Last updated: 2024-01-16

Checks: 7 0

Knit directory: dgrp-starve/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20221101) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 1aeb6cb. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .snakemake/
    Ignored:    code/methodComp/bglr/err-bglr-f.5381.err
    Ignored:    code/methodComp/bglr/err-bglr-m.5382.err
    Ignored:    code/methodComp/m/meth-m.4676.err
    Ignored:    code/methodComp/m/meth-m.4685.err
    Ignored:    code/methodComp/method-f.4751.out
    Ignored:    data/fb/
    Ignored:    data/snake/
    Ignored:    snake/.snakemake/
    Ignored:    snake/GOfile.yaml
    Ignored:    snake/ReadMe.md
    Ignored:    snake/code/bayesC/
    Ignored:    snake/code/misc/
    Ignored:    snake/customL.Rds
    Ignored:    snake/data/
    Ignored:    snake/datafile.yaml
    Ignored:    snake/dgrp.yaml
    Ignored:    snake/fig/
    Ignored:    snake/fullGO.yaml
    Ignored:    snake/gofig.yaml
    Ignored:    snake/guide/
    Ignored:    snake/logs/
    Ignored:    snake/slurm/
    Ignored:    snake/smake.sbatch
    Ignored:    snake/snubnose.sbatch
    Ignored:    snake/zz_lost/
    Ignored:    zz_lost/

Untracked files:
    Untracked:  analysis/allotter.R
    Untracked:  analysis/old_index.Rmd.Rmd
    Untracked:  analysis/sparseComp.Rmd
    Untracked:  forester.R
    Untracked:  malegofind.R
    Untracked:  pippinRMDbackup.R
    Untracked:  snake/code/binner.R
    Untracked:  snake/code/combine_GO.R
    Untracked:  snake/code/dataFinGO.R
    Untracked:  snake/code/datafile.yaml
    Untracked:  snake/code/filterNcombine_GO.R
    Untracked:  snake/code/filter_GO.R
    Untracked:  snake/code/go/
    Untracked:  snake/code/idTEMP.R
    Untracked:  snake/code/imstuff.R
    Untracked:  snake/code/method/bayesHome.R
    Untracked:  snake/code/method/multiplotGO.Rmd
    Untracked:  snake/code/scheming.R
    Untracked:  snake/code/scripts/
    Untracked:  snake/code/srfile.yaml

Unstaged changes:
    Modified:   analysis/Method/BayesC.Rmd
    Modified:   analysis/bigGO.Rmd
    Modified:   analysis/goReport.Rmd
    Modified:   analysis/index.Rmd
    Modified:   snake/code/method/bayesGO.R
    Modified:   snake/code/method/goFish.R
    Modified:   snake/code/method/varbvs.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/blupUp.Rmd) and HTML (docs/blupUp.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 1aeb6cb nklimko 2024-01-16 wflow_publish(“analysis/blupUp.Rmd”)
html 7327d20 nklimko 2024-01-16 Build site.
Rmd 878d371 nklimko 2024-01-16 wflow_publish(“analysis/blupUp.Rmd”)

#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')

Surface Level / Sanity Check / Congruence / On course

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")
Female BayesC/BLUP Comparison
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")
Male BayesC/BLUP Comparison
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

All Data
plot_grid(gg[[1]], ncol=1)

Version Author Date
7327d20 nklimko 2024-01-16
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

All Data
plot_grid(gg[[2]], ncol=1)

Version Author Date
7327d20 nklimko 2024-01-16
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")
GO-TBLUP Genes
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")
GO-TBLUP Genes
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")
GO-TBLUP Gene Comparison
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 bot h sexes togethe r, the only tw o genes that are found in both rankings are InR and Pdk1. Coincidentally, both are significantly involved genes for both sexes.
  • InR is an insulin receptor.

  • Pdk is a pyruvate dehydrogenase kinase.

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