Aim: find genes appear in overlapping modules of the 66 and 57b libraries.
before we continue to explore the genes in Magenta module (in the 66 analysis), we need to be sure that they overlap those in the matching module in the female-only analysis (57 libraries).
#over lap the two modules tables, to check if the modules contain the same genes
# to avoid confusion, i used the module colors for the 66 libs ("moduleColors" 15 modules + grey=genes with no module), and the modules numbers for the 57 libs ("moduleLabels_57" 14 modules + "zero" = genes with no module):
compareModules <- overlapTable(
moduleColors, moduleLabels_57,
na.rm = FALSE, ignore = NULL,
levels1 = NULL, levels2 = NULL)
compareModules## $countTable
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## black 7 10 1 5 5 0 1 1 9 108 0 18 0 0 6
## blue 9 2100 56 106 1 183 37 16 3 0 7 0 66 0 0
## brown 87 288 108 65 558 31 46 38 15 40 15 10 5 7 7
## cyan 2 3 0 3 6 0 0 2 1 9 0 7 0 0 2
## green 5 36 411 3 0 0 16 0 0 0 2 0 0 1 0
## greenyellow 0 0 0 0 0 0 0 1 1 4 0 59 0 0 0
## grey 82 21 24 17 4 6 7 8 8 1 5 5 4 2 1
## magenta 2 2 8 15 0 0 0 40 0 0 15 0 1 3 0
## midnightblue 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0
## pink 0 1 0 0 1 0 1 0 95 0 0 1 0 1 0
## purple 0 1 3 1 0 0 6 0 0 0 30 0 0 45 0
## red 3 0 2 11 0 1 2 163 0 0 0 2 2 0 0
## salmon 0 10 3 0 0 0 0 0 0 0 24 0 0 0 0
## tan 0 0 0 0 0 0 0 0 46 0 0 2 0 0 0
## turquoise 6 3022 127 404 1 0 10 26 8 0 15 4 2 0 0
## yellow 80 384 50 73 48 131 212 33 72 16 24 26 23 6 26
##
## $pTable
## 0 1 2 3 4
## black 1.944931e-01 1.000000e+00 0.99999908 9.928061e-01 0.98120013
## blue 1.000000e+00 1.997788e-186 1.00000000 1.000000e+00 1.00000000
## brown 1.120000e-15 1.000000e+00 0.27505652 9.992095e-01 0.00000000
## cyan 2.517280e-01 1.000000e+00 1.00000000 4.344484e-01 0.01779718
## green 9.972806e-01 1.000000e+00 0.00000000 1.000000e+00 1.00000000
## greenyellow 1.000000e+00 1.000000e+00 1.00000000 1.000000e+00 1.00000000
## grey 1.153730e-78 1.000000e+00 0.01562078 1.834318e-01 0.99810637
## magenta 6.916210e-01 1.000000e+00 0.34764024 6.781161e-04 1.00000000
## midnightblue 1.000000e+00 3.749102e-08 1.00000000 1.000000e+00 1.00000000
## pink 1.000000e+00 1.000000e+00 1.00000000 1.000000e+00 0.99819000
## purple 1.000000e+00 1.000000e+00 0.96723093 9.978422e-01 1.00000000
## red 8.919785e-01 1.000000e+00 0.99999543 7.383876e-01 1.00000000
## salmon 1.000000e+00 9.999615e-01 0.55401504 1.000000e+00 1.00000000
## tan 1.000000e+00 1.000000e+00 1.00000000 1.000000e+00 1.00000000
## turquoise 1.000000e+00 0.000000e+00 1.00000000 3.034538e-35 1.00000000
## yellow 1.600954e-14 1.000000e+00 0.99999997 8.912700e-01 0.99975378
## 5 6 7 8
## black 1.000000e+00 9.969253e-01 9.963410e-01 2.889056e-02
## blue 5.951636e-28 1.000000e+00 1.000000e+00 1.000000e+00
## brown 9.939640e-01 3.667367e-01 7.849680e-01 9.999474e-01
## cyan 1.000000e+00 1.000000e+00 3.091547e-01 5.909919e-01
## green 1.000000e+00 4.992165e-01 1.000000e+00 1.000000e+00
## greenyellow 1.000000e+00 1.000000e+00 8.801336e-01 8.103921e-01
## grey 6.655179e-01 4.645189e-01 2.870394e-01 1.193793e-01
## magenta 1.000000e+00 1.000000e+00 2.191298e-37 1.000000e+00
## midnightblue 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## pink 1.000000e+00 9.656359e-01 1.000000e+00 3.006307e-153
## purple 1.000000e+00 6.429146e-02 1.000000e+00 1.000000e+00
## red 9.985872e-01 9.862464e-01 3.329534e-236 1.000000e+00
## salmon 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## tan 1.000000e+00 1.000000e+00 1.000000e+00 4.684758e-73
## turquoise 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## yellow 1.291603e-36 1.276331e-115 8.544321e-01 6.949936e-13
## 9 10 11 12 13
## black 2.012823e-161 1.000000e+00 7.056549e-12 1.000000e+00 1.000000e+00
## blue 1.000000e+00 1.000000e+00 1.000000e+00 7.451910e-17 1.000000e+00
## brown 2.564125e-04 7.874644e-01 9.838230e-01 9.981624e-01 7.489749e-01
## cyan 5.631826e-09 1.000000e+00 2.769478e-07 1.000000e+00 1.000000e+00
## green 1.000000e+00 9.887437e-01 1.000000e+00 1.000000e+00 9.544245e-01
## greenyellow 2.622762e-02 1.000000e+00 1.779790e-110 1.000000e+00 1.000000e+00
## grey 9.682592e-01 1.203230e-01 1.123483e-01 1.330191e-01 3.517466e-01
## magenta 1.000000e+00 3.483424e-13 1.000000e+00 5.820796e-01 1.715613e-02
## midnightblue 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## pink 1.000000e+00 1.000000e+00 7.336032e-01 1.000000e+00 4.724209e-01
## purple 1.000000e+00 1.487365e-35 1.000000e+00 1.000000e+00 6.969258e-84
## red 1.000000e+00 1.000000e+00 7.033302e-01 5.611040e-01 1.000000e+00
## salmon 1.000000e+00 3.985004e-37 1.000000e+00 1.000000e+00 1.000000e+00
## tan 1.000000e+00 1.000000e+00 1.299666e-01 1.000000e+00 1.000000e+00
## turquoise 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## yellow 9.020444e-01 2.877257e-02 6.584196e-03 1.641330e-03 7.911932e-01
## 14
## black 6.305832e-05
## blue 1.000000e+00
## brown 2.928756e-01
## cyan 8.957763e-03
## green 1.000000e+00
## greenyellow 1.000000e+00
## grey 5.545167e-01
## magenta 1.000000e+00
## midnightblue 1.000000e+00
## pink 1.000000e+00
## purple 1.000000e+00
## red 1.000000e+00
## salmon 1.000000e+00
## tan 1.000000e+00
## turquoise 1.000000e+00
## yellow 1.321401e-14# using this function i was able to match modules from the analysis including all 66 libraries, and the analysis of females only (57 libraries).
# matching modules of the two analyses
# 66 all == in 57 females:
# (module number 0) MM.grey == MM.grey (module number 0)
# (module number 1) MM.turquoise == MM.turquoise (module number 1)
# (module number 2) MM.blue == MM.turquoise (module number 1)
# (module number 3) MM.brown == MM.grey (module number 0)
# (module number 4) MM.yellow == MM.red (module number 6)
# (module number 5) MM.green == MM.blue (module number 2)
# (module number 6) MM.red == MM.black (module number 7)
# (module number 7) MM.black == MM.magenta (module number 9)
# (module number 8) MM.pink == MM.pink (module number 8)
# (module number 9) MM.magenta == MM.black (module number 7)
# (module number 10) MM.purple == MM.salmon (module number 13)
# (module number 11) MM.greenyellow == MM.greenyellow (module number 11)
# (module number 12) MM.tan == MM.pink (module number 8)ma
# (module number 13) MM.salmon == MM.purple (module number 10)
# (module number 14) MM.cyan == MM.magenta (module number 9)according to “Module-Trait interaction”, Magenta module looks interesting, as it have a negative correlation with DWVa, and positive correlation with VDV2. the matching module of Magenta (66libs) is black (57libs). So we gonna overlap the black module from 57, to magenta genes, from 66, using inner_join(),to get a table containing the overlapping genes, their geneTraitSignificance (for DWVa, ARV_2 and VDV2), geneModuleMembership, and Intramodular connectivity
geneInfoMagenta_66 = data.frame(
module = moduleColors,
geneTraitSignificance_66,
geneModuleMembership_66,
IntraModCon = Alldegrees1_66$kWithin) %>%
dplyr::select(c(module, IntraModCon, MMmagenta, GS.DWVa, GS.VDV2, GS.ARV_2)) %>%
dplyr::filter(module == "magenta") %>%
rownames_to_column("genes")
geneInfoBlack_57 = data.frame(
module = moduleColors_57,
geneTraitSignificance_57,
geneModuleMembership_57,
IntraModCon = Alldegrees1_57$kWithin) %>%
dplyr::select(c(module, IntraModCon, MMblack, GS.DWVa, GS.VDV2, GS.ARV_2)) %>%
dplyr::filter(module%in% c("black")) %>%
rownames_to_column("genes")
# make a table of the overlapping genes
overlap <- inner_join(geneInfoMagenta_66, geneInfoBlack_57, by = "genes")
#change the columns names to specify from which analysis it was taken (66libs or 57libs)
overlap <- overlap %>%
dplyr::rename(module_66 = module.x,
module_57 = module.y,
IntraModCon_66 = IntraModCon.x,
IntraModCon_57 = IntraModCon.y,
GS.DWVa_66 = GS.DWVa.x,
GS.DWVa_57 = GS.DWVa.y,
GS.VDV2_66 = GS.VDV2.x,
GS.VDV2_57 = GS.VDV2.y,
GS.ARV_2_66 = GS.ARV_2.x,
GS.ARV_2_57 = GS.ARV_2.y)
nrow(overlap) ## [1] 40overlap## genes module_66 IntraModCon_66 MMmagenta GS.DWVa_66 GS.VDV2_66
## 1 111243784 magenta 11.223440 0.8695517 -0.2925204 0.6398958
## 2 111243925 magenta 4.977129 0.7149557 -0.2427469 0.4471420
## 3 111243999 magenta 4.241030 0.7307336 -0.2982548 0.4398575
## 4 111244168 magenta 17.163039 0.9124374 -0.4469089 0.7614299
## 5 111244366 magenta 8.195112 0.8114501 -0.2901975 0.5408519
## 6 111244889 magenta 9.047380 0.8363032 -0.2406085 0.4918116
## 7 111244965 magenta 11.223471 0.8127270 -0.2927762 0.6688896
## 8 111245094 magenta 5.241102 0.7606702 -0.3232085 0.6049363
## 9 111245189 magenta 6.223219 0.7316771 -0.1443773 0.7684324
## 10 111245224 magenta 19.225611 0.9438103 -0.5257100 0.6266771
## 11 111245582 magenta 10.819486 0.8437882 -0.3930408 0.5766143
## 12 111245627 magenta 13.826894 0.8747785 -0.5259656 0.4969625
## 13 111246014 magenta 15.741042 0.9034515 -0.3590890 0.6036798
## 14 111246302 magenta 8.552619 0.7739854 -0.3738306 0.5160128
## 15 111246841 magenta 13.968961 0.8861245 -0.1939304 0.7451658
## 16 111247393 magenta 3.646495 0.7145910 -0.4529772 0.6247344
## 17 111248051 magenta 5.443256 0.7283844 -0.1430254 0.7403198
## 18 111248663 magenta 13.455119 0.8688504 -0.6526831 0.5182510
## 19 111249529 magenta 18.248457 0.9232903 -0.5461694 0.5990873
## 20 111249640 magenta 10.505546 0.8062042 -0.3610908 0.6917785
## 21 111250285 magenta 5.554644 0.7489627 -0.2247173 0.6877988
## 22 111250955 magenta 4.535455 0.7238696 -0.2948846 0.6458510
## 23 111251058 magenta 18.325534 0.9211589 -0.5047920 0.5369363
## 24 111251111 magenta 13.781119 0.8587504 -0.3695021 0.6502168
## 25 111251804 magenta 15.157361 0.8791005 -0.5437127 0.5160739
## 26 111252023 magenta 13.973837 0.8661814 -0.5672668 0.6133908
## 27 111252053 magenta 14.862509 0.8963405 -0.4178445 0.5145422
## 28 111252649 magenta 12.697701 0.8870761 -0.4561971 0.5753213
## 29 111252854 magenta 17.943261 0.9222455 -0.4752507 0.6964539
## 30 111253379 magenta 3.682578 0.6949258 -0.3074540 0.5421018
## 31 111253800 magenta 16.094312 0.9034662 -0.4544488 0.5971160
## 32 111253808 magenta 13.508291 0.8650949 -0.6047886 0.5006907
## 33 111253948 magenta 5.039276 0.7178141 -0.3471731 0.6011234
## 34 111254122 magenta 11.418235 0.8774034 -0.3206842 0.6280394
## 35 111254141 magenta 13.238670 0.8526678 -0.3209055 0.7659278
## 36 111254601 magenta 8.248668 0.7797701 -0.2432382 0.7774221
## 37 111254776 magenta 13.196680 0.8765098 -0.4034354 0.6133320
## 38 111255374 magenta 14.757598 0.8823629 -0.3964158 0.6671085
## 39 111255470 magenta 16.037453 0.9044367 -0.4818122 0.6461752
## 40 111255627 magenta 6.632640 0.8014901 -0.2709482 0.4003038
## GS.ARV_2_66 module_57 IntraModCon_57 MMblack GS.DWVa_57 GS.VDV2_57
## 1 -0.129847510 black 22.198350 0.7409757 -0.1943919 0.6597310
## 2 -0.391770250 black 22.088913 0.7607896 -0.2090687 0.5748613
## 3 -0.112730602 black 11.075776 0.6563008 -0.1522141 0.4569898
## 4 -0.171302433 black 49.723289 0.8580801 -0.3877705 0.8136362
## 5 -0.358470771 black 22.937088 0.7655673 -0.1417271 0.6444956
## 6 -0.240698081 black 12.616443 0.6405715 -0.1229824 0.5257225
## 7 -0.324538421 black 52.272521 0.8857001 -0.2032580 0.7670656
## 8 0.134047618 black 8.915815 0.5596773 -0.2762196 0.5807265
## 9 -0.212014499 black 28.195801 0.7603835 -0.1371829 0.8240359
## 10 -0.224120281 black 29.806858 0.7578145 -0.4297188 0.7010731
## 11 -0.302416626 black 27.375348 0.7780039 -0.2685444 0.6611281
## 12 -0.276748922 black 30.693962 0.7879235 -0.4369329 0.6151987
## 13 -0.214597145 black 33.400666 0.7981056 -0.3258962 0.7015680
## 14 -0.163662362 black 24.511668 0.7523032 -0.3946526 0.6608939
## 15 -0.236442918 black 54.124280 0.8862224 -0.1341113 0.8185060
## 16 -0.046751586 black 11.589818 0.6705948 -0.4482936 0.6491722
## 17 0.148530942 black 32.074934 0.7986558 -0.1266740 0.7410321
## 18 -0.194953621 black 19.074242 0.6801872 -0.5747656 0.5810407
## 19 -0.329921444 black 34.749502 0.7903098 -0.4706368 0.7192397
## 20 -0.258344046 black 56.341325 0.8916551 -0.2882585 0.7901045
## 21 0.018579016 black 29.566964 0.7943232 -0.1039504 0.7123644
## 22 0.041280877 black 14.494932 0.6693379 -0.2837554 0.6675542
## 23 -0.288503815 black 27.844484 0.7489284 -0.4479580 0.6478006
## 24 -0.247056774 black 39.694638 0.8356024 -0.3193897 0.7651151
## 25 -0.337826325 black 17.486192 0.6553006 -0.4825198 0.6164134
## 26 -0.309702151 black 25.692861 0.7391902 -0.5140609 0.7157492
## 27 -0.267409457 black 15.371700 0.6452337 -0.3593588 0.5857436
## 28 -0.064013137 black 32.744145 0.7993151 -0.3623834 0.6194092
## 29 -0.282119472 black 41.119880 0.8276113 -0.4351661 0.8064760
## 30 0.260606048 black 9.704401 0.5867435 -0.2413451 0.5273003
## 31 -0.299018590 black 34.594783 0.8077115 -0.3511759 0.6996857
## 32 -0.235716934 black 15.657505 0.6563528 -0.5229291 0.5900728
## 33 -0.369352756 black 21.436117 0.7513489 -0.3130022 0.7144338
## 34 -0.240210917 black 31.362879 0.7999102 -0.2610105 0.6896842
## 35 -0.321845412 black 51.516648 0.8738305 -0.2592884 0.8677597
## 36 0.026391573 black 55.251005 0.8943184 -0.2240337 0.7831164
## 37 -0.009339968 black 44.403272 0.8433046 -0.3374515 0.6571393
## 38 -0.258446908 black 42.451929 0.8290113 -0.3530532 0.7453012
## 39 -0.134350234 black 32.857336 0.7820477 -0.4449958 0.7108208
## 40 -0.301184133 black 15.041197 0.7044843 -0.2142499 0.5115095
## GS.ARV_2_57
## 1 -0.4165116
## 2 -0.2430201
## 3 -0.1764775
## 4 -0.3362780
## 5 -0.4394230
## 6 -0.4722398
## 7 -0.3949422
## 8 -0.4222172
## 9 -0.4360924
## 10 -0.2661796
## 11 -0.3216513
## 12 -0.1085196
## 13 -0.2846728
## 14 -0.2563243
## 15 -0.4523176
## 16 -0.1961815
## 17 -0.4208976
## 18 -0.1336256
## 19 -0.2633597
## 20 -0.2238572
## 21 -0.4346871
## 22 -0.2533139
## 23 -0.2802167
## 24 -0.2908267
## 25 -0.3081682
## 26 -0.2723798
## 27 -0.3800227
## 28 -0.1398686
## 29 -0.2810398
## 30 -0.2036610
## 31 -0.2702058
## 32 -0.1969170
## 33 -0.4263867
## 34 -0.2594001
## 35 -0.4086162
## 36 -0.2423346
## 37 -0.1090063
## 38 -0.3177539
## 39 -0.2549213
## 40 -0.3069202# we have 40 overlapping genes in the magenat and balck modules, as the "overlapTable()" function computed.
# add gene annotation:
# load the annotation file:
annot_varroa <- read_csv("/Users/nuriteliash/Documents/GitHub/varroa-virus-networks/data/annot_varroa.csv", col_names = TRUE, )##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Name = col_character(),
## Accession = col_character(),
## Start = col_double(),
## Stop = col_double(),
## Strand = col_character(),
## GeneID = col_double(),
## Locus = col_character(),
## `Locus tag` = col_character(),
## `Protein product` = col_character(),
## Length = col_double(),
## `Protein Name` = col_character()
## )# remove the "LOC" from the gene name
annot_varroa$Locus <- str_replace(annot_varroa$Locus, "LOC", '')
# and change the col name to "genes", so it will the same as in the "overlap" table
colnames(annot_varroa)[which(names(annot_varroa) == "Locus")] <- "genes"
head(annot_varroa)## # A tibble: 6 x 11
## Name Accession Start Stop Strand GeneID genes `Locus tag` `Protein produc…
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr>
## 1 Un NW_01921… 3.38e7 3.39e7 - 1.11e8 1112… - XP_022672900.1
## 2 Un NW_01921… 3.38e7 3.39e7 - 1.11e8 1112… - XP_022672902.1
## 3 Un NW_01921… 3.38e7 3.39e7 - 1.11e8 1112… - XP_022672903.1
## 4 Un NW_01921… 3.38e7 3.39e7 - 1.11e8 1112… - XP_022672904.1
## 5 Un NW_01921… 3.38e7 3.39e7 - 1.11e8 1112… - XP_022672905.1
## 6 Un NW_01921… 3.38e7 3.39e7 - 1.11e8 1112… - XP_022672908.1
## # … with 2 more variables: Length <dbl>, `Protein Name` <chr>#now join by "genes" name (INNER JOIN: returns rows when there is a match in both tables)
overlap_annot <- inner_join(overlap, annot_varroa, by = "genes") %>%
dplyr::select(c(genes, module_66, module_57,
IntraModCon_66,
IntraModCon_57,
GS.DWVa_66,
GS.DWVa_57,
GS.VDV2_66,
GS.VDV2_57,
GS.ARV_2_66,
GS.ARV_2_57, Name, Accession, 'Protein product', Length, 'Protein Name'))
# and filter out duplicated genes
overlap_annot <-overlap_annot[!duplicated(overlap_annot[,'genes']),]
nrow(overlap_annot)## [1] 40write_csv(x = as.data.frame(overlap_annot), "results/overlap_annot.csv")#load the former hub-genes:
hub_old <- read_csv("/Users/nuriteliash/Documents/GitHub/varroa-virus-networks/data/hub_annot_old.csv", col_names = TRUE, ) ##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## genes = col_double(),
## virus.x = col_character(),
## `Protein Name` = col_character()
## )hub_old$genes <- as.character(hub_old$genes)
#make a data.frame of all genes with modules:
all.genes.66 <- data.frame(
module = moduleColors,
geneTraitSignificance_66,
geneModuleMembership_66,
IntraModCon = Alldegrees1_66$kWithin) %>%
dplyr::select(c(module, IntraModCon, MMmagenta, GS.DWVa, GS.VDV2, GS.ARV_2)) %>%
rownames_to_column("genes")
# how many of the hub_old genes exist in the magenta module?
#return all rows from x, and all columns from x and y.
f <- left_join(geneInfoMagenta_66, hub_old, by = "genes")
f## genes module IntraModCon MMmagenta GS.DWVa GS.VDV2 GS.ARV_2
## 1 111242876 magenta 6.304724 0.7781005 -0.55860975 0.3865490 -0.095821062
## 2 111242941 magenta 3.358378 0.6740230 -0.03559228 0.4985539 -0.373804028
## 3 111243035 magenta 7.128138 0.7715768 -0.54448573 0.2902412 -0.277404593
## 4 111243305 magenta 4.285400 0.6966181 -0.36694122 0.3647269 0.011610041
## 5 111243700 magenta 6.890664 0.7797593 -0.50750752 0.1999109 -0.380266319
## 6 111243784 magenta 11.223440 0.8695517 -0.29252038 0.6398958 -0.129847510
## 7 111243925 magenta 4.977129 0.7149557 -0.24274692 0.4471420 -0.391770250
## 8 111243999 magenta 4.241030 0.7307336 -0.29825475 0.4398575 -0.112730602
## 9 111244078 magenta 3.374151 0.6919971 -0.33533175 0.3611966 -0.208142504
## 10 111244149 magenta 16.988361 0.9304244 -0.48439634 0.4236942 -0.304095303
## 11 111244168 magenta 17.163039 0.9124374 -0.44690886 0.7614299 -0.171302433
## 12 111244366 magenta 8.195112 0.8114501 -0.29019746 0.5408519 -0.358470771
## 13 111244889 magenta 9.047380 0.8363032 -0.24060850 0.4918116 -0.240698081
## 14 111244895 magenta 2.068979 0.6492903 -0.28659303 0.3932824 -0.141030907
## 15 111244965 magenta 11.223471 0.8127270 -0.29277621 0.6688896 -0.324538421
## 16 111245018 magenta 10.260275 0.8132027 -0.41876209 0.4578334 -0.226893844
## 17 111245094 magenta 5.241102 0.7606702 -0.32320849 0.6049363 0.134047618
## 18 111245189 magenta 6.223219 0.7316771 -0.14437728 0.7684324 -0.212014499
## 19 111245224 magenta 19.225611 0.9438103 -0.52570997 0.6266771 -0.224120281
## 20 111245319 magenta 7.817929 0.8013402 -0.24848118 0.6531090 -0.067417949
## 21 111245376 magenta 4.876569 0.7354068 -0.19736208 0.2936822 -0.379812078
## 22 111245582 magenta 10.819486 0.8437882 -0.39304075 0.5766143 -0.302416626
## 23 111245627 magenta 13.826894 0.8747785 -0.52596560 0.4969625 -0.276748922
## 24 111245837 magenta 16.026146 0.9163486 -0.58327073 0.4521852 -0.127271585
## 25 111246014 magenta 15.741042 0.9034515 -0.35908905 0.6036798 -0.214597145
## 26 111246302 magenta 8.552619 0.7739854 -0.37383063 0.5160128 -0.163662362
## 27 111246491 magenta 4.731972 0.7243380 -0.27915085 0.3901429 -0.072605013
## 28 111246841 magenta 13.968961 0.8861245 -0.19393037 0.7451658 -0.236442918
## 29 111246969 magenta 17.838370 0.9346302 -0.58194475 0.4790201 -0.212437048
## 30 111247339 magenta 4.523613 0.7229202 -0.34663930 0.4761560 -0.151848387
## 31 111247393 magenta 3.646495 0.7145910 -0.45297715 0.6247344 -0.046751586
## 32 111247862 magenta 6.542614 0.7758697 -0.33731582 0.5054671 0.109744742
## 33 111248051 magenta 5.443256 0.7283844 -0.14302545 0.7403198 0.148530942
## 34 111248124 magenta 3.901441 0.6893971 -0.42204025 0.3537784 0.283866463
## 35 111248148 magenta 6.149397 0.7606247 -0.57545123 0.4151146 -0.041018485
## 36 111248568 magenta 1.829990 0.5807608 -0.26411587 0.4060202 -0.306289459
## 37 111248663 magenta 13.455119 0.8688504 -0.65268311 0.5182510 -0.194953621
## 38 111248915 magenta 4.459002 0.7267045 -0.29229990 0.3312422 -0.038655649
## 39 111249021 magenta 8.141350 0.8077766 -0.44375069 0.3634997 -0.091720769
## 40 111249201 magenta 11.098487 0.8507984 -0.50830265 0.2503012 -0.247125145
## 41 111249253 magenta 8.251518 0.8020107 -0.40969162 0.6600876 -0.324010195
## 42 111249484 magenta 4.464690 0.7055220 -0.42598659 0.3450115 -0.203629418
## 43 111249529 magenta 18.248457 0.9232903 -0.54616945 0.5990873 -0.329921444
## 44 111249604 magenta 4.735817 0.7003771 -0.45218043 0.1925725 -0.085875225
## 45 111249640 magenta 10.505546 0.8062042 -0.36109078 0.6917785 -0.258344046
## 46 111249853 magenta 12.252758 0.8805647 -0.43067943 0.3819218 -0.112479806
## 47 111250109 magenta 8.528234 0.8164129 -0.47870266 0.3976912 -0.011172649
## 48 111250180 magenta 4.102724 0.6885171 -0.23015673 0.5069783 0.097909207
## 49 111250285 magenta 5.554644 0.7489627 -0.22471728 0.6877988 0.018579016
## 50 111250305 magenta 4.684375 0.7219795 -0.32741370 0.4934716 -0.293294652
## 51 111250595 magenta 4.709594 0.7414470 -0.45882561 0.3259767 -0.080592986
## 52 111250955 magenta 4.535455 0.7238696 -0.29488456 0.6458510 0.041280877
## 53 111250993 magenta 4.629426 0.6872982 -0.23804344 0.5113298 0.248894530
## 54 111251058 magenta 18.325534 0.9211589 -0.50479197 0.5369363 -0.288503815
## 55 111251111 magenta 13.781119 0.8587504 -0.36950213 0.6502168 -0.247056774
## 56 111251195 magenta 6.102890 0.7458169 -0.23543342 0.5127819 -0.071340245
## 57 111251417 magenta 4.786996 0.7257890 -0.28697745 0.2185357 -0.421669075
## 58 111251520 magenta 5.255670 0.7425263 -0.25489980 0.3141708 -0.244275808
## 59 111251804 magenta 15.157361 0.8791005 -0.54371268 0.5160739 -0.337826325
## 60 111252023 magenta 13.973837 0.8661814 -0.56726682 0.6133908 -0.309702151
## 61 111252053 magenta 14.862509 0.8963405 -0.41784453 0.5145422 -0.267409457
## 62 111252065 magenta 7.885429 0.8012434 -0.39036880 0.5699691 0.045579925
## 63 111252397 magenta 5.193537 0.7380677 -0.55890989 0.5028148 0.319286128
## 64 111252565 magenta 8.306397 0.7789317 -0.43534521 0.2731924 -0.307079182
## 65 111252592 magenta 5.891810 0.7337840 -0.32871356 0.1819197 -0.385443756
## 66 111252595 magenta 9.411344 0.8314912 -0.49628367 0.3365239 -0.226382522
## 67 111252649 magenta 12.697701 0.8870761 -0.45619707 0.5753213 -0.064013137
## 68 111252854 magenta 17.943261 0.9222455 -0.47525067 0.6964539 -0.282119472
## 69 111253109 magenta 12.628946 0.8589323 -0.51295405 0.3590835 -0.274514767
## 70 111253281 magenta 6.835902 0.7420314 -0.35210159 0.2102720 -0.445180599
## 71 111253379 magenta 3.682578 0.6949258 -0.30745403 0.5421018 0.260606048
## 72 111253800 magenta 16.094312 0.9034662 -0.45444879 0.5971160 -0.299018590
## 73 111253808 magenta 13.508291 0.8650949 -0.60478863 0.5006907 -0.235716934
## 74 111253948 magenta 5.039276 0.7178141 -0.34717309 0.6011234 -0.369352756
## 75 111254070 magenta 7.548068 0.7705447 -0.58683486 0.1324167 -0.342197236
## 76 111254122 magenta 11.418235 0.8774034 -0.32068423 0.6280394 -0.240210917
## 77 111254126 magenta 11.503014 0.8601873 -0.64867282 0.3551917 -0.090396930
## 78 111254141 magenta 13.238670 0.8526678 -0.32090553 0.7659278 -0.321845412
## 79 111254343 magenta 2.212959 0.6291470 -0.22748056 0.3686377 -0.146417132
## 80 111254601 magenta 8.248668 0.7797701 -0.24323823 0.7774221 0.026391573
## 81 111254622 magenta 3.673205 0.6552071 -0.54754374 0.2970110 0.226340414
## 82 111254776 magenta 13.196680 0.8765098 -0.40343539 0.6133320 -0.009339968
## 83 111255263 magenta 6.030624 0.7449968 -0.56202488 0.2294823 -0.175801033
## 84 111255374 magenta 14.757598 0.8823629 -0.39641578 0.6671085 -0.258446908
## 85 111255470 magenta 16.037453 0.9044367 -0.48181219 0.6461752 -0.134350234
## 86 111255627 magenta 6.632640 0.8014901 -0.27094816 0.4003038 -0.301184133
## virus.x Protein Name
## 1 <NA> <NA>
## 2 <NA> <NA>
## 3 <NA> <NA>
## 4 <NA> <NA>
## 5 <NA> <NA>
## 6 <NA> <NA>
## 7 <NA> <NA>
## 8 <NA> <NA>
## 9 <NA> <NA>
## 10 <NA> <NA>
## 11 <NA> <NA>
## 12 <NA> <NA>
## 13 <NA> <NA>
## 14 <NA> <NA>
## 15 <NA> <NA>
## 16 <NA> <NA>
## 17 <NA> <NA>
## 18 <NA> <NA>
## 19 <NA> <NA>
## 20 <NA> <NA>
## 21 <NA> <NA>
## 22 <NA> <NA>
## 23 <NA> <NA>
## 24 <NA> <NA>
## 25 <NA> <NA>
## 26 <NA> <NA>
## 27 <NA> <NA>
## 28 <NA> <NA>
## 29 <NA> <NA>
## 30 <NA> <NA>
## 31 <NA> <NA>
## 32 <NA> <NA>
## 33 <NA> <NA>
## 34 <NA> <NA>
## 35 <NA> <NA>
## 36 <NA> <NA>
## 37 <NA> <NA>
## 38 <NA> <NA>
## 39 <NA> <NA>
## 40 <NA> <NA>
## 41 <NA> <NA>
## 42 <NA> <NA>
## 43 <NA> <NA>
## 44 <NA> <NA>
## 45 <NA> <NA>
## 46 <NA> <NA>
## 47 <NA> <NA>
## 48 <NA> <NA>
## 49 <NA> <NA>
## 50 <NA> <NA>
## 51 <NA> <NA>
## 52 <NA> <NA>
## 53 <NA> <NA>
## 54 <NA> <NA>
## 55 <NA> <NA>
## 56 <NA> <NA>
## 57 <NA> <NA>
## 58 <NA> <NA>
## 59 <NA> <NA>
## 60 <NA> <NA>
## 61 <NA> <NA>
## 62 <NA> <NA>
## 63 <NA> <NA>
## 64 DWVa troponin C
## 65 ARV_2 muscle LIM protein 1-like
## 66 <NA> <NA>
## 67 <NA> <NA>
## 68 <NA> <NA>
## 69 <NA> <NA>
## 70 ARV_2 troponin I-like isoform X1
## 71 <NA> <NA>
## 72 <NA> <NA>
## 73 <NA> <NA>
## 74 <NA> <NA>
## 75 <NA> <NA>
## 76 <NA> <NA>
## 77 <NA> <NA>
## 78 <NA> <NA>
## 79 <NA> <NA>
## 80 <NA> <NA>
## 81 <NA> <NA>
## 82 <NA> <NA>
## 83 <NA> <NA>
## 84 <NA> <NA>
## 85 <NA> <NA>
## 86 <NA> <NA>sum(!is.na(f$virus.x))## [1] 3# only 3 genes of magenta module appear in the former hubgene. analysis
# so to what module these 53-hubgenes belong to?
e <- left_join(hub_old, all.genes.66, by = "genes")
e## # A tibble: 53 x 9
## genes virus.x `Protein Name` module IntraModCon MMmagenta GS.DWVa GS.VDV2
## <chr> <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1112… DWVa sarcoplasmic … purple 16.3 0.331 -0.420 -0.115
## 2 1112… DWVa LOW QUALITY P… purple 16.5 0.372 -0.390 -0.136
## 3 1112… DWVa myosin heavy … purple 15.7 0.299 -0.323 -0.222
## 4 1112… DWVa LOW QUALITY P… purple 15.8 0.241 -0.416 -0.184
## 5 1112… DWVa paramyosin-li… purple 15.2 0.394 -0.471 -0.0357
## 6 1112… DWVa glutamate rec… purple 14.2 0.356 -0.347 -0.141
## 7 1112… DWVa protein phosp… purple 13.3 0.398 -0.376 -0.0163
## 8 1112… DWVa LOW QUALITY P… purple 13.9 0.389 -0.454 -0.183
## 9 1112… DWVa glutamate rec… purple 14.0 0.196 -0.440 -0.312
## 10 1112… DWVa solute carrie… purple 11.1 0.442 -0.347 -0.00790
## # … with 43 more rows, and 1 more variable: GS.ARV_2 <dbl># in the 66 libs, most of them belong to the purple module
all.genes.57 <- data.frame(
module = moduleColors_57,
geneTraitSignificance_57,
geneModuleMembership_57,
IntraModCon = Alldegrees1_57$kWithin) %>%
dplyr::select(c(module, IntraModCon, MMmagenta, GS.DWVa, GS.VDV2, GS.ARV_2)) %>%
rownames_to_column("genes")
g <- left_join(hub_old, all.genes.57, by = "genes")
g## # A tibble: 53 x 9
## genes virus.x `Protein Name` module IntraModCon MMmagenta GS.DWVa GS.VDV2
## <chr> <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1112… DWVa sarcoplasmic … salmon 15.8 -0.0494 -0.303 -0.190
## 2 1112… DWVa LOW QUALITY P… salmon 13.6 -0.00292 -0.281 -0.151
## 3 1112… DWVa myosin heavy … salmon 15.1 -0.104 -0.203 -0.261
## 4 1112… DWVa LOW QUALITY P… salmon 15.3 -0.0104 -0.343 -0.236
## 5 1112… DWVa paramyosin-li… salmon 12.9 -0.108 -0.367 -0.136
## 6 1112… DWVa glutamate rec… salmon 13.8 -0.107 -0.223 -0.203
## 7 1112… DWVa protein phosp… salmon 13.0 -0.00668 -0.272 -0.0683
## 8 1112… DWVa LOW QUALITY P… purple 14.4 -0.0892 -0.349 -0.205
## 9 1112… DWVa glutamate rec… salmon 11.0 -0.0396 -0.353 -0.341
## 10 1112… DWVa solute carrie… salmon 9.64 0.145 -0.285 0.0203
## # … with 43 more rows, and 1 more variable: GS.ARV_2 <dbl># in the 57 libs, most belong to the salmon module.
# and what about the silenced genes? to which module they belong to?
# prepare a table describing each gene number, short-name and full annotation, of the silenced genes:
genes <- as.factor(c("111250594", "111244103","111244832", "111248360", "111245345", "111248674", "111251059", "111245371", "111244631", "111252566", "111249664"))
annot <- as.factor(c("Sarcalumenin", "glycerol-3-phosphate dehydrogenase", "Calmodulin", "Cuticle-protein8","Cuticle-protein-14", "Glutamate-receptor-3", "Glutamate-gated-chloride-channel-subunit-beta", "Organic-cation-transporter-protein", "Twitchin", "Annulin", "Defense-protein-Hdd11"))
ShortName <- as.factor(c("Sar", "Gly","clmd", "CuP8", "CuP14", "Glut", "Chl", "Trans", "Twitch", "Anl", "HDD"))
silenced.genes <- data.frame(genes, ShortName, annot)
# to what module the silenced genes belong to?
silenced_overlap.66 <- left_join(silenced.genes, all.genes.66, by = "genes")
# of the 11 genes, 8 belong to purple module, 2 to pink and 1 to green.
# all have negative interaction with DWVa.