tabulate.top.dep.features {SIM} | R Documentation |
Lists the integrated analysis p-values for the dependent features in the analyzed regions, together with the available annotation.
tabulate.top.dep.features(input.regions = "all chrs", adjust.method = c("BY", "BH", "raw"), run.name = NULL)
input.regions |
vector indicating the regions to be analyzed. Can be defined in four w
ays:
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. |
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. |
Output is a .txt file containing a table with sorted integrated analysis p-values of the
dependent features. It includes the ann.dep
columns that were read in the assemble.data
function.
No values are returned. The results are stored in a subdirectory of run.name
as txt.
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.indep.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 top dependent features with lowest p-value tabulate.top.dep.features(input.regions = 8, adjust.method="BY",run.name = "chr8")