Function to randomly select genes associated with randomly pathways.

This function first randomly selects a number (np) of pathways, then randomly selects a number (ng) of genes that are associated with at least one of the selected pathways. The function can be used to compare Sigora's performance to traditional overrepresentation tests.

genesFromRandomPathways(GPSrepo, np, ng)

Arguments

GPSrepo	A signature repository (created by ..) or one of the precompiled options.
np	How many pathways to select.
ng	Number of genes to be selected.

Value

selectedPathways

A vector containing the "np" originally selected pathways.

genes

A vector containing the "ng" selected genes from selectedPathways.

References

Foroushani AB, Brinkman FS and Lynn DJ (2013).“Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures.”PeerJ, 1

See also

sigora-package

Examples

data('kegH')
## select 50 genes from 3 human KEGG pathways
seed=1234
set.seed(seed)
a1<-genesFromRandomPathways(kegH,3,50)
#> ### randomly selected pathways are: 
#> hsa05218 
#>  hsa03440 
#>  hsa04060 
## originally selected pathways:
a1[["selectedPathways"]]
#> [1] "hsa05218" "hsa03440" "hsa04060"
## what are the genes
a1[["genes"]]
#>  [1] "578"    "3458"   "80310"  "56034"  "27177"  "27242"  "53342"  "2660"  
#>  [9] "944"    "163702" "4233"   "657"    "5291"   "3624"   "130399" "146956"
#> [17] "8822"   "3562"   "8940"   "5196"   "4049"   "268"    "3444"   "1436"  
#> [25] "3597"   "5156"   "8792"   "282618" "3455"   "1647"   "9577"   "29949" 
#> [33] "58985"  "9518"   "8742"   "8743"   "6376"   "3439"   "673"    "3606"  
#> [41] "8793"   "2253"   "581"    "8795"   "3479"   "5617"   "56477"  "10913" 
#> [49] "90"     "3467"  
## sigora's results
sigoraRes <- sigora(GPSrepo =kegH, queryList = a1[["genes"]],
        level = 4)
#>   pathwy.id                            description    pvalues Bonferroni
#> 1  hsa04060 Cytokine-cytokine receptor interaction 2.787e-304   8.5e-302
#>   successes PathwaySize        N sample.size
#> 1    216.39    14774.33 452219.2      227.45
## compare to traditional methods results:
oraRes <- ora(a1[["genes"]],kegH)
dim(oraRes)
#> [1] 29  6
oraRes
#>    pathwyid                                              description success
#> 1  hsa04060                   Cytokine-cytokine receptor interaction      35
#> 2  hsa05218                                                 Melanoma      12
#> 3  hsa04630                               Jak-STAT signaling pathway      15
#> 4  hsa01521                EGFR tyrosine kinase inhibitor resistance       8
#> 5  hsa04151                               PI3K-Akt signaling pathway      14
#> 6  hsa05164                                              Influenza A      10
#> 7  hsa05214                                                   Glioma       7
#> 8  hsa04015                                   Rap1 signaling pathway      10
#> 9  hsa04650                Natural killer cell mediated cytotoxicity       8
#> 10 hsa05224                                            Breast cancer       8
#> 11 hsa05160                                              Hepatitis C       8
#> 12 hsa04014                                    Ras signaling pathway       9
#> 13 hsa04010                                   MAPK signaling pathway      10
#> 14 hsa04210                                                Apoptosis       7
#> 15 hsa04350                               TGF-beta signaling pathway       6
#> 16 hsa05213                                       Endometrial cancer       5
#> 17 hsa05168                                 Herpes simplex infection       8
#> 18 hsa05215                                          Prostate cancer       6
#> 19 hsa05223                               Non-small cell lung cancer       5
#> 20 hsa04217                                              Necroptosis       7
#> 21 hsa05216                                           Thyroid cancer       4
#> 22 hsa05212                                        Pancreatic cancer       5
#> 23 hsa05220                                 Chronic myeloid leukemia       5
#> 24 hsa05202                  Transcriptional misregulation in cancer       7
#> 25 hsa05210                                        Colorectal cancer       5
#> 26 hsa05162                                                  Measles       6
#> 27 hsa04550 Signaling pathways regulating pluripotency of stem cells       6
#> 28 hsa04510                                           Focal adhesion       7
#> 29 hsa05169                             Epstein-Barr virus infection       7
#>    pathwaySize   pvalues       Bonfer
#> 1          294 1.279e-38 1.803390e-36
#> 2           72 2.309e-14 3.255690e-12
#> 3          162 6.941e-14 9.786810e-12
#> 4           79 4.164e-08 5.871240e-06
#> 5          354 4.542e-08 6.404220e-06
#> 6          170 1.367e-07 1.927470e-05
#> 7           75 5.556e-07 7.833960e-05
#> 8          210 9.769e-07 1.377429e-04
#> 9          131 2.123e-06 2.993430e-04
#> 10         147 5.042e-06 7.109220e-04
#> 11         155 7.474e-06 1.053834e-03
#> 12         232 1.933e-05 2.725530e-03
#> 13         295 2.038e-05 2.873580e-03
#> 14         136 2.996e-05 4.224360e-03
#> 15          94 3.483e-05 4.911030e-03
#> 16          58 3.883e-05 5.475030e-03
#> 17         195 3.978e-05 5.608980e-03
#> 18          97 4.164e-05 5.871240e-03
#> 19          66 7.274e-05 1.025634e-02
#> 20         162 9.163e-05 1.291983e-02
#> 21          37 1.006e-04 1.418460e-02
#> 22          75 1.343e-04 1.893630e-02
#> 23          76 1.430e-04 2.016300e-02
#> 24         186 2.170e-04 3.059700e-02
#> 25          86 2.563e-04 3.613830e-02
#> 26         138 2.940e-04 4.145400e-02
#> 27         140 3.177e-04 4.479570e-02
#> 28         199 3.283e-04 4.629030e-02
#> 29         201 3.489e-04 4.919490e-02