kda.finish.trim {Mergeomics} | R Documentation |
kda.finish.trim
trims p-values, false discovery rates, and
fold scores to make them nicer to look at before saving the file. It also
returns trimmed results to the user.
kda.finish.trim(res, job)
res |
includes p-values, false discovery rates, and fold scores of the nodes |
job |
data frame including output folder path to store trimmed results |
res |
Trimmed and formatted p-values, false discovery rates, and fold scores of the nodes |
Ville-Petteri Makinen
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.
kda.finish
, kda.finish.estimate
,
kda.finish.save
, kda.finish.summarize
## get the prepared and KDA applied dataset:(see kda.analyze for details) data(job_kda_analyze) ## finish the KDA process by estimating additional measures for the modules ## such as module sizes, overlaps with hub neighborhoods, etc. # job.kda <- kda.finish(job.kda) # if (nrow(job.kda$results)==0){ # cat("No Key Driver Found!!!!") # } else{ ## Estimate additional measures - see kda.analyze and kda.finish for details # res <- kda.finish.estimate(job.kda) ## Save full results about modules such as co-hub, nodes, P-values info etc. # res <- kda.finish.save(res, job.kda) ## Create a simpler file for viewing by trimming floating numbers # res <- kda.finish.trim(res, job.kda) # } ## See kda.analyze() and kda.finish() for details