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$kWithin) %>%
dplyr::select(c(module, IntraModCon, MMblack, GS.DWVa, GS.VDV2, GS.ARV_2)) %>%
dplyr::filter(module%in% c("black")) %>%
rownames_to_column("genes")
# make a table of the overlapping genes
overlap <- inner_join(geneInfoMagenta_66, geneInfoBlack_57, by = "genes")
#change the columns names to specify from which analysis it was taken (66libs or 57libs)
overlap <- overlap %>%
dplyr::rename(module_66 = module.x,
module_57 = module.y,
IntraModCon_66 = IntraModCon.x,
IntraModCon_57 = IntraModCon.y,
GS.DWVa_66 = GS.DWVa.x,
GS.DWVa_57 = GS.DWVa.y,
GS.VDV2_66 = GS.VDV2.x,
GS.VDV2_57 = GS.VDV2.y,
GS.ARV_2_66 = GS.ARV_2.x,
GS.ARV_2_57 = GS.ARV_2.y)
nrow(overlap) ## [1] 40# we have 40 overlapping genes in the magenat and balck modules, as the "overlapTable()" function computed.
# add gene annotation:
# load the annotation file:
annot_varroa <- read_csv("/Users/nuriteliash/Documents/GitHub/varroa-virus-networks/data/annot_varroa.csv", col_names = TRUE, )##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Name = col_character(),
## Accession = col_character(),
## Start = col_double(),
## Stop = col_double(),
## Strand = col_character(),
## GeneID = col_double(),
## Locus = col_character(),
## `Locus tag` = col_character(),
## `Protein product` = col_character(),
## Length = col_double(),
## `Protein Name` = col_character()
## )# remove the "LOC" from the gene name
annot_varroa$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] 40overlap_annot## genes module_66 module_57 IntraModCon_66 IntraModCon_57 GS.DWVa_66
## 1 111243784 magenta black 11.223440 22.198350 -0.2925204
## 2 111243925 magenta black 4.977129 22.088913 -0.2427469
## 5 111243999 magenta black 4.241030 11.075776 -0.2982548
## 7 111244168 magenta black 17.163039 49.723289 -0.4469089
## 8 111244366 magenta black 8.195112 22.937088 -0.2901975
## 9 111244889 magenta black 9.047380 12.616443 -0.2406085
## 10 111244965 magenta black 11.223471 52.272521 -0.2927762
## 11 111245094 magenta black 5.241102 8.915815 -0.3232085
## 18 111245189 magenta black 6.223219 28.195801 -0.1443773
## 19 111245224 magenta black 19.225611 29.806858 -0.5257100
## 20 111245582 magenta black 10.819486 27.375348 -0.3930408
## 21 111245627 magenta black 13.826894 30.693962 -0.5259656
## 22 111246014 magenta black 15.741042 33.400666 -0.3590890
## 23 111246302 magenta black 8.552619 24.511668 -0.3738306
## 24 111246841 magenta black 13.968961 54.124280 -0.1939304
## 25 111247393 magenta black 3.646495 11.589818 -0.4529772
## 29 111248051 magenta black 5.443256 32.074934 -0.1430254
## 30 111248663 magenta black 13.455119 19.074242 -0.6526831
## 31 111249529 magenta black 18.248457 34.749502 -0.5461694
## 32 111249640 magenta black 10.505546 56.341325 -0.3610908
## 34 111250285 magenta black 5.554644 29.566964 -0.2247173
## 35 111250955 magenta black 4.535455 14.494932 -0.2948846
## 36 111251058 magenta black 18.325534 27.844484 -0.5047920
## 37 111251111 magenta black 13.781119 39.694638 -0.3695021
## 38 111251804 magenta black 15.157361 17.486192 -0.5437127
## 39 111252023 magenta black 13.973837 25.692861 -0.5672668
## 40 111252053 magenta black 14.862509 15.371700 -0.4178445
## 41 111252649 magenta black 12.697701 32.744145 -0.4561971
## 43 111252854 magenta black 17.943261 41.119880 -0.4752507
## 44 111253379 magenta black 3.682578 9.704401 -0.3074540
## 45 111253800 magenta black 16.094312 34.594783 -0.4544488
## 47 111253808 magenta black 13.508291 15.657505 -0.6047886
## 49 111253948 magenta black 5.039276 21.436117 -0.3471731
## 50 111254122 magenta black 11.418235 31.362879 -0.3206842
## 51 111254141 magenta black 13.238670 51.516648 -0.3209055
## 52 111254601 magenta black 8.248668 55.251005 -0.2432382
## 54 111254776 magenta black 13.196680 44.403272 -0.4034354
## 55 111255374 magenta black 14.757598 42.451929 -0.3964158
## 56 111255470 magenta black 16.037453 32.857336 -0.4818122
## 57 111255627 magenta black 6.632640 15.041197 -0.2709482
## GS.DWVa_57 GS.VDV2_66 GS.VDV2_57 GS.ARV_2_66 GS.ARV_2_57 Name
## 1 -0.1943919 0.6398958 0.6597310 -0.129847510 -0.4165116 Un
## 2 -0.2090687 0.4471420 0.5748613 -0.391770250 -0.2430201 Un
## 5 -0.1522141 0.4398575 0.4569898 -0.112730602 -0.1764775 Un
## 7 -0.3877705 0.7614299 0.8136362 -0.171302433 -0.3362780 Un
## 8 -0.1417271 0.5408519 0.6444956 -0.358470771 -0.4394230 Un
## 9 -0.1229824 0.4918116 0.5257225 -0.240698081 -0.4722398 Un
## 10 -0.2032580 0.6688896 0.7670656 -0.324538421 -0.3949422 Un
## 11 -0.2762196 0.6049363 0.5807265 0.134047618 -0.4222172 Un
## 18 -0.1371829 0.7684324 0.8240359 -0.212014499 -0.4360924 Un
## 19 -0.4297188 0.6266771 0.7010731 -0.224120281 -0.2661796 Un
## 20 -0.2685444 0.5766143 0.6611281 -0.302416626 -0.3216513 Un
## 21 -0.4369329 0.4969625 0.6151987 -0.276748922 -0.1085196 Un
## 22 -0.3258962 0.6036798 0.7015680 -0.214597145 -0.2846728 Un
## 23 -0.3946526 0.5160128 0.6608939 -0.163662362 -0.2563243 Un
## 24 -0.1341113 0.7451658 0.8185060 -0.236442918 -0.4523176 Un
## 25 -0.4482936 0.6247344 0.6491722 -0.046751586 -0.1961815 Un
## 29 -0.1266740 0.7403198 0.7410321 0.148530942 -0.4208976 Un
## 30 -0.5747656 0.5182510 0.5810407 -0.194953621 -0.1336256 Un
## 31 -0.4706368 0.5990873 0.7192397 -0.329921444 -0.2633597 Un
## 32 -0.2882585 0.6917785 0.7901045 -0.258344046 -0.2238572 Un
## 34 -0.1039504 0.6877988 0.7123644 0.018579016 -0.4346871 Un
## 35 -0.2837554 0.6458510 0.6675542 0.041280877 -0.2533139 Un
## 36 -0.4479580 0.5369363 0.6478006 -0.288503815 -0.2802167 Un
## 37 -0.3193897 0.6502168 0.7651151 -0.247056774 -0.2908267 Un
## 38 -0.4825198 0.5160739 0.6164134 -0.337826325 -0.3081682 Un
## 39 -0.5140609 0.6133908 0.7157492 -0.309702151 -0.2723798 Un
## 40 -0.3593588 0.5145422 0.5857436 -0.267409457 -0.3800227 Un
## 41 -0.3623834 0.5753213 0.6194092 -0.064013137 -0.1398686 Un
## 43 -0.4351661 0.6964539 0.8064760 -0.282119472 -0.2810398 Un
## 44 -0.2413451 0.5421018 0.5273003 0.260606048 -0.2036610 Un
## 45 -0.3511759 0.5971160 0.6996857 -0.299018590 -0.2702058 Un
## 47 -0.5229291 0.5006907 0.5900728 -0.235716934 -0.1969170 Un
## 49 -0.3130022 0.6011234 0.7144338 -0.369352756 -0.4263867 Un
## 50 -0.2610105 0.6280394 0.6896842 -0.240210917 -0.2594001 Un
## 51 -0.2592884 0.7659278 0.8677597 -0.321845412 -0.4086162 Un
## 52 -0.2240337 0.7774221 0.7831164 0.026391573 -0.2423346 Un
## 54 -0.3374515 0.6133320 0.6571393 -0.009339968 -0.1090063 Un
## 55 -0.3530532 0.6671085 0.7453012 -0.258446908 -0.3177539 Un
## 56 -0.4449958 0.6461752 0.7108208 -0.134350234 -0.2549213 Un
## 57 -0.2142499 0.4003038 0.5115095 -0.301184133 -0.3069202 Un
## Accession Protein product Length
## 1 NW_019211455.1 XP_022645599.1 97
## 2 NW_019211455.1 XP_022646010.1 275
## 5 NW_019211455.1 XP_022646231.1 825
## 7 NW_019211455.1 XP_022646682.1 121
## 8 NW_019211455.1 XP_022647130.1 198
## 9 NW_019211455.1 XP_022648164.1 123
## 10 NW_019211455.1 XP_022648335.1 81
## 11 NW_019211454.1 XP_022648662.1 180
## 18 NW_019211454.1 XP_022648917.1 132
## 19 NW_019211455.1 XP_022649014.1 115
## 20 NW_019211455.1 XP_022649852.1 92
## 21 NW_019211455.1 XP_022649973.1 115
## 22 NW_019211455.1 XP_022650905.1 211
## 23 NW_019211456.1 XP_022651395.1 165
## 24 NW_019211456.1 XP_022652845.1 141
## 25 NW_019211456.1 XP_022653963.1 194
## 29 NW_019211456.1 XP_022655498.1 139
## 30 NW_019211457.1 XP_022657111.1 108
## 31 NW_019211457.1 XP_022659256.1 86
## 32 NW_019211457.1 XP_022659492.1 102
## 34 NW_019211457.1 XP_022661021.1 103
## 35 NW_019211458.1 XP_022662723.1 230
## 36 NW_019211458.1 XP_022662996.1 149
## 37 NW_019211458.1 XP_022663149.1 142
## 38 NW_019211458.1 XP_022664567.1 93
## 39 NW_019211458.1 XP_022665066.1 88
## 40 NW_019211458.1 XP_022665140.1 175
## 41 NW_019211459.1 XP_022666611.1 186
## 43 NW_019211459.1 XP_022667135.1 107
## 44 NW_019211459.1 XP_022668409.1 116
## 45 NW_019211460.1 XP_022669529.1 82
## 47 NW_019211460.1 XP_022669567.1 157
## 49 NW_019211460.1 XP_022669964.1 234
## 50 NW_019211460.1 XP_022670391.1 131
## 51 NW_019211460.1 XP_022670438.1 75
## 52 NW_019211460.1 XP_022671339.1 120
## 54 NW_019211460.1 XP_022671703.1 152
## 55 NW_019211454.1 XP_022673017.1 147
## 56 NW_019211454.1 XP_022673201.1 178
## 57 NW_019211454.1 XP_022673516.1 437
## Protein Name
## 1 cystatin-B-like
## 2 uncharacterized protein LOC111243925 isoform X3
## 5 chondroitin sulfate synthase 3-like
## 7 NADH dehydrogenase
## 8 alpha-crystallin A chain-like
## 9 uncharacterized protein LOC111244889
## 10 cytochrome b-c1 complex subunit 6, mitochondrial-like
## 11 uncharacterized protein LOC111245094 isoform X1
## 18 uncharacterized protein LOC111245189
## 19 ATPase inhibitor, mitochondrial-like
## 20 NADH dehydrogenase
## 21 NADH dehydrogenase
## 22 ATP synthase subunit O, mitochondrial-like
## 23 ATP synthase subunit delta, mitochondrial-like
## 24 coiled-coil-helix-coiled-coil-helix domain-containing protein 2-like
## 25 high affinity copper uptake protein 1-like isoform X2
## 29 uncharacterized protein LOC111248051
## 30 cytochrome b-c1 complex subunit 7-like
## 31 cytochrome c oxidase subunit NDUFA4-like
## 32 uncharacterized protein LOC111249640 isoform X2
## 34 uncharacterized protein LOC111250285
## 35 uncharacterized protein LOC111250955
## 36 cytochrome c oxidase subunit 5A, mitochondrial-like
## 37 ATP synthase lipid-binding protein, mitochondrial-like
## 38 ATP synthase subunit e, mitochondrial-like
## 39 putative cytochrome c oxidase subunit 7A3, mitochondrial
## 40 NADH dehydrogenase
## 41 uncharacterized protein LOC111252649 isoform X2
## 43 putative ATP synthase subunit f, mitochondrial
## 44 lipopolysaccharide-induced tumor necrosis factor-alpha factor homolog
## 45 MICOS complex subunit Mic10-like
## 47 succinate dehydrogenase
## 49 uncharacterized protein LOC111253948
## 50 uncharacterized protein LOC111254122
## 51 cytochrome c oxidase subunit 7C, mitochondrial-like
## 52 mitochondrial import inner membrane translocase subunit TIM14-like isoform X1
## 54 39S ribosomal protein L23, mitochondrial-like
## 55 NADH dehydrogenase
## 56 ATP synthase subunit d, mitochondrial-like
## 57 tachykinin-like peptides receptor 99Dwrite_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># 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.