![pca column 64 bit 4shared pca column 64 bit 4shared](http://2.bp.blogspot.com/-wfohADm9o5I/UkAu_4fO_pI/AAAAAAAAAYY/QxNAka4k7Vk/s1600/idm4.jpg)
In the example that we show, we will calculate the pathway \(p\)-values parametrically, by specifying numReps = 0. Even though we increased the permutations tenfold, the function completed execution less than 10 times longer (as we mentioned above, roughly a quarter of the computing time is extracting the PCs from each pathway, which does not depend on the number of permutations). If we increase the number of permutations from 1000 to 10,000, this calculation takes 222 seconds ( \(7.9\times\) longer). We will now describe the computational cost for the non-parametric approach.įor the tiny \(250 \times 656\) assay with 15 associated pathways, calculating pathway \(p\)-values with 1000 replicates completes in 28 seconds. We will adjust the \(p\)-values with the Hochberg (1988) and Sidak Step-Down FWER-adjustment procedures. We will use two of our available cores with the parallel computing approach. The remaining 70-80% of the cost will be the permutation test (for 1000 permutations). If you choose to calculate the pathway \(p\)-values non-parametrically, about 20-30% of the computing costs will be extracting the AES-PCs from each pathway (though this proportion will increase if the LARS algorithm has convergence issues with the given pathway). For even moderately-sized data sets (~2000 features) and 1000 pathways, this could take half an hour or more. You could increase the permutations to a larger value, should your computing resources allow for that. Be warned, however, that this may be too few permutations to create accurate seperation in pathway significance \(p\)-values. Therefore, when testing the relationship between the response and the PCs extracted by AES-PCA, the accuracy of the permuted \(p\)-values will depend on how many permutations you call for. This object class comes with enhanced printing methods and some other benefits.ģ.1.2 Calculate Pathway-Specific Model \(p\)-Valuesįor the AES-PCA method, pathway \(p\)-values can be calculated with a permutation test. Additionally, if you use the tidyverse package suite (and have these packages loaded), then the output will be a tibble object, rather than a data frame object. The data frame will have its rows sorted in increasing order by the adjusted \(p\)-value corresponding to the first adjustment method requested. : Additional columns for each requested FDR/FWER adjustment. rawp: The unadjusted \(p\)-values of each pathway.GetPathwa圜ollection(colon_OmicsSurv) $TERMS #> pathwa圓 #> "KEGG_PENTOSE_PHOSPHATE_PATHWAY" #> pathway60 #> "KEGG_RETINOL_METABOLISM" #> pathway87 #> "KEGG_ERBB_SIGNALING_PATHWAY" #> pathway120 #> "KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION" #> pathway176 #> "KEGG_NON_SMALL_CELL_LUNG_CANCER" #> pathway177 #> "KEGG_ASTHMA" #> pathway187 #> "BIOCARTA_RELA_PATHWAY" #> pathway266 #> "BIOCARTA_SET_PATHWAY" #> pathwa圓90 #> "BIOCARTA_TNFR1_PATHWAY" #> pathway413 #> "ST_GA12_PATHWAY" #> pathway491 #> "PID_EPHB_FWD_PATHWAY" #> pathway536 #> "PID_TNF_PATHWAY" #> pathway757 #> "REACTOME_INSULIN_RECEPTOR_SIGNALLING_CASCADE" #> pathway781 #> "REACTOME_PHOSPHOLIPID_METABOLISM" #> pathway1211 #> "REACTOME_SIGNALING_BY_INSULIN_RECEPTOR"