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Dive back in:

We left off looking to modify the selection cutoff for top terms. While various ideas were floated(standard devs, percentiles, flat cutoff), there was not a consistently most useful method for looking at both male and female data. As such, I opted to run a series of flat cutoffs(100, 50, 25) for both complete methods per sex.

I first looked at the top terms ordered to look for patterns in the data. While I’m unsure of the significance, the clustering of correlations for top terms shares a shape within sexes across methods. Notably, female data had a sharper increase(negative slope) for top results

load('snake/data/go/50_tables/enrichment.Rdata')

topGrid <- function(data, sex, psize, custom.title, custom.Xlab, custom.Ylab){
  plothole <- ggplot(data, aes(x=index, y=cor, label=term))+
    geom_point(color=viridis(1, begin=0.5), size=psize)+
    theme_minimal() +
    labs(x=custom.Xlab, y=custom.Ylab, tag=sex, title=custom.title) +
    theme(text=element_text(size=10), plot.tag = element_text(size=15))
  return(plothole)
}

gg[[1]] <- topGrid(blupF, 'F', 1, 'Top Female Results: TBLUP', 'Rank', 'Correlation')
gg[[2]] <- topGrid(blupM, 'M', 1, 'Top Male Results: TBLUP', 'Rank', 'Correlation')
gg[[3]] <- topGrid(bayesF, 'F', 1, 'Top Female Results: BayesC', 'Rank', 'Correlation')
gg[[4]] <- topGrid(bayesM, 'M', 1, 'Top Male Results: BayesC', 'Rank', 'Correlation')

plot_grid(gg[[1]], gg[[2]], gg[[3]], gg[[4]], ncol=2)

Version Author Date
8f35a08 nklimko 2024-01-23

After this, I found the correlation between the two methods to see how similar generated results are.

print(cor(blupF$cor, bayesF$cor))
[1] 0.9490421

Female Overall

print(cor(blupF[1:200,cor], bayesF[1:200, cor]))
[1] 0.9937145

Female Top 200

print(cor(blupM$cor, bayesM$cor))
[1] 0.9598015

Male Overall

print(cor(blupM[1:200,cor], bayesM[1:200, cor]))
[1] 0.9967979

Male Top 100

Moving past this, I wanted to assess the effect of term count on enrichment——000000——000000——000000——000000——000000

load("snake/data/go/50_tables/enrich/kables.Rdata")

percentModder <- function(dataKable, custom.caption){
  
  dataKable[,5] <- dataKable[,5]*2
  dataKable[,8] <- dataKable[,8]*2
  
  colnames(dataKable) <- rep(c('Flybase Gene', 'Percent', 'Gene'), 3)

  kabled <- kable(dataKable, caption=custom.caption, "simple", header = c('Top 100 GO Terms' = 3, 'Top 50 GO Terms' = 3, 'Top 25 GO Terms' = 3))

  return(kabled)
  
}
bayesF_KableMod <- percentModder(bayesF_Kable, 'Female BayesC')

#print(bayesF_KableMod)

kable(bayesF_Kable, caption="Female BayesC", "simple")
Female BayesC
Flybase Gene Count Gene Flybase Gene Count Gene Flybase Gene Count Gene
FBgn0262738 15 norpA FBgn0025595 9 AkhR FBgn0025595 8 AkhR
FBgn0003731 13 Egfr FBgn0003731 9 Egfr FBgn0000575 6 emc
FBgn0004635 13 rho FBgn0003205 9 Ras85D FBgn0004552 4 Akh
FBgn0003205 10 Ras85D FBgn0004635 9 rho FBgn0283499 4 InR
FBgn0025595 9 AkhR FBgn0000575 7 emc FBgn0000490 4 dpp
FBgn0003310 8 NULL FBgn0262738 7 norpA FBgn0010303 3 hep
FBgn0000575 7 emc FBgn0004552 5 Akh FBgn0015279 3 Pi3K92E
FBgn0015795 7 Rab7 FBgn0015279 5 Pi3K92E FBgn0033799 3 GLaz
FBgn0283499 6 InR FBgn0283499 5 InR FBgn0036449 3 bmm
FBgn0039114 6 Lsd-1 FBgn0003310 5 NULL FBgn0003731 3 Egfr
FBgn0004552 5 Akh FBgn0035586 4 CG10671 FBgn0003205 3 Ras85D
FBgn0015279 5 Pi3K92E FBgn0026252 4 msk FBgn0003463 3 sog
FBgn0035586 5 CG10671 FBgn0000490 4 dpp FBgn0003719 3 tld
FBgn0003463 5 sog FBgn0261648 4 salm FBgn0262738 3 norpA
FBgn0003719 5 tld FBgn0010303 3 hep
FBgn0036545 5 GXIVsPLA2 FBgn0036046 3 Ilp2
FBgn0039655 5 CG14507 FBgn0024248 3 chico
FBgn0003118 5 pnt FBgn0033799 3 GLaz
FBgn0005672 5 spi FBgn0036449 3 bmm
FBgn0036046 4 Ilp2 FBgn0036260 3 Rh7
FBgn0003651 4 svp FBgn0003463 3 sog
FBgn0010379 4 Akt1 FBgn0003719 3 tld
FBgn0024248 4 chico FBgn0003984 3 vn
FBgn0036449 4 bmm FBgn0038197 3 foxo
FBgn0026252 4 msk FBgn0039114 3 Lsd-1
FBgn0036260 4 Rh7 FBgn0003218 3 rdgB
FBgn0000490 4 dpp FBgn0026207 3 mbo
FBgn0003984 4 vn FBgn0027537 3 Nup93-1
FBgn0261648 4 salm FBgn0031078 3 Nup205
FBgn0029720 4 CG3009 FBgn0033737 3 Nup54
FBgn0030013 4 GIIIspla2 FBgn0033766 3 CG8771
FBgn0033170 4 sPLA2 FBgn0038274 3 Nup93-2
FBgn0050503 4 CG30503 FBgn0061200 3 Nup153
FBgn0250862 4 CG42237 FBgn0003256 3 rl
FBgn0003218 4 rdgB FBgn0034140 3 Lst
FBgn0030608 4 Lsd-2 FBgn0015795 3 Rab7
FBgn0035206 4 CG9186 FBgn0002940 3 ninaE
FBgn0040336 4 Seipin FBgn0003295 3 ru
FBgn0026207 4 mbo
FBgn0004435 4 Galphaq
FBgn0000257 4 car
FBgn0000482 4 dor
FBgn0003256 4 rl
FBgn0035871 4 BI-1
FBgn0052350 4 Vps11
FBgn0285911 4 NULL
FBgn0015754 4 Lis-1
FBgn0004647 4 NULL
FBgn0002940 4 ninaE
FBgn0003295 4 ru
FBgn0020386 4 Pdk1
FBgn0262451 4 ban
FBgn0004784 4 inaC
FBgn0003169 3 put
FBgn0010303 3 hep
FBgn0028717 3 Lnk
FBgn0010051 3 Itp-r83A
FBgn0030607 3 dob
FBgn0033226 3 CG1882
FBgn0033799 3 GLaz
FBgn0262103 3 Sik3
FBgn0002576 3 lz
FBgn0004885 3 tok
FBgn0050418 3 nord
FBgn0039152 3 Root
FBgn0038197 3 foxo
FBgn0028741 3 fab1
FBgn0029870 3 Marf
FBgn0052703 3 Erk7
FBgn0027537 3 Nup93-1
FBgn0031078 3 Nup205
FBgn0033737 3 Nup54
FBgn0033766 3 CG8771
FBgn0038274 3 Nup93-2
FBgn0061200 3 Nup153
FBgn0261549 3 rdgA
FBgn0002566 3 lt
FBgn0034140 3 Lst
FBgn0038659 3 EndoA
FBgn0086676 3 spin
FBgn0015721 3 ktub
FBgn0086687 3 Desat1
FBgn0014010 3 Rab5
FBgn0025680 3 cry
FBgn0038167 3 Lkb1
FBgn0004611 3 Plc21C
FBgn0001263 3 inaD
FBgn0052699 3 LPCAT
FBgn0265048 3 cv-d 
if(0){
bayesF_KableMod <- percentModder(bayesF_Kable)
bayesM_KableMod <- percentModder(bayesM_Kable)
blupF_KableMod <- percentModder(blupF_Kable )
blupM_KableMod <- percentModder(blupM_Kable )

kable(bayesF_KableMod, caption="Female BayesC", "simple")
kable(bayesM_KableMod, caption="Male BayesC", "simple")
kable(blupF_KableMod, caption="Female TBLUP", "simple")
kable(blupM_KableMod, caption="Male TBLUP", "simple")
}

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/libs/gnu9/openblas/0.3.7/lib/libopenblasp-r0.3.7.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] DT_0.31            kableExtra_1.3.4   knitr_1.43         reshape2_1.4.4    
 [5] melt_1.10.0        ggcorrplot_0.1.4.1 lubridate_1.9.3    forcats_1.0.0     
 [9] stringr_1.5.0      purrr_1.0.1        readr_2.1.4        tidyr_1.3.0       
[13] tibble_3.2.1       tidyverse_2.0.0    scales_1.2.1       viridis_0.6.4     
[17] viridisLite_0.4.2  qqman_0.1.9        cowplot_1.1.1      ggplot2_3.4.4     
[21] data.table_1.14.8  dplyr_1.1.3        workflowr_1.7.1   

loaded via a namespace (and not attached):
 [1] httr_1.4.7        sass_0.4.7        jsonlite_1.8.7    bslib_0.5.0      
 [5] getPass_0.2-2     highr_0.10        yaml_2.3.7        pillar_1.9.0     
 [9] glue_1.6.2        digest_0.6.33     promises_1.2.0.1  rvest_1.0.3      
[13] colorspace_2.1-0  htmltools_0.5.5   httpuv_1.6.12     plyr_1.8.9       
[17] pkgconfig_2.0.3   calibrate_1.7.7   webshot_0.5.5     processx_3.8.2   
[21] svglite_2.1.2     whisker_0.4.1     later_1.3.1       tzdb_0.4.0       
[25] timechange_0.2.0  git2r_0.32.0      generics_0.1.3    farver_2.1.1     
[29] cachem_1.0.8      withr_2.5.0       cli_3.6.1         magrittr_2.0.3   
[33] evaluate_0.21     ps_1.7.5          fs_1.6.3          fansi_1.0.4      
[37] MASS_7.3-60       xml2_1.3.3        tools_4.1.2       hms_1.1.3        
[41] lifecycle_1.0.3   munsell_0.5.0     callr_3.7.3       compiler_4.1.2   
[45] jquerylib_0.1.4   systemfonts_1.0.5 rlang_1.1.1       grid_4.1.2       
[49] rstudioapi_0.15.0 htmlwidgets_1.6.2 labeling_0.4.3    rmarkdown_2.23   
[53] gtable_0.3.4      R6_2.5.1          gridExtra_2.3     fastmap_1.1.1    
[57] utf8_1.2.3        rprojroot_2.0.3   stringi_1.7.12    Rcpp_1.0.11      
[61] vctrs_0.6.4       tidyselect_1.2.0  xfun_0.39