kda.finish.summarize {Mergeomics} | R Documentation |
Create a summary file of top key drivers. The file includes the key driver of each block of the dataset and their p-values.
kda.finish.summarize(res, job)
res |
the data frame including the p-values, false discovery rates, and fold
scores of the nodes obtained from |
job |
the data frame including the path of output file which will briefly contain top key drivers of the blocks and ranked p-values of those top key drivers |
kda.finish.summarize
determines the ranking scores of blocks,
finds the top node for each block, selects and saves top key drivers, and
stores P-values into file. top drovers of the blocks are also returned to
the user.
res |
data frame including top node for each block |
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.trim
## 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) ## Create a summary file of top hit KDs. # res <- kda.finish.summarize(res, job.kda) # } ## See kda.analyze() and kda.finish() for details