| Title: | Global test for groups of variables via model comparisons |
|---|---|
| Description: | The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. |
| Authors: | U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel |
| Maintainer: | Manuela Hummel <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 4.23.0 |
| Built: | 2024-07-03 05:40:24 UTC |
| Source: | https://github.com/bioc/GlobalAncova |
Simulated data consisting of 24 binary variables and a binary outcome Y with 100 observations. Names of variables associated with the outcome start with true, names of other variables start with zero.
data(bindata)data(bindata)
data(bindata) #str(bindata)data(bindata) #str(bindata)
Normalized gene expression data of 12 patients with colorectal cancer.
Samples are taken from inside the tumours. Additionally, from same patients samples are
taken from normal tissue, see colon.normal. The expression matrix is only an
exemplary subset of 1747 probe sets associated with cell proliferation.
data(colon.normal)data(colon.normal)
The format is: num [1:1747, 1:12] 8.74 10.53 8.48 12.69 8.55 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:1747] "200808_s_at" "215706_x_at" "217185_s_at" "202136_at" ... ..$ : chr [1:12] "Co10.N.E.84.F.CEL" "Co14.N.E.89.F.CEL" "Co17.N.E.1037.F.CEL" "Co1.N.E.31.F.CEL" ...
Groene, J. et al., 2006, Transcriptional census of 36 microdissected colorectal cancers yields a gene signature to distinguish UICC II and III, Int J Cancer 119(8):1829–36.
data(colon.normal) #str(colon.normal)data(colon.normal) #str(colon.normal)
Covariate data for the colon data example:
Sex of the patient.
Age of the patient.
Location of the tumour.
Histologic tumour grade.
UICC stage of colorectal carcinoma.
data(colon.pheno)data(colon.pheno)
The format is:
'data.frame':12 obs. of 5 variables:
$sex:Factor w/ 2 levels "0","1": 2 2 1 2 2 1 2 1 2 1 ...
$age:int 71 76 63 73 58 66 60 66 86 76 ...
$location:Factor w/ 2 levels "distal","proximal": 1 1 1 1 1 1 1 1 2 1 ...
$grade:Factor w/ 2 levels "2","3": 1 1 2 2 1 2 1 2 2 2 ...
$UICC.stage:Factor w/ 2 levels "2","3": 2 1 2 1 2 1 1 1 2 1 ...
Groene, J. et al., 2006, Transcriptional census of 36 microdissected colorectal cancers yields a gene signature to distinguish UICC II and III, Int J Cancer 119(8):1829–36.
data(colon.pheno) #str(colon.pheno)data(colon.pheno) #str(colon.pheno)
Normalized gene expression data of 12 patients with colorectal cancer.
Samples are taken from inside the tumours. Additionally, from same patients samples are
taken from normal tissue, see colon.normal. The expression matrix is only an
exemplary subset of 1747 probe sets associated with cell proliferation.
data(colon.tumour)data(colon.tumour)
The format is: num [1:1747, 1:12] 8.77 10.40 8.52 12.86 8.28 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:1747] "200808_s_at" "215706_x_at" "217185_s_at" "202136_at" ... ..$ : chr [1:12] "Co10.T.IT.83.F.CEL" "Co14.T.IT.88.F.CEL" "Co17.T.IT.563.F.CEL" "Co1.T.IT.30.F.CEL" ...
Groene, J. et al., 2006, Transcriptional census of 36 microdissected colorectal cancers yields a gene signature to distinguish UICC II and III, Int J Cancer 119(8):1829–36.
data(colon.tumour) #str(colon.tumour)data(colon.tumour) #str(colon.tumour)
"GAhier"
Class for storing results of hierarchical testing procedure performed by gGlobalAncova.hierarchical
## S4 method for signature 'GAhier' show(object) ## S4 method for signature 'GAhier' results(object) ## S4 method for signature 'GAhier' sigEndnodes(object, onlySingleton=FALSE) ## S4 method for signature 'GAhier' Plot.hierarchy(object, dend, col=1:2, lwd=1:2, collab, returndend=FALSE, cex.labels=1.5, ...)## S4 method for signature 'GAhier' show(object) ## S4 method for signature 'GAhier' results(object) ## S4 method for signature 'GAhier' sigEndnodes(object, onlySingleton=FALSE) ## S4 method for signature 'GAhier' Plot.hierarchy(object, dend, col=1:2, lwd=1:2, collab, returndend=FALSE, cex.labels=1.5, ...)
object |
object of class |
onlySingleton |
if |
dend |
|
col |
colors for significant and non-significant nodes and branches, respectively |
lwd |
line width for branches to non-significant and significant nodes, respectively |
collab |
vector of colors for coloring dendrogram leave labels (can be independent of significant/non-significant nodes); has to be named according to variable names; if missing, significant and non-significant variables are colored using colors defined in |
returndend |
if |
cex.labels |
size of leave labels |
... |
further graphical parameters, passed to |
clustervariables:Object of class "list" containing names of variables in each tested cluster
p.values:Object of class "list" containing p-values for each tested cluster
alpha:Object of class "numeric"; chosen global significance level; if K had been specified, this additionally contains the adjusted significance levels for the K sub-hierarchies
n.variables:Object of class "numeric"; number of variables in total
permstats:Object of class "matrixOrNULL"; if returnPermstats had been set to TRUE, this is a matrix containing individual statistics for all variables for all permutations, otherwise NULL
signature(object = "GAhier"): Show general information and significant end nodes
signature(object = "GAhier"): Get a data.frame with significant end nodes, number and names of variables included in each node and corresponding p-value
signature(object = "GAhier"): Get names of signficant end nodes
signature(object = "GAhier"): Plot hierarchy dendrogram, where significant nodes (and branches to those nodes) are highlighted
Coloring the dendrogram in Plot.hierarchy is based on functionality from the globaltest package
Manuela Hummel [email protected]
showClass("GAhier") # see examples in documentation of gGlobalAncova.hierarchicalshowClass("GAhier") # see examples in documentation of gGlobalAncova.hierarchical
Computation of a permutation test for the association between sets of variables (e.g. genes, SNPs, ...) and clinical entities. The variables can be continuous, binary, categorical, ordinal, or of mixed types. The test is carried out by comparing the deviances of the full generalized linear model and the reduced model lacking the design parameters of interest. The variable-wise models are summarized to a global test statistic for the complete set.
gGlobalAncova(data, formula.full, formula.red=~1, model.dat, Sets, sumstat=sum, perm=10000)gGlobalAncova(data, formula.full, formula.red=~1, model.dat, Sets, sumstat=sum, perm=10000)
data |
|
formula.full |
model formula for the full model |
formula.red |
model formula for the reduced model (that does not contain the terms of interest) |
model.dat |
|
Sets |
vector of names or indices of variables or list of those, defining sets of variables |
sumstat |
function for summarizing univariate test statistics; default is |
perm |
number of permutations |
A data.frame with test statistic and p-value for each tested set.
The test is fast for categorical data and categorical design variable. For other types of variables and more complex designs it is rather slow.
This work was supported by BMBF grant 01ZX1309B, Germany.
Reinhard Meister [email protected]
Manuela Hummel [email protected]
data(bindata) gGlobalAncova(bindata[,-1], formula.full = ~group, model.dat = bindata, perm = 1000)data(bindata) gGlobalAncova(bindata[,-1], formula.full = ~group, model.dat = bindata, perm = 1000)
Hierarchical testing procedure according to Meinshausen (2008) screening for groups of related variables within a hierarchy instead of screening individual variables independently. Groups are tested by the generalized GlobalAncova approach. The family-wise error rate is simultaneously controlled over all levels of the hierarchy. In order to reduce computational complexity for large hierarchies, a "short cut" is implemented, where the testing procedure is applied separately to K sub-hierarchies. The p-values are adjusted such that they are identical to the ones obtained when testing the complete hierarchy.
gGlobalAncova.hierarchical(data, H, formula.full, formula.red=~1, model.dat, sumstat=sum, alpha=0.05, K, perm=10000, returnPermstats=FALSE, permstats)gGlobalAncova.hierarchical(data, H, formula.full, formula.red=~1, model.dat, sumstat=sum, alpha=0.05, K, perm=10000, returnPermstats=FALSE, permstats)
data |
|
H |
dendrogram object specifying the hierarchy of the variables; |
formula.full |
model formula for the full model |
formula.red |
model formula for the reduced model (that does not contain the terms of interest) |
model.dat |
|
sumstat |
function for summarizing univariate test statistics; default is |
alpha |
global significance level |
K |
optional integer; if this is specified, "short cut" on hierarchical testing will be applied separately to |
perm |
number of permutations |
returnPermstats |
if |
permstats |
if variable-wise permutation statistics were calculated previously, they can be provided in order not to repeat permutation testing (but only the hierarchical prodcedure); useful e.g. if procedure is run again with different |
The hierarchical procedure starts with testing the global null hypothesis that all variables are not associated with the design of interest, and then moves down the given hierarchy testing subclusters of variables. A subcluster is only tested if the null hypothesis corresponding to its ancestor cluster could be rejected. The p-values are adjusted for multiple testing according to cluster size , where is the total number of variables and is the number of variables in cluster .
If K is specified and the procedure is split to K sub-hierarchies containing variables, p-values are additionally adjusted by , such that resulting p-values are identical to the ones obtained when testing the complete hierarchy
an object of class GAhier
Manuela Hummel [email protected]
Meinshausen N, 2008. Hierarchical testing of variable importance. Biometrika, 95(2):265
gGlobalAncova, GAhier, Plot.hierarchy
data(bindata) X <- as.matrix(bindata[,-1]) # get a hierarchy for variables dend <- as.dendrogram(hclust(dist(t(X)))) # hierarchical test set.seed(555) res <- gGlobalAncova.hierarchical(X, H = dend, formula.full = ~group, model.dat = bindata, alpha = 0.05, perm = 1000) res results(res) # get names of significant clusters sigEndnodes(res) # visualize results Plot.hierarchy(res, dend) # starting with 3 sub-hierarchies set.seed(555) res2 <- gGlobalAncova.hierarchical(X, H = dend, K = 3, formula.full = ~group, model.dat = bindata, alpha = 0.05, perm = 1000) results(res2)data(bindata) X <- as.matrix(bindata[,-1]) # get a hierarchy for variables dend <- as.dendrogram(hclust(dist(t(X)))) # hierarchical test set.seed(555) res <- gGlobalAncova.hierarchical(X, H = dend, formula.full = ~group, model.dat = bindata, alpha = 0.05, perm = 1000) res results(res) # get names of significant clusters sigEndnodes(res) # visualize results Plot.hierarchy(res, dend) # starting with 3 sub-hierarchies set.seed(555) res2 <- gGlobalAncova.hierarchical(X, H = dend, K = 3, formula.full = ~group, model.dat = bindata, alpha = 0.05, perm = 1000) results(res2)
Computation of a F-test for the association between expression values and clinical entities. In many cases a two way layout with gene and a dichotomous group as factors will be considered. However, adjustment for other covariates and the analysis of arbitrary clinical variables, interactions, gene co-expression, time series data and so on is also possible. The test is carried out by comparison of corresponding linear models via the extra sum of squares principle. Corresponding p-values, permutation p-values and/or asymptotic p-values are given.
There are three possible ways of using GlobalAncova. The general way is to define
formulas for the full and reduced model, respectively, where the formula terms correspond
to variables in model.dat.
An alternative is to specify the full model and the name of the model terms
that shall be tested regarding differential expression.
In order to make this layout compatible with the
function call in the first version of the package there is also a method where simply
a group variable (and possibly covariate information) has to be given. This is maybe
the easiest usage in cases where no 'special' effects like e.g. interactions are
of interest.
## S4 method for signature 'matrix,formula,formula,ANY,missing,missing,missing' GlobalAncova(xx, formula.full, formula.red, model.dat, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature ## 'matrix,formula,missing,ANY,missing,missing,character' GlobalAncova(xx, formula.full, model.dat,test.terms, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature 'matrix,missing,missing,missing,ANY,ANY,missing' GlobalAncova(xx, group, covars = NULL, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)## S4 method for signature 'matrix,formula,formula,ANY,missing,missing,missing' GlobalAncova(xx, formula.full, formula.red, model.dat, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature ## 'matrix,formula,missing,ANY,missing,missing,character' GlobalAncova(xx, formula.full, model.dat,test.terms, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature 'matrix,missing,missing,missing,ANY,ANY,missing' GlobalAncova(xx, group, covars = NULL, test.genes, method = c("permutation","approx","both","Fstat"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula.full |
Model formula for the full model. |
formula.red |
Model formula for the reduced model (that does not contain the terms of interest.) |
model.dat |
Data frame that contains all the variable information for each sample. |
group |
Vector with the group membership information. |
covars |
Vector or matrix which contains the covariate information for each sample. |
test.terms |
Character vector that contains names of the terms of interest. |
test.genes |
Vector of gene names or a list where each element is a vector of gene names. |
method |
p-values can be calculated permutation-based ( |
perm |
Number of permutations to be used for the permutation approach. The default is 10,000. |
max.group.size |
Maximum size of a gene set for which the asymptotic p-value is calculated. For bigger gene sets the permutation approach is used. |
eps |
Resolution of the asymptotic p-value. |
acc |
Accuracy parameter needed for the approximation. Higher values indicate higher accuracy. |
If test.genes = NULL a list with components
effect |
Name(s) of the tested effect(s) |
ANOVA |
ANOVA table |
test.result |
F-value, theoretical p-value, permutation-based and/or asymptotic p-value |
terms |
Names of all model terms |
If a collection of gene sets is provided in test.genes a matrix is returned whose columns show the number of genes, value of the
F-statistic, theoretical p-value, permutation-based and/or asymptotic p-value for each of the gene sets.
In this method, besides the expression matrix xx, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In this method, besides the expression matrix xx, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested. The basic idea
behind this method is that one can select single terms, possibly from the list of
terms provided by previous GlobalAncova output, and test them without having
to specify each time a model formula for the reduced model.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides the expression matrix xx a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Reinhard Meister [email protected]
Ulrich Mansmann [email protected]
Manuela Hummel [email protected]
with contributions from Sven Knueppel
Mansmann, U. and Meister, R., 2005, Testing differential gene expression in functional groups, Methods Inf Med 44 (3).
Plot.genes, Plot.subjects, GlobalAncova.closed, GAGO, GlobalAncova.decomp
data(vantVeer) data(phenodata) data(pathways) GlobalAncova(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes=pathways[1], method="both", perm = 100) GlobalAncova(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes=pathways[1], method="both", perm = 100) GlobalAncova(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes=pathways[1], method="both", perm = 100)data(vantVeer) data(phenodata) data(pathways) GlobalAncova(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes=pathways[1], method="both", perm = 100) GlobalAncova(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes=pathways[1], method="both", perm = 100) GlobalAncova(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes=pathways[1], method="both", perm = 100)
Three functions adapted from package globaltest to test gene sets from databases for association of the gene expression profile with a response variable. Three function are provided for Gene Ontology and for the Broad Institute's gene sets.
GAGO (xx, ..., id, annotation, probe2entrez, ontology = c("BP", "CC", "MF"), minsize=1, maxsize=Inf, multtest = c("holm", "focuslevel", "BH", "BY"), focuslevel = 10, sort = TRUE) GABroad (xx, ..., id, annotation, probe2entrez, collection, category = c("c1", "c2", "c3", "c4", "c5"), multtest = c("holm", "BH", "BY"), sort = TRUE)GAGO (xx, ..., id, annotation, probe2entrez, ontology = c("BP", "CC", "MF"), minsize=1, maxsize=Inf, multtest = c("holm", "focuslevel", "BH", "BY"), focuslevel = 10, sort = TRUE) GABroad (xx, ..., id, annotation, probe2entrez, collection, category = c("c1", "c2", "c3", "c4", "c5"), multtest = c("holm", "BH", "BY"), sort = TRUE)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. Gene names have to be included as the row names of |
... |
Arguments describing the tests to be performed are passed on to |
id |
The identifier(s) of gene sets to be tested (character vector). If omitted, tests all gene sets in the database. |
annotation |
The name of the probe annotation package for the microarray that was used, or
the name of the genome wide annotation package for the species
(e.g. org.Hs.eg.db for human). If an organism package is given, the argument
|
probe2entrez |
Use only if no probe annotation package is available. A mapping from probe identifiers to entrez gene ids. May be an environment, named list or named vector. |
multtest |
The method of multiple testing correction. Choose from: Benjamini and Hochberg FDR
control (BH); Benjamini and Yekutieli FDR control (BY) or Holm familywise error
control (holm). For |
sort |
If |
ontology |
The ontology or ontologies to be used. Default is to use all three ontologies. |
minsize |
The minimum number of probes that may be annotated to a gene set. Gene sets with fewer annotated probes are discarded. |
maxsize |
The maximum number of probes that may be annotated to a gene set. Gene sets with more annotated probes are discarded. |
focuslevel |
The focus level to be used for the focus level method. Either a vector of gene
set ids, or a numerical level. In the latter case, |
collection |
The Broad gene set collection, created by a call to
|
category |
The subcategory of the Broad collection to be tested. The default is to test all sets. |
These are utility functions to make it easier to do gene set testing of gene sets available
in gene set databases. The functions automatically retrieve the gene sets, preprocess and
select them, perform global test, do multiple testing correction, and sort the results on
the basis of their p-values.
All functions require that annotate and the appropriate annotation packages are installed.
GAGO requires the
GO.db package; GABroad requires the user to download the XML file "msigdb_v2.5.xml"
from \ http://www.broad.mit.edu/gsea/downloads.jsp, and to preprocess that file using
the getBroadSets function.
The function returns a data frame with raw and multiplicity-adjusted p-values for each gene set.
Functions GAGOand GABroad correspond to functions gtGO,
and gtBroad in package globaltest. The
difference is in the use of the GlobalAncova test instead of gt within the procedures.
Jelle Goeman: [email protected]; Jan Oosting; Manuela Hummel
Goeman, J.J. and Mansmann, U., Multiple testing on the directed acyclic graph of Gene Ontology. Bioinformatics 2008; 24(4): 537-44.
gtGO,
gtKEGG,
gtBroad,
GlobalAncova,
gt,
# see vignettes of packages GlobalAncova and globaltest and help of gtGO# see vignettes of packages GlobalAncova and globaltest and help of gtGO
There are three possible ways of using GlobalAncova. The general way is to define
formulas for the full and reduced model, respectively, where the formula terms correspond
to variables in model.dat.
An alternative is to specify the full model and the name of the model terms
that shall be tested regarding differential expression.
In order to make this layout compatible with the
function call in the first version of the package there is also a method where simply
a group variable (and possibly covariate information) has to be given. This is maybe
the easiest usage in cases where no 'special' effects like e.g. interactions are
of interest.
In this method, besides the expression matrix xx, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In this method, besides the expression matrix xx, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested. The basic idea
behind this method is that one can select single terms, possibly from the list of
terms provided by previous GlobalAncova output, and test them without having
to specify each time a model formula for the reduced model.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides the expression matrix xx a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
Computation of a closed testing procedure for several groups of genes, e.g. pathways, as an alternative of correcting for multiple testing. Starting from the pathways of interest a family of null hypotheses is created that is closed under intersection. Each null hypothesis can be rejected at a given level if it is rejected along with all hypotheses included in it.
There are three possible ways of using GlobalAncova.
Also GlobalAncova.closed can be invoked with these three alternatives.
## S4 method for signature ## 'matrix,list,formula,formula,ANY,missing,missing,missing' GlobalAncova.closed(xx, test.genes, formula.full, formula.red, model.dat, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature ## 'matrix,list,formula,missing,ANY,missing,missing,character' GlobalAncova.closed(xx, test.genes, formula.full, model.dat, test.terms, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature ## 'matrix,list,missing,missing,missing,ANY,ANY,missing' GlobalAncova.closed(xx, test.genes, group, covars = NULL, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)## S4 method for signature ## 'matrix,list,formula,formula,ANY,missing,missing,missing' GlobalAncova.closed(xx, test.genes, formula.full, formula.red, model.dat, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature ## 'matrix,list,formula,missing,ANY,missing,missing,character' GlobalAncova.closed(xx, test.genes, formula.full, model.dat, test.terms, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50) ## S4 method for signature ## 'matrix,list,missing,missing,missing,ANY,ANY,missing' GlobalAncova.closed(xx, test.genes, group, covars = NULL, previous.test, level, method = c("permutation","approx"), perm = 10000, max.group.size = 2500, eps = 1e-16, acc = 50)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
test.genes |
A list of named pathways that shall be tested, each containing vectors of gene names. |
previous.test |
The output of a call to |
level |
The global level of significance of the testing procedure. |
formula.full |
Model formula for the full model. |
formula.red |
Model formula for the reduced model (that does not contain the terms of interest). |
model.dat |
Data frame that contains all the variable information for each sample. |
group |
Vector with the group membership information. |
covars |
Vector or matrix which contains the covariate information for each sample. |
test.terms |
Character vector that contains names of the terms of interest. |
method |
Raw p-values can be calculated permutation-based ( |
perm |
Number of permutations to be used for the permutation approach. The default is 10,000. |
max.group.size |
Maximum size of a gene set for which the asymptotic p-value is calculated. For bigger gene sets the permutation approach is used. |
eps |
Resolution of the asymptotic p-value. |
acc |
Accuracy parameter needed for the approximation. Higher values indicate higher accuracy. |
A list with components
new.data |
Family of null hypotheses (vectors of genes to be tested simultaneously with |
test.results |
Test results for each pathway of interest and all hypotheses included in it. |
significant |
Names of the significant pathways. |
not.significant |
Names of the non significant pathways. |
In
this method, besides the expression matrix xx and the list of gene groups
test.genes, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In
this method, besides the expression matrix xx and the list of gene groups
test.genes, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides
the expression matrix xx and the list of gene groups
test.genes a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Reinhard Meister [email protected]
Ulrich Mansmann [email protected]
Manuela Hummel [email protected]
Marcus, R., Peritz, E. and Gabriel, K.R., 1976, On closed testing procedures with special reference to ordered analysis of variance, Biometrika 63 (3): 655–660.
GlobalAncova, Plot.genes, Plot.subjects
There are three possible ways of using GlobalAncova, use methods ? GlobalAncova
for getting more information.
Also GlobalAncova.closed can be invoked with these three alternatives.
In
this method, besides the expression matrix xx and the list of gene groups
test.genes, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In
this method, besides the expression matrix xx and the list of gene groups
test.genes, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides
the expression matrix xx and the list of gene groups
test.genes a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
Computation of a F-test for the association between expression values and clinical entities. The test is carried out by comparison of corresponding linear models via the extra sum of squares principle. In models with various influencing factors extra sums of squares can be treated with sequential and type III decomposition. Adjustment for global covariates, e.g. gene expression values in normal tissue as compared to tumour tissue, can be applied. Given theoretical p-values may not be appropriate due to correlations and non-normality. The functions are hence seen more as a descriptive tool.
GlobalAncova.decomp(xx, formula, model.dat = NULL, method = c("sequential", "type3", "all"), test.genes = NULL, genewise = FALSE, zz = NULL, zz.per.gene = FALSE)GlobalAncova.decomp(xx, formula, model.dat = NULL, method = c("sequential", "type3", "all"), test.genes = NULL, genewise = FALSE, zz = NULL, zz.per.gene = FALSE)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula |
Model formula for the linear model. |
model.dat |
Data frame that contains all the variable information for each sample. |
method |
Whether sequential or type III decomposition or both should be calculated. |
test.genes |
Vector of gene names or a list where each element is a vector of gene names. |
genewise |
Shall the sequential decomposition be displayed for each single gene in a (small) gene set? |
zz |
Global covariate, i.e. matrix of same dimensions as |
zz.per.gene |
If set to |
Depending on parameters test.genes, method and genewise ANOVA tables, or lists of ANOVA tables for each
decomposition and/or gene set, or lists with components of ANOVA tables for each gene are returned.
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Ramona Scheufele [email protected]
Reinhard Meister [email protected]
Manuela Hummel [email protected]
Urlich Mansmann [email protected]
Plot.sequential, pair.compare, GlobalAncova
data(vantVeer) data(phenodata) data(pathways) # sequential or type III decomposition GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "sequential", test.genes = pathways[1:3]) GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "type3", test.genes = pathways[1:3]) # adjustment for global covariate data(colon.tumour) data(colon.normal) data(colon.pheno) GlobalAncova.decomp(xx = colon.tumour, formula = ~ UICC.stage + sex + location, model.dat = colon.pheno, method = "all", zz = colon.normal)data(vantVeer) data(phenodata) data(pathways) # sequential or type III decomposition GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "sequential", test.genes = pathways[1:3]) GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "type3", test.genes = pathways[1:3]) # adjustment for global covariate data(colon.tumour) data(colon.normal) data(colon.pheno) GlobalAncova.decomp(xx = colon.tumour, formula = ~ UICC.stage + sex + location, model.dat = colon.pheno, method = "all", zz = colon.normal)
Pairwise comparisons of gene expression in different levels of a factor by GlobalAncova tests. The method uses the reduction in residual sum of squares obtained when two respective factor levels are set to the same level. Holm-adjusted permutation-based p-values are given.
pair.compare(xx, formula, group, model.dat = NULL, test.genes = NULL, perm = 10000)pair.compare(xx, formula, group, model.dat = NULL, test.genes = NULL, perm = 10000)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula |
Model formula for the linear model. |
group |
Factor for which pairwise comparisons shall be calculated. |
model.dat |
Data frame that contains all the variable information for each sample. |
test.genes |
Vector of gene names or a list where each element is a vector of gene names. |
perm |
Number of permutations to be used for the permutation approach. The default is 10,000. |
An ANOVA table, or list of ANOVA tables for each gene set, for the pairwise comparisons.
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Ramona Scheufele [email protected]
Reinhard Meister [email protected]
Manuela Hummel [email protected]
Urlich Mansmann [email protected]
GlobalAncova, GlobalAncova.decomp
data(vantVeer) data(phenodata) data(pathways) pair.compare(xx = vantVeer, formula = ~ grade, group = "grade", model.dat = phenodata, test.genes = pathways[1:3], perm = 100)data(vantVeer) data(phenodata) data(pathways) pair.compare(xx = vantVeer, formula = ~ grade, group = "grade", model.dat = phenodata, test.genes = pathways[1:3], perm = 100)
A list of nine cancer related pathways corresponding to the van t'Veer data. Each element contains a vector gene names corresponding to those in the data set.
data(pathways)data(pathways)
The format is: List of 9 $ androgen_receptor_signaling: chr [1:72] "AW025529" "NM_001648" "NM_001753" "NM_003298" ...$ apoptosis : chr [1:187] "AB033060" "NM_002341" "NM_002342" "AI769763" ... $ cell_cycle_control : chr [1:31] "NM_001759" "NM_001760" "NM_001786" "NM_001789" ... $ notch_delta_signalling : chr [1:34] "NM_002405" "AL133036" "NM_003260" "NM_004316" ... $ p53_signalling : chr [1:33] "NM_002307" "NM_002392" "NM_003352" "NM_002745" ...$ ras_signalling : chr [1:266] "D25274" "AI033397" "NM_003029" "NM_001626" ...$ tgf_beta_signaling : chr [1:82] "NM_003036" "AI090812" "AI697699" "AI760298" ... $ tight_junction_signaling : chr [1:326] "D25274" "AA604213" "AF018081" "NM_003005" ... $ wnt_signaling : chr [1:176] "AB033058" "AB033087" "NM_003012" "NM_003014" ...
data(pathways) #str(pathways)data(pathways) #str(pathways)
Covariate data for the van t'Veer example:
Sample number.
Development of distant metastases within five years (0-no/1-yes).
Tumor grade (three ordere levels).
Estrogen receptor status (pos-positive/neg-negative).
data(phenodata)data(phenodata)
The format is:
'data.frame':96 obs. of 4 variables:
$Sample:int 1 2 3 4 5 6 7 8 9 10 ...
$metastases:int 0 0 0 0 0 0 0 0 0 0 ...
$grade:int 2 1 3 3 3 2 1 3 3 2 ...
$ERstatus:Factor w/ 2 levels "neg","pos": 2 2 1 2 2 2 2 1 2 2 ...
data(phenodata) #str(phenodata)data(phenodata) #str(phenodata)
Plot that combines Plot.genes and Plot.sequential into one graphic.
Plot.all(xx, formula, model.dat = NULL, test.genes = NULL, name.geneset = "")Plot.all(xx, formula, model.dat = NULL, test.genes = NULL, name.geneset = "")
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula |
Model formula for the linear model. |
model.dat |
Data frame that contains all the variable information for each sample. |
test.genes |
Vector of gene names or gene indices specifying a gene set. |
name.geneset |
Name of the plotted geneset. |
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Ramona Scheufele [email protected]
Reinhard Meister [email protected]
Manuela Hummel [email protected]
Urlich Mansmann [email protected]
Plot.genes, Plot.sequential, GlobalAncova.decomp, GlobalAncova
data(vantVeer) data(phenodata) data(pathways) Plot.all(vantVeer, formula = ~ ERstatus + metastases + grade, model.dat = phenodata, test.genes = pathways[[3]], name.geneset = "cell cycle pathway")data(vantVeer) data(phenodata) data(pathways) Plot.all(vantVeer, formula = ~ ERstatus + metastases + grade, model.dat = phenodata, test.genes = pathways[[3]], name.geneset = "cell cycle pathway")
Produces a plot to show the influence of individual variables on the test result produced by gGlobalAncova. The variables can be continuous, binary, categorical, ordinal, or of mixed types.
Plot.features(data, formula.full, formula.red = ~1, model.dat, Set, returnValues = FALSE, ...)Plot.features(data, formula.full, formula.red = ~1, model.dat, Set, returnValues = FALSE, ...)
data |
|
formula.full |
model formula for the full model |
formula.red |
model formula for the reduced model (that does not contain the terms of interest) |
model.dat |
|
Set |
optional vector of names or indices of variables, defining the set of variables to plot; if missing, all variables in |
returnValues |
shall variable-wise statistics = bar heights be returned? |
... |
graphical parameters passed to |
If returnValues = TRUE, a vector with the bar heights is returned.
Manuela Hummel [email protected]
data(bindata) Plot.features(bindata[,-1], formula.full = ~group, model.dat = bindata)data(bindata) Plot.features(bindata[,-1], formula.full = ~group, model.dat = bindata)
Produces a plot to show the influence of individual genes on the test result produced by GlobalAncova.
There are three possible ways of using GlobalAncova.
Also Plot.genes can be invoked with these three alternatives.
## S4 method for signature 'matrix,formula,formula,ANY,missing,missing,missing' Plot.genes(xx, formula.full, formula.red, model.dat, group, covars = NULL,test.terms,test.genes, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature ## 'matrix,formula,missing,ANY,missing,missing,character' Plot.genes(xx, formula.full, formula.red, model.dat, group, covars = NULL,test.terms,test.genes, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature 'matrix,missing,missing,missing,ANY,ANY,missing' Plot.genes(xx,formula.full, formula.red, model.dat, group, covars = NULL,test.terms,test.genes, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, bar.names, ...)## S4 method for signature 'matrix,formula,formula,ANY,missing,missing,missing' Plot.genes(xx, formula.full, formula.red, model.dat, group, covars = NULL,test.terms,test.genes, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature ## 'matrix,formula,missing,ANY,missing,missing,character' Plot.genes(xx, formula.full, formula.red, model.dat, group, covars = NULL,test.terms,test.genes, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature 'matrix,missing,missing,missing,ANY,ANY,missing' Plot.genes(xx,formula.full, formula.red, model.dat, group, covars = NULL,test.terms,test.genes, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, bar.names, ...)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula.full |
Model formula for the full model. |
formula.red |
Model formula for the reduced model (that does not contain the terms of interest.) |
model.dat |
Data frame that contains all the variable information for each sample. |
group |
Vector with the group membership information. |
covars |
Vector or matrix which contains the covariate information for each sample. |
test.terms |
Character vector that contains names of the terms of interest. |
test.genes |
Vector of gene names or gene indices specifying the gene set. If missing, the plot refers to all genes in |
Colorgroup |
Character variable giving the group that specifies coloring.
If the function is called using the argument |
legendpos |
Position of the legend (a single keyword from the list '"bottomright"', '"bottom"', '"bottomleft"', '"left"', '"topleft"', '"top"', '"topright"', '"right"' and '"center"'). |
returnValues |
Shall bar heights (gene-wise reduction in sum of squares) be returned? |
bar.names |
Vector of bar labels. If missing, gene names from |
... |
Graphical parameters for specifying colors, titles etc. |
In this method, besides the expression matrix xx, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In this method, besides the expression matrix xx, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides the expression matrix xx a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Reinhard Meister [email protected]
Ulrich Mansmann [email protected]
Manuela Hummel [email protected]
GlobalAncova, Plot.subjects, Plot.sequential
data(vantVeer) data(phenodata) data(pathways) Plot.genes(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.genes(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.genes(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes = pathways[[3]])data(vantVeer) data(phenodata) data(pathways) Plot.genes(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.genes(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.genes(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes = pathways[[3]])
There are three possible ways of using GlobalAncova, use methods ? GlobalAncova
for getting more information.
Also Plot.genes can be invoked with these three alternatives.
In this method, besides the expression matrix xx, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In this method, besides the expression matrix xx, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides the expression matrix xx a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
Plot to show the sum of squares decomposition for each gene into parts according to all variables.
Plot.sequential(xx, formula, model.dat = NULL, test.genes = NULL, name.geneset = "")Plot.sequential(xx, formula, model.dat = NULL, test.genes = NULL, name.geneset = "")
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula |
Model formula for the linear model. |
model.dat |
Data frame that contains all the variable information for each sample. |
test.genes |
Vector of gene names or gene indices specifying a gene set. |
name.geneset |
Name of the plotted geneset. |
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Ramona Scheufele [email protected]
Reinhard Meister [email protected]
Manuela Hummel [email protected]
Urlich Mansmann [email protected]
GlobalAncova.decomp, Plot.genes, GlobalAncova
data(vantVeer) data(phenodata) data(pathways) Plot.sequential(vantVeer, formula = ~ ERstatus + metastases + grade, model.dat = phenodata, test.genes = pathways[[3]], name.geneset = "cell cycle pathway")data(vantVeer) data(phenodata) data(pathways) Plot.sequential(vantVeer, formula = ~ ERstatus + metastases + grade, model.dat = phenodata, test.genes = pathways[[3]], name.geneset = "cell cycle pathway")
Produces a plot to show the influence of the samples on the test result produced by GlobalAncova.
There are three possible ways of using GlobalAncova.
Also Plot.subjects can be invoked with these three alternatives.
## S4 method for signature 'matrix,formula,formula,ANY,missing,missing,missing' Plot.subjects(xx, formula.full, formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature ## 'matrix,formula,missing,ANY,missing,missing,character' Plot.subjects(xx, formula.full,formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature 'matrix,missing,missing,missing,ANY,ANY,missing' Plot.subjects(xx, formula.full, formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...)## S4 method for signature 'matrix,formula,formula,ANY,missing,missing,missing' Plot.subjects(xx, formula.full, formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature ## 'matrix,formula,missing,ANY,missing,missing,character' Plot.subjects(xx, formula.full,formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...) ## S4 method for signature 'matrix,missing,missing,missing,ANY,ANY,missing' Plot.subjects(xx, formula.full, formula.red, model.dat, group,covars = NULL, test.terms,test.genes, Colorgroup = NULL, sort = FALSE, legendpos = "topright", returnValues = FALSE, bar.names, ...)
xx |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |
formula.full |
Model formula for the full model. |
formula.red |
Model formula for the reduced model (that does not contain the terms of interest.) |
model.dat |
Data frame that contains all the variable information for each sample. |
group |
Vector with the group membership information. |
covars |
Vector or matrix which contains the covariate information for each sample. |
test.terms |
Character vector that contains names of the terms of interest. |
test.genes |
Vector of gene names or gene indices specifying the gene set. If missing, the plot refers to all genes in |
Colorgroup |
Character variable giving the group that specifies coloring.
If the function is called using the argument |
sort |
Should the samples be ordered by |
legendpos |
Position of the legend (a single keyword from the list '"bottomright"', '"bottom"', '"bottomleft"', '"left"', '"topleft"', '"top"', '"topright"', '"right"' and '"center"'). |
returnValues |
Shall bar heights (subject-wise reduction in sum of squares) be returned? |
bar.names |
Vector of bar labels. If missing, column names of |
... |
Graphical parameters for specifying colors, titles etc. |
In this method, besides the expression matrix xx, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In this method, besides the expression matrix xx, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides the expression matrix xx a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.
Reinhard Meister [email protected]
Ulrich Mansmann [email protected]
Manuela Hummel [email protected]
GlobalAncova, Plot.genes, Plot.sequential
data(vantVeer) data(phenodata) data(pathways) Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.subjects(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes = pathways[[3]])data(vantVeer) data(phenodata) data(pathways) Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.subjects(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases") Plot.subjects(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes = pathways[[3]])
There are three possible ways of using GlobalAncova, use methods ? GlobalAncova
for getting more information.
Also Plot.subjects can be invoked with these three alternatives.
In this method, besides the expression matrix xx, model formulas for the full
and reduced model and a data frame model.dat specifying corresponding model
terms have to be given. Terms that are included in the full but not in the reduced
model are those whose association with differential expression will be tested.
The arguments group, covars and test.terms are '"missing"'
since they are not needed for this method.
In this method, besides the expression matrix xx, a model formula for the full
model and a data frame model.dat specifying corresponding model
terms are required. The character argument test.terms names the terms of interest
whose association with differential expression will be tested.
The arguments formula.red, group and covars are '"missing"'
since they are not needed for this method.
Besides the expression matrix xx a clinical variable group is
required. Covariate adjustment is possible via the argument covars but
more complex models have to be specified with the methods described above.
This method emulates the function call in the first version of the package.
The arguments formula.full, formula.red, model.dat and
test.terms are '"missing"' since they are not needed for this method.
Normalized gene expression data for the van t'Veer example:
A subset of 96 samples without BRCA1 or BRCA2 mutations and 1113 genes associated with nine
cancer related pathways (see also ?pathways) was chosen.
data(vantVeer)data(vantVeer)
The format is: num [1:1113, 1:96] 0.13 0.936 -0.087 0.118 0.168 -0.081 0.023 -0.086 -0.154 0.025 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:1113] "AW025529" "NM_001648" "NM_001753" "NM_003298" ... ..$ : chr [1:96] "1" "2" "3" "4" ...
data(vantVeer) #str(vantVeer)data(vantVeer) #str(vantVeer)