tabulate.top.indep.features {SIM} | R Documentation |
Lists the mean z-scores for independent features in the analyzed regions, calculated across the significant dependent features. Gives insight in the expression levels most strongly associated with copy number changes.
tabulate.top.indep.features(input.regions = "all chrs", adjust.method = c("BY", "BH", "raw"), significance = 0.2, sort.order = "positive", run.name = NULL)
input.regions |
vector indicating the regions to be analyzed. Can be defined in four
ways:
1) predefined input region: insert a predefined input region, choices are:
"all chrs" , "all chrs auto" , "all arms" , "all arms auto"
In the predefined regions "all arms" and "all arms auto" the arms 13p,
14p, 15p, 21p and 22p are left out, because in most studies there are no or few probes
in these regions. To include them, just make your own vector of arms.
2) whole chromosome(s): insert a single chromosome or a list of chromosomes as a vector
c(1, 2, 3) .
3) chromosome arms: insert a single chromosome arm or a list of chromosome arms like
c("1q", "2p", "2q") .
4) subregions of a chromosome: insert a chromosome number followed by the start and end position
like c("chr1_1-1000000")
These regions can also be combined, e.g. c("chr1_1-1000000","2q", 3) .
See details for more information. |
adjust.method |
Method used to adjust the p-values for multiple
testing. Either "BY" (recommended when copy number is used as dependent data),
"BH" or "raw" . Defaults to "BY". See SIM for more information about adjustin
g p-values. |
significance |
threshold used to select the significant dependent features. Only the z-scores with these features are used to calculate the mean z-scores across the independent features. |
sort.order |
Indicates how the z-scores are sorted, either "positive" or "negative" . |
run.name |
Name of the analysis. The results will be stored in a folder with this
name in the current working directory (use getwd() to print the current working
directory). If the run.name = NULL , the default folder "analysis_results"
will be generated. |
tabulate.top.indep.features
can only be run after integrated.analysis
with zscores=T
.
Output is a .txt file containing a table with the mean z-scores of all independent features
per analyzed region. It includes the ann.indep
columns that were read in the
assemble.data
function.
Depending on the argument "adjust.method", the p-values are first corrected for multiple testing. Next, th e z-scores are filtered to include only those entries that correspond to significant (p-value < "significa nce") dependent features to calculate the mean z-scores.
No values are returned. The results are stored in a subdirectory of run.name
as pdf.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes R.X.Menezes@lumc.nl
SIM
, assemble.data
, integrated.analysis
,
sim.plot.zscore.heatmap
, sim.plot.pvals.on.region
,
sim.plot.pvals.on.genome
, tabulate.pvals
,
tabulate.top.dep.features
, impute.nas.by.surrounding
,
sim.update.chrom.table
#load the datasets and the samples to run the integrated analysis data(expr.data) data(acgh.data) data(samples) #assemble the data assemble.data(dep.data = acgh.data, indep.data = expr.data, ann.dep = colnames(acgh.data)[1:4], ann.indep = colnames(expr.data)[1:4], dep.id="ID", dep.chr = "CHROMOSOME",dep.pos = "STARTPOS",dep.symb="Symbol",indep.id="ID",indep.chr = "CHROMOSOME", indep.pos = "STARTPOS",indep.symb="Symbol", overwrite = TRUE,run.name = "chr8") #run the integrated analysis integrated.analysis(samples = samples, input.regions = 8, adjust=FALSE, zscores=TRUE, method = "auto", run.name="chr8") #get the highest associated independent features tabulate.top.indep.features(input.regions = 8, adjust.method="BY", significance=0.2,sort.order='positive', run.name = "chr8")