions(example.block)) conditionCount(example.block) BCfeatureCount(example.block) BCfeatures(example.block)[1:2] BCconditionCount(example.block) BCconditions(example.block)[1:2] head(featureNames(example.block)) head(sampleNames(example.block))library(Biobase) library(biclust) example.file <- system.file("extdata", "sampleExpressionSet.blocks", package="rqubic") example.block <- parseQubicBlocks(example.file) head(features(example.block)) featureCount(example.block) head(conditions(example.block)) conditionCount(example.block) BCfeatureCount(example.block) BCfeatures(example.block)[1:2] BCconditionCount(example.block) BCconditions(example.block)[1:2] head(featureNames(example.block)) head(sampleNames(example.block))
generateSeeds takes either matrix or
an ExpressionSet object to generate seeds. Seeds
are defined as pairs of genes (edges) which share coincident
expression levels in samples. The higher the coincidence, the higher
the score of the seeds will be. The seeds are generated by subsequent
comparing each pair of genes. When all seeds have been produced, they
are sorted by the coincidence scores and returned as an object. See
the details section for notes on implementation.
In the rqubic package, generateSeeds currently supports
two data types: ExpressionSet (an inherited type
of eSet, or numeric matrix.
Both methods requires in addition a parameter, minColWidth,
specifying the minimum number of conditions shared by the two genes of
each seed. Its default value is 2. When this default value is used,
the minimum coincidence score is defined as ,
where represents the number of conditions. When a
non-default value is provided, the value is used to select seeds.
signature(object = "eSet")An object representing
expression data. Note that the exprs must be a matrix of
integers, otherwise the method warns and coerces the storage mode
of matrix into integer.
signature(object = "matrix")A matrix of integers. In case filled by non-integers, the method warns and coerces the storage mode into integer
The function compares all pairs of genes, namely all edges of a complete graph composed by genes. The weight of each edge is defined as the number of samples, in which two genes have the same expression level. This weight, also known as the coincidence score, reflects the co-regulation relationship between two genes.
The seed is chosen by picking edges with higher scores than the
minimum score, provided by the minColWidth parameter (default:
2).
To implement such a selection algorithm, a Fibonacci heap is constructed in the C codes. Its size is predefined as a constant, which should be reduced in case the gene number is too large to run the algorithm. A new seed, which was selected by having a higher coincidence score than the minimum, is inserted to the heap. And dependent on whether the heap is full or not, it is either inserted by squeezing the minimum seed out, or put into the heap directly.
Once the heap is filled by examining all pairs of genes, it is dumped
into an array of edge pointers, with decreasingly ordered edge
pointers by their scores. This array is captured as an external
pointer, attached as an attribute of an rqubicSeeds object.
An rqubicSeeds object holds an integer, which records the
height of the heap. It has (besides the class identifier) two
attributes: one for the external pointer, and the other one for the
threshold of the coincidence score.
In the rqubic implementation, the variable arr_c[i][j]
holds the level symbols ( in the default case), whereas in
the QUBIC implementation, this variable holds the index of
level symbols, and the level symbols are saved in the global variable
symbols.
Jitao David Zhang <[email protected]>
data(sample.ExpressionSet, package="Biobase") sample.disc <- quantileDiscretize(sample.ExpressionSet) sample.seeds <- generateSeeds(sample.disc) sample.seeds ## with higher threshold of incidence score sample.seeds.higher <- generateSeeds(sample.disc, minColWidth=5) sample.seeds.higherdata(sample.ExpressionSet, package="Biobase") sample.disc <- quantileDiscretize(sample.ExpressionSet) sample.seeds <- generateSeeds(sample.disc) sample.seeds ## with higher threshold of incidence score sample.seeds.higher <- generateSeeds(sample.disc, minColWidth=5) sample.seeds.higher
These functions parse output files of the QUBIC command line tool developed by Ma et al.
parseQubicRules(filename) parseQubicChars(file, check.names=FALSE, ...) parseQubicBlocks(filename)parseQubicRules(filename) parseQubicChars(file, check.names=FALSE, ...) parseQubicBlocks(filename)
filename |
Input filename |
file |
Input filename |
check.names |
logical, should the column names be checked? |
... |
other parameters passed to the |
Parse QUBIC Command Line Tool Output Files
parseQubicRules and parseQubicChars both return a data
frame.
parseQubicBlocks returns an instance of
QUBICBiclusterSet class.
Jitao David Zhang <[email protected]>
http://csbl.bmb.uga.edu/~maqin/bicluster/
getRqubicFile <- function(filename) system.file("extdata", filename, package="rqubic") ## parse QUBIC rules rule.file <- getRqubicFile("sampleExpressionSet.rules") rqubic.sample.rule <- parseQubicRules(rule.file) ## parse QUBIC chars chars.file <- getRqubicFile("sampleExpressionSet.chars") rqubic.sample.chars <- parseQubicChars(chars.file) ## parse QUBIC blocks block.file <- getRqubicFile("sampleExpressionSet.blocks") rqubic.sample.data <- parseQubicBlocks(block.file)getRqubicFile <- function(filename) system.file("extdata", filename, package="rqubic") ## parse QUBIC rules rule.file <- getRqubicFile("sampleExpressionSet.rules") rqubic.sample.rule <- parseQubicRules(rule.file) ## parse QUBIC chars chars.file <- getRqubicFile("sampleExpressionSet.chars") rqubic.sample.chars <- parseQubicChars(chars.file) ## parse QUBIC blocks block.file <- getRqubicFile("sampleExpressionSet.blocks") rqubic.sample.data <- parseQubicBlocks(block.file)
Performs recursive quantilizations on gene expression data across
samples, to quantileDiscretize gene expression matrix. The quantile parameter
q determines the estimated proportion of differentially
expressed genes (2q as for both up- and down-regulatons). The
rank parameter r determines how many discrete levels should
differentially expressed genes (or outliers) have. See details below.
quantileDiscretize(x, ...)quantileDiscretize(x, ...)
x |
It can be an object of the |
... |
Currently, the ... accepts two parameter: |
qEstimated proportion of conditions where gene is up- or
down-regulated, value between , default value is set to 0.06. By specifying q one
estimates that in 2q of all conditions, the expression value
of a gene is considered as outlier.
rankRanks (levels) of outliers, a positive integer, default is 1L. By default,
all conditions get one label for each gene in ,
representing down expression, not changing and high expression
respectively. In case , the outliers are further divided into
rank levels by applying recursive quantilization with equal
intervals.
Parameter q corresponds to the command line option -q
in the QUBIC command line tool, and the rank option corresponds to
-r.
For each gene, the algorithm applies quantile discretization first to
divide conditions into negative (lower), un-changed and positive (higher) expressions. Negative
and positive expressed conditions are considered as outliers. For
outliers in each direction, the algorithm tries to further quantileDiscretize
the expression values in case .
This second discretization step is performed by dividing the sorted
outliers into tandom groups with equal conditions. A label
is assigned to each of these tandom groups, in the following order:
for outliers with negative expression, from the most negative group to the least negative group (not the other way around!).
Similarly, for positive outliers, labels in the order of
are assigned to tandom groups from the least positive group to the most positive group.
That is, signs of labels indicate the direction of gene expression change, and the absolute value represents the quantileDiscretized rank in the outliers.
An object of the same class as the input parameter, with the
exprs slot replaced by the quantileDiscretized matrix, which is a
matrix of integer.
Note that the resulting discrete matrix of this implementation can be slighly different from the one used by the QUBIC command line tool.
The main reason for this is the internal data type: while QUBIC
uses float to represent expression matrix, we use double
to represent the matrix.
It has the advantages of interfacing to R, having higher precision and avoiding errors caused by floating presentation. It is implemented with potential larger costs of memory, however for test data sets (for example the ALL dataset with more than 120 samples and 12000 genes) the peak memory use (<100M) as well as the execution time (CPU time 0.028s) are well under control.
The differentially is especially often observed when there are many tied values. These cases however are very rare cases and we assume they will not affect the results to a large extent.
Jitao David Zhang <[email protected]>
Li et al. (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data Nucleic Acids Research 37:e101
parseQubicChars parses the quantileDiscretized matrix by the
QUBIC command line tool into a data frame.
library(Biobase) data(sample.ExpressionSet, package="Biobase") sample.disc <- quantileDiscretize(sample.ExpressionSet) exprs(sample.disc)[1:6, 1:6] ## Equivalent to pass a numeric matrix sample.mat.disc <- quantileDiscretize(exprs(sample.ExpressionSet)) sample.mat.disc[1:6, 1:6] ## Not run: identical(exprs(sample.disc),sample.mat.disc) ## with multiple ranks sample.rank3 <- quantileDiscretize(sample.ExpressionSet, rank=3) exprs(sample.rank3)[1:6, 1:6]library(Biobase) data(sample.ExpressionSet, package="Biobase") sample.disc <- quantileDiscretize(sample.ExpressionSet) exprs(sample.disc)[1:6, 1:6] ## Equivalent to pass a numeric matrix sample.mat.disc <- quantileDiscretize(exprs(sample.ExpressionSet)) sample.mat.disc[1:6, 1:6] ## Not run: identical(exprs(sample.disc),sample.mat.disc) ## with multiple ranks sample.rank3 <- quantileDiscretize(sample.ExpressionSet, rank=3) exprs(sample.rank3)[1:6, 1:6]
Object representing a set of biclusters identified by the QUBIC
algorithm. The class structure inherits the
Biclust class in the biclust package.
Created by functions parsing the output files of QUBIC command line tool, or functions calling QUBIC algorithm implementations in R.
Not intended to be created manually by end-users. However, interested
users are invited to review the source code or use the
showClass method to view the construction of the class.
See the class structure of Biclust. The slots
Parameter and Info have been filled with lists
releveant to the QUBIC algorithm, and all items should be accessed
by S4-methods to make sure the consistency.
signature(object = "QUBICBiclusterSet", index =
"missing"): Return S values of QUBIC biclusters as a vector
signature(object = "QUBICBiclusterSet", index = "numeric"): S
values of specified bicluster(s) are returned. Index is one or a
vector of integers. Non-integers will be coereced.
signature(x = "QUBICBiclusterSet", i = "ANY", j =
"missing", drop = "missing"): Returning a
subset of the current QUBICBiclusterSet.
signature(object = "Biclust", index
= "character"): return an input parameter specified by the
parameter name
signature(object = "Biclust", index
= "missing"): return a list of input parameters used by the
biclustering algorithm, for example QUBIC
signature(object = "Biclust", index
= "ANY"): return information of the biclusters. For end-users,
specific information accessors should be preferred, for example
features, conditions and Svalue
signature(object = "Biclust", index
= "missing"): return all information of the biclusters in a list. For end-users,
specific information accessors should be preferred, for example
features, conditions and Svalue
signature(object = "QUBICBiclusterSet"): showing method
Jitao David Zhang <[email protected]>
Guojun Li, Qin Ma, Haibao Tang, Andrew H. Paternson and Ying Xu (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Research, 37:e101
Biclust is the basic block accomodating
biclusters identified by the QUBIC algorithm.
showClass("QUBICBiclusterSet")showClass("QUBICBiclusterSet")
The function takes seeds and quantileDiscretized ExpressionSet as input, biclusters the data and returns an object holding biclusters. Users may control the report number of clusters, tolerance of incoherent genes (or conditions), as well as the filtering of redundant clusters.
quBicluster(seeds, eset, report.no = 100L, tolerance = 0.95, filter.proportion = 1)quBicluster(seeds, eset, report.no = 100L, tolerance = 0.95, filter.proportion = 1)
seeds |
An object of the S3-class |
eset |
Discretized expression data |
report.no |
Number of biclusters that should be reported. Detected biclusters are ranked by the S-score, which is defined by the product of gene counts and sample counts. They are ordered and the top ones are reported. |
tolerance |
Percentage of tolerated incoherent samples, 0.95 by default |
filter.proportion |
Proportion of a cluster, over which the cluster is considered as redudant. Each bicluster is compared to all better ranking biclusters, and the overlapping proportion is measured by the proportion of the product of overlapping samples and overlapping genes, to the product samples and genes. If the proportion is larger than the given threshold, the block will be considered redundant and therefore not reported. Setting the threshold to 1 (default) does not perform any filtering. |
The function calls a C routine to perform the biclustering. Currently the routine returns blocks with fewer samples specified by the minimum column number, due to the set of tolerance values. This might be changed in the fewer versions.
An object of the QUBICBiclusterSet-class, holding all biclusters.
Jitao David Zhang <[email protected]>
Li et al. (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data Nucleic Acids Research 37:e101
quantileDiscretize and generateSeeds
data(sample.ExpressionSet, package="Biobase") rqubic.example.discret <- quantileDiscretize(sample.ExpressionSet, rank=2L) rqubic.example.sel.seeds <- generateSeeds(rqubic.example.discret, minColWidth=2L) rqubic.example.blocks <- quBicluster(rqubic.example.sel.seeds, rqubic.example.discret, report.no=200L, filter.proportion=0.1) ## print features in each bicluster BCfeatures(rqubic.example.blocks)data(sample.ExpressionSet, package="Biobase") rqubic.example.discret <- quantileDiscretize(sample.ExpressionSet, rank=2L) rqubic.example.sel.seeds <- generateSeeds(rqubic.example.discret, minColWidth=2L) rqubic.example.blocks <- quBicluster(rqubic.example.sel.seeds, rqubic.example.discret, report.no=200L, filter.proportion=0.1) ## print features in each bicluster BCfeatures(rqubic.example.blocks)
This function complements the functionality of
writeBiclusterResults in the biclust package. It
constructs a Biclust object from a plain text file.
readBiclusterResults(filename, featureNames, sampleNames, delimiter = ";", ...)readBiclusterResults(filename, featureNames, sampleNames, delimiter = ";", ...)
filename |
Character, name of the file storing biclustering information |
featureNames |
Optional character vector, feature names of the underlying expression dataset. See details. |
sampleNames |
Optional character vector, sample names of the underlying expression dataset. See details. |
delimiter |
Character used to separate features, samples and counts of them. |
... |
Other parameters passed to the |
Currently output files written by the writeBiclusterResults
function does not contain original feature names or sample names in
the expression dataset from which biclusters were mined. The
featureNames and sampleNames allow to use this
information to construct a Biclust object that
has the same dimension as the original expression dataset.
A Biclust object
Jitao David Zhang <[email protected]>
In case several biclustering algorithms were applied to the same
expression dataset, they can be combined with
combineBiclusts once the results were read from plain texts.
temp <- tempfile() library(biclust) data(BicatYeast, package="biclust") res <- biclust(BicatYeast, method=BCBimax(), number=5) writeBiclusterResults(temp, res,"CC with delta 1.5",dimnames(BicatYeast)[[1]],dimnames(BicatYeast)[[2]], delimiter=";") res.back <- readBiclusterResults(temp, delimiter=";") res.full.back <- readBiclusterResults(temp,featureNames=rownames(BicatYeast), sampleNames=colnames(BicatYeast),delimiter=";")temp <- tempfile() library(biclust) data(BicatYeast, package="biclust") res <- biclust(BicatYeast, method=BCBimax(), number=5) writeBiclusterResults(temp, res,"CC with delta 1.5",dimnames(BicatYeast)[[1]],dimnames(BicatYeast)[[2]], delimiter=";") res.back <- readBiclusterResults(temp, delimiter=";") res.full.back <- readBiclusterResults(temp,featureNames=rownames(BicatYeast), sampleNames=colnames(BicatYeast),delimiter=";")
The QUBIC commmand line tool (developed by Ma et al.) requires a tab-limited data matrix as input file, with some special requirements (see details below). This function takes an object of ExpressionSet and outputs the file.
writeQubicInputFile(x, file = "", featureNames, sampleNames)writeQubicInputFile(x, file = "", featureNames, sampleNames)
x |
An object inheriting the |
file |
Filename to output, or a connection to write to (e.g. |
featureNames |
Specifies the feature names. It can be left blank,
in which case the result of calling |
sampleNames |
Specifies the sample names. It can be left blank,
in which case the result of calling |
The description of the data
format can be checked by running the QUBIC tool in the command line
mode, with the option -h (for help). A special
requirement, which makes it different from the results of the
write.table function in R, is that before the sample
names (column names), an “o” must be added.
No visible value will be returned, the function is called for its side effect.
Jitao David Zhang <[email protected]>
tmpfile <- tempfile() data(sample.ExpressionSet, package="Biobase") sub.eset <- sample.ExpressionSet[1:3, 1:3] ## write to standard output writeQubicInputFile(sub.eset) ## write to a temporary file writeQubicInputFile(sub.eset, tmpfile) head(readLines(tmpfile)) ## specify names with one column name in fData/pData writeQubicInputFile(sub.eset, file="", sampleNames="sex") ## alternatively specifiy names manually writeQubicInputFile(sub.eset, file="",sampleNames=paste("Sample", 1:3))tmpfile <- tempfile() data(sample.ExpressionSet, package="Biobase") sub.eset <- sample.ExpressionSet[1:3, 1:3] ## write to standard output writeQubicInputFile(sub.eset) ## write to a temporary file writeQubicInputFile(sub.eset, tmpfile) head(readLines(tmpfile)) ## specify names with one column name in fData/pData writeQubicInputFile(sub.eset, file="", sampleNames="sex") ## alternatively specifiy names manually writeQubicInputFile(sub.eset, file="",sampleNames=paste("Sample", 1:3))