runAn | R Documentation |
This function runs the analysis of an ICA decomposition contained in an IcaSet object, according to the parameters entered by the user and contained in a MineICAParams.
runAn(params, icaSet, keepVar,
heatmapCutoff = params["selCutoff"],
funClus = c("Mclust", "kmeans"), nbClus,
clusterOn = "A", keepComp, keepSamples,
adjustBy = c("none", "component", "variable"),
typePlot = c("boxplot", "density"),
mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"),
dbGOstats = c("KEGG", "GO"), ontoGOstats = "BP",
condGOstats = TRUE,
cutoffGOstats = params["pvalCutoff"],
writeGenesByComp = TRUE, writeFeaturesByComp = FALSE,
selCutoffWrite = 2.5, runVarAnalysis = TRUE,
onlySign = T, runClustering = FALSE, runGOstats = TRUE,
plotHist = TRUE, plotHeatmap = TRUE)
params |
An object of class
|
icaSet |
An object of class
|
keepVar |
The variable labels to be considered, i.e
a subset of the annotation variables available in
( |
keepSamples |
The samples to be considered, i.e a
subset of ( |
heatmapCutoff |
The cutoff (applied to the scaled feature/gene projections contained in S/SByGene) used to select the contributing features/genes. |
funClus |
The function to be used to cluster the
samples, must be one of
|
nbClus |
The number of clusters to be computed when
applying |
keepComp |
The indices of the components to be
analyzed, must be included in |
adjustBy |
The way the p-values of the Wilcoxon and
Kruskal-Wallis tests should be corrected for multiple
testing: |
typePlot |
The type of plot used to show distribution of sample-groups contributions, either "density" or "boxplot" |
mart |
A mart object used for annotation, see
function |
dbGOstats |
The used database to use ('GO' and/or 'KEGG'), default is both. |
ontoGOstats |
A string specifying the GO ontology to
use. Must be one of 'BP', 'CC', or 'MF', see
|
condGOstats |
A logical indicating whether the
calculation should conditioned on the GO structure, see
|
cutoffGOstats |
The p-value threshold used for selecting enriched gene sets, default is params["pvalCutoff"] |
writeGenesByComp |
If TRUE (default) the gene
projections ( |
writeFeaturesByComp |
If TRUE (default) the feature
projections ( |
runGOstats |
If TRUE the enrichment analysis of the
contributing genes is run for each component using
package |
plotHist |
If TRUE the position of the sample annotations within the histograms of the sample contributions are plotted. |
plotHeatmap |
If TRUE the heatmap of the contributing features/genes are plotted for each component. |
runClustering |
If TRUE the potential associations between a clustering of the samples (performed according to the components), and the sample annotations, are tested using chi-squared tests. |
runVarAnalysis |
If TRUE the potential associations
between sample contributions (contained in
|
onlySign |
If TRUE (default), only the significant
results are plotted in functions |
selCutoffWrite |
The cutoff applied to the absolute
feature/gene projection values to select the
features/genes that will be annotated using package
|
clusterOn |
Specifies the matrix used to apply
clustering if
|
This function calls functions of the MineICA package depending on the arguments:
writeProjByComp
(if
writeGenesByComp=TRUE
or
writeFeaturesByComp
)which writes in html files the description of the features/genes contributing to each component, and their projection values on all the components.
plot_heatmapsOnSel
(if
plotHeatmap=TRUE
)which plots heatmaps of the data restricted to the contributing features/genes of each component.
plotPosAnnotInComp
(if plotHist=TRUE
)which plots, within the
histogram of the sample contribution values of every
component, the position of groups of samples formed
according to the sample annotations contained in
pData(icaSet)
.
clusterSamplesByComp
(if
runClustering=TRUE
)which clusters the samples according to each component.
clusVarAnalysis
(if
runClustering=TRUE
)which computes the
chi-squared test of association between a given
clustering of the samples and each annotation level
contained in pData(icaSet)
, and summarizes the
results in an HTML file.
runEnrich
(if runGOstats=TRUE
)which perforns enrichment analysis of the contributing genes of the components using package GOstats.
qualVarAnalysis
and
quantVarAnalysis
(if
varAnalysis=TRUE
)which tests if the groups of
samples formed according to sample annotations contained
in pData(icaSet)
are differently distributed on
the components, in terms of contribution value.
Several directories containing the results of each analysis are created by the function:
contains the annotations of the features or genes, one file per component;
contains two directories: 'qual/' and 'quant/' which respectively contain the results of the association between components qualitative and quantitative variables;
contains the heatmaps (one pdf file per component) of contributing genes by component;
contains athe histograms of sample contributions superimposed with the histograms of the samples grouped by variable;
contains the association between a
clustering of the samples performed on the mixing matrix
A
and the variables.
NULL
Anne Biton
writeProjByComp
,
## Not run:
## load an example of IcaSet
data(icaSetCarbayo)
## make sure the 'mart' attribute is correctly defined
mart(icaSetCarbayo) <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
## creation of an object of class MineICAParams
## here we use a low threshold because 'icaSetCarbayo' is already
# restricted to the contributing features/genes
params <- buildMineICAParams(resPath="~/resMineICACarbayotestRunAn/", selCutoff=2, pvalCutoff=0.05)
require(hgu133a.db)
runAn(params=params, icaSet=icaSetCarbayo)
## End(Not run)
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