Mergeomics (version 1.0.0)

kda.finish.summarize: Summarize the wKDA results

Description

Create a summary file of top key drivers. The file includes the key driver of each block of the dataset and their p-values.

Usage

kda.finish.summarize(res, job)

Arguments

res

the data frame including the p-values, false discovery rates, and fold scores of the nodes obtained from kda.finish.trim

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

Value

res

data frame including top node for each block

Details

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.

References

Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems. bioRxiv doi: http://dx.doi.org/10.1101/036012

See Also

kda.finish, kda.finish.estimate, kda.finish.save, kda.finish.trim

Examples

## 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