GO-TBLUP Report
Last updated: 2024-01-16
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Knit directory: dgrp-starve/
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#load('snake/data/topTables.Rdata')
options(knitr.kable.NA = '')Summary
GBLUP had the R2 constraints removed from the model to allow each component of the model to maximally explain variance. Overall maximum still 0.8.
While this does remove a degree of certainty, model accuracy not only improved significantly but was able to find similar results to bayesC.
#function
partMake <- function(data, sex, nullInt, upperCutoff, lowerCutoff, psize, custom.title, custom.Xlab, custom.Ylab){
plothole <- ggplot(data, aes(x=term, y=cor, label=term))+
geom_point(color=viridis(1, begin=0.5), size=psize)+
geom_text(aes(label=ifelse(cor>upperCutoff, as.character(term),'')), hjust=0, size=2, angle=0)+
geom_text(aes(label=ifelse(cor<lowerCutoff, as.character(term),'')), hjust=1, size=2, angle=90)+
geom_hline(yintercept = nullInt) +
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)
}
#data
load('snake/data/go/50_tables/saveTables.Rdata')
#Cutoff selection
sigFactor <- 3
sdf <- sd(unlist(allDataF[,2]))
meanf <- mean(unlist(allDataF[,2]))
cutoffF <- meanf + sigFactor*sdf
sdm <- sd(unlist(allDataM[,2]))
meanm <- mean(unlist(allDataM[,2]))
cutoffM <- meanm + sigFactor*sdm
#graphs
gg[[1]] <- partMake(allDataF, 'F', 0.31, cutoffF, 0.2, 1, 'Effect of GO Annotations in TBLUP models', 'GO Term', 'Prediction Accuracy')
gg[[2]] <- partMake(allDataM, 'M', 0.43, cutoffM, 0.2, 1, 'Effect of GO Annotations in TBLUP models', 'GO Term', 'Prediction Accuracy')
subF <- allDataF[which(cor>cutoffF),]
subM <- allDataM[which(cor>cutoffM),]
subF <- subF[order(-cor),]
subM <- subM[order(-cor),]
#write ordered GO terms to table file for enrichment purposes
cat(unlist(subF[,1]), sep = '\n', file='snake/data/go/50_tables/topHitsF.txt')
cat(unlist(subM[,1]), sep = '\n', file='snake/data/go/50_tables/topHitsM.txt')
cat(unlist(subF[,1]), sep = '\n', file='snake/code/go/enrichment/blup/f/topHitsF.txt')
cat(unlist(subM[,1]), sep = '\n', file='snake/code/go/enrichment/blup/m/topHitsM.txt')
topBlupSoloF <- readRDS('snake/code/go/enrichment/blup/f/finalData.Rds')
topBlupSoloM <- readRDS('snake/code/go/enrichment/blup/m/finalData.Rds')Initial Findings
Comparison of top 20 terms from both BayesC and TBLUP yields familiar results: 11 of top 20 match for females, 10 of top 20 match for males
This suggests the models are both accurate and able to detect GO terms of interest, even with delimited R2.
Females
kable(finF, caption = 'Female BayesC/BLUP Comparison', "simple")| BayesC_Cor | TBLUP_Cor | Term | BayesC_Rank | TBLUP_Rank |
|---|---|---|---|---|
| 0.4382497 | 0.4319931 | GO.0045819 | 1 | 3 |
| 0.4231229 | 0.3881740 | GO.0033500 | 2 | 11 |
| 0.4178735 | 0.4514600 | GO.0055088 | 3 | 1 |
| 0.4014416 | 0.4147260 | GO.0042675 | 4 | 6 |
| 0.3984704 | 0.4366905 | GO.0008586 | 6 | 2 |
| 0.3919625 | 0.3811383 | GO.0016042 | 7 | 15 |
| 0.3873729 | 0.3805084 | GO.0007368 | 8 | 17 |
| 0.3799043 | 0.3833308 | GO.0006644 | 10 | 13 |
| 0.3777651 | 0.3805090 | GO.0046488 | 12 | 16 |
| 0.3736580 | 0.4223885 | GO.0017056 | 14 | 5 |
| 0.3691317 | 0.3786734 | GO.0061883 | 19 | 18 |
Males
kable(finM, caption = 'Male BayesC/BLUP Comparison', "simple")| BayesC_Cor | TBLUP_Cor | Term | BayesC_Rank | TBLUP_Rank |
|---|---|---|---|---|
| 0.5116379 | 0.5225415 | GO.0035008 | 1 | 2 |
| 0.5064311 | 0.5079819 | GO.0140042 | 2 | 8 |
| 0.5062906 | 0.5156219 | GO.0007485 | 3 | 4 |
| 0.4984272 | 0.5117790 | GO.0042461 | 6 | 7 |
| 0.4978650 | 0.5141982 | GO.0016327 | 8 | 5 |
| 0.4924000 | 0.5048001 | GO.0006044 | 11 | 11 |
| 0.4923077 | 0.5124686 | GO.0040018 | 12 | 6 |
| 0.4918994 | 0.5326720 | GO.0042593 | 13 | 1 |
| 0.4882313 | 0.5156778 | GO.0001738 | 15 | 3 |
| 0.4866070 | 0.4983168 | GO.0045196 | 17 | 18 |
Overall Results
For GO-TBLUP, I filtered top terms that were 3 standard deviations above the mean for each sex.
We then translated the top GO terms into human readable categories to assess our findings. Below are the top ten ordered by correlation.
Female
Top Terms
id: GO:0055088 name: lipid homeostasis
id: GO:0008586 name: imaginal disc-derived wing vein morphogenesis
id: GO:0045819 name: positive regulation of glycogen catabolic process
id: GO:0043066 name: negative regulation of apoptotic process
id: GO:0017056 name: structural constituent of nuclear pore
id: GO:0042675 name: compound eye cone cell differentiation
id: GO:0035556 name: intracellular signal transduction
id: GO:0006606 name: protein import into nucleus
id: GO:0005524 name: ATP binding
id: GO:0000281 name: mitotic cytokinesis
id: GO:0033500 name: carbohydrate homeostasis
id: GO:0042749 name: regulation of circadian sleep/wake cycle
id: GO:0006644 name: phospholipid metabolic process
id: GO:0004672 name: protein kinase activity
id: GO:0016042 name: lipid catabolic process
id: GO:0046488 name: phosphatidylinositol metabolic process
id: GO:0007368 name: determination of left/right symmetry
Male
Top Terms
id: GO:0042593 name: glucose homeostasis
id: GO:0035008 name: positive regulation of melanization defense response
id: GO:0001738 name: morphogenesis of a polarized epithelium
id: GO:0007485 name: imaginal disc-derived male genitalia development
id: GO:0016327 name: apicolateral plasma membrane
id: GO:0040018 name: positive regulation of multicellular organism growth
id: GO:0042461 name: photoreceptor cell development
id: GO:0140042 name: lipid droplet formation
id: GO:0030295 name: protein kinase activator activity
id: GO:0050830 name: defense response to Gram-positive bacterium
id: GO:0006044 name: N-acetylglucosamine metabolic process
id: GO:0070328 name: triglyceride homeostasis
id: GO:0045793 name: positive regulation of cell size
id: GO:0007166 name: cell surface receptor signaling pathway
id: GO:0007419 name: ventral cord development
Post Processing
Beyond this, we took the models to determine if certain genes were enriched in the GO terms of interest. From the selected terms, we pooled the associated genes and totaled gene occurrence.
Females involved 81 unique genes at least 3 times across top terms. Of these, 7 were present 4 or more times.
Males had a significantly lower number of genes involved than expected. Only 7 genes were involved at least 3 times across top terms. This may suggest that the selection criteria is too stringent for males despite males having a higher base prediction accuracy.
After establishing unique genes, we translated the FlyBase gene codes to human-readable genes.
Females
kable(topBlupSoloF, caption = 'GO-TBLUP Genes', "simple")| flybase | count | gene |
|---|---|---|
| FBgn0010379 | 5 | Akt1 |
| FBgn0003731 | 5 | Egfr |
| FBgn0025595 | 4 | AkhR |
| FBgn0262103 | 4 | Sik3 |
| FBgn0283499 | 4 | InR |
| FBgn0020386 | 4 | Pdk1 |
| FBgn0028484 | 4 | Ack |
| FBgn0004552 | 3 | Akh |
| FBgn0035039 | 3 | Adck |
| FBgn0261984 | 3 | Ire1 |
| FBgn0283472 | 3 | S6k |
| FBgn0000575 | 3 | emc |
| FBgn0004635 | 3 | rho |
| FBgn0026252 | 3 | msk |
| FBgn0035142 | 3 | Hipk |
| FBgn0003169 | 3 | put |
| FBgn0011300 | 3 | babo |
| FBgn0260945 | 3 | Atg1 |
| FBgn0002413 | 3 | dco |
| FBgn0032006 | 3 | Pvr |
| FBgn0003091 | 3 | Pkc53E |
| FBgn0003093 | 3 | Pkc98E |
| FBgn0003256 | 3 | rl |
| FBgn0003502 | 3 | Btk29A |
| FBgn0003744 | 3 | trc |
| FBgn0004784 | 3 | inaC |
| FBgn0004864 | 3 | hop |
| FBgn0010197 | 3 | Gyc32E |
| FBgn0010441 | 3 | pll |
| FBgn0011817 | 3 | nmo |
| FBgn0013987 | 3 | MAPk-Ak2 |
| FBgn0015765 | 3 | p38a |
| FBgn0017581 | 3 | Lk6 |
| FBgn0020621 | 3 | Pkn |
| FBgn0023169 | 3 | AMPKalpha |
| FBgn0024846 | 3 | p38b |
| FBgn0025625 | 3 | Sik2 |
| FBgn0025743 | 3 | mbt |
| FBgn0026063 | 3 | KP78b |
| FBgn0026064 | 3 | KP78a |
| FBgn0027497 | 3 | Madm |
| FBgn0028741 | 3 | fab1 |
| FBgn0031299 | 3 | CG4629 |
| FBgn0031784 | 3 | CG9222 |
| FBgn0033915 | 3 | CG8485 |
| FBgn0034568 | 3 | CG3216 |
| FBgn0034950 | 3 | Pask |
| FBgn0036368 | 3 | CG10738 |
| FBgn0036544 | 3 | sff |
| FBgn0037098 | 3 | Wnk |
| FBgn0038167 | 3 | Lkb1 |
| FBgn0038630 | 3 | CG14305 |
| FBgn0039083 | 3 | CG10177 |
| FBgn0040056 | 3 | CG17698 |
| FBgn0044826 | 3 | Pak3 |
| FBgn0046706 | 3 | Haspin |
| FBgn0051183 | 3 | CG31183 |
| FBgn0052666 | 3 | Drak |
| FBgn0052703 | 3 | Erk7 |
| FBgn0052944 | 3 | CG32944 |
| FBgn0085386 | 3 | CG34357 |
| FBgn0259712 | 3 | CG42366 |
| FBgn0260399 | 3 | gwl |
| FBgn0260934 | 3 | par-1 |
| FBgn0261278 | 3 | grp |
| FBgn0261360 | 3 | CG42637 |
| FBgn0261387 | 3 | CG17528 |
| FBgn0261456 | 3 | hpo |
| FBgn0261854 | 3 | aPKC |
| FBgn0262738 | 3 | norpA |
| FBgn0262866 | 3 | S6kII |
| FBgn0263395 | 3 | hppy |
| FBgn0266136 | 3 | Gyc76C |
| FBgn0267339 | 3 | p38c |
| FBgn0267390 | 3 | dop |
| FBgn0267698 | 3 | Pak |
| FBgn0283657 | 3 | Tlk |
| FBgn0002466 | 3 | sti |
| FBgn0010303 | 3 | hep |
| FBgn0024227 | 3 | aurB |
| FBgn0026181 | 3 | Rok |
Males
kable(topBlupSoloM, caption = 'GO-TBLUP Genes', "simple")| flybase | count | gene |
|---|---|---|
| FBgn0036046 | 4 | Ilp2 |
| FBgn0086687 | 4 | Desat1 |
| FBgn0283499 | 4 | InR |
| FBgn0020386 | 4 | Pdk1 |
| FBgn0024248 | 3 | chico |
| FBgn0261873 | 3 | sdt |
| FBgn0037874 | 3 | Tctp |
Comparison
allSolo <- cbind(topBlupSoloF[1:7], topBlupSoloM)
names(allSolo) <- c('Female Gene', 'Count', 'Name', 'Male Gene', 'Count', 'Name')
kable(allSolo, caption = 'GO-TBLUP Gene Comparison', "simple")| Female Gene | Count | Name | Male Gene | Count | Name |
|---|---|---|---|---|---|
| FBgn0010379 | 5 | Akt1 | FBgn0036046 | 4 | Ilp2 |
| FBgn0003731 | 5 | Egfr | FBgn0086687 | 4 | Desat1 |
| FBgn0025595 | 4 | AkhR | FBgn0283499 | 4 | InR |
| FBgn0262103 | 4 | Sik3 | FBgn0020386 | 4 | Pdk1 |
| FBgn0283499 | 4 | InR | FBgn0024248 | 3 | chico |
| FBgn0020386 | 4 | Pdk1 | FBgn0261873 | 3 | sdt |
| FBgn0028484 | 4 | Ack | FBgn0037874 | 3 | Tctp |
Looking at both sexes together, the only two genes that are found in both rankings are InR and Pdk1. Coincidentally, both are significantly involved genes for both sexes.
Intuitively, both are heavily involved in carbohydrate modification activity.
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