GObayesC Report

Last updated: 2023-12-12

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.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20221101)

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.

Session information: recorded

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

Cache: none

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

File paths: relative

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

Repository version: 4db970c

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 4db970c. 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/Snakefile.yaml
    Ignored:    snake/bayesCheck.R
    Ignored:    snake/bayesTest.Rds
    Ignored:    snake/binner2.R
    Ignored:    snake/code/misc/
    Ignored:    snake/data/
    Ignored:    snake/datafile.yaml
    Ignored:    snake/dgrp.yaml
    Ignored:    snake/f_file.yaml
    Ignored:    snake/goNames.sh
    Ignored:    snake/goPost/
    Ignored:    snake/gofig.yaml
    Ignored:    snake/gospace.R
    Ignored:    snake/h2_synth.R
    Ignored:    snake/labMake.R
    Ignored:    snake/logs/
    Ignored:    snake/meta.sh
    Ignored:    snake/metaList
    Ignored:    snake/newlines
    Ignored:    snake/note1
    Ignored:    snake/pipelines/
    Ignored:    snake/pipoff.R
    Ignored:    snake/rawHits
    Ignored:    snake/s1.sh
    Ignored:    snake/s2.sh
    Ignored:    snake/s3.sh
    Ignored:    snake/slurm/
    Ignored:    snake/smake.sbatch
    Ignored:    snake/snubnose.sbatch
    Ignored:    snake/space.yaml
    Ignored:    snake/sparsecomp.R
    Ignored:    snake/sprase/
    Ignored:    snake/srfile.yaml
    Ignored:    snake/temp.Rds
    Ignored:    snake/temp9/
    Ignored:    snake/temporaire-rewrite.R
    Ignored:    snake/trimHits
    Ignored:    snake/vavrfile.yaml
    Ignored:    snake/vavrmake
    Ignored:    snake/zz_lost/
    Ignored:    zz_lost/

Untracked files:
    Untracked:  analysis/allotter.R
    Untracked:  analysis/old_index.Rmd.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/srfile.yaml

Unstaged changes:
    Modified:   analysis/Method/BayesC.Rmd
    Modified:   analysis/bigGO.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/pippin.Rmd) and HTML (docs/pippin.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	4db970c	nklimko	2023-12-12	wflow_publish(“analysis/pippin.Rmd”)
html	959eff1	nklimko	2023-12-12	Build site.
Rmd	f1b715f	nklimko	2023-12-12	wflow_publish(“analysis/pippin.Rmd”)
html	43af6b9	nklimko	2023-12-05	Build site.
Rmd	d1987b3	nklimko	2023-12-05	wflow_publish(“analysis/pippin.Rmd”)

load('snake/data/topTables.Rdata')

options(knitr.kable.NA = '')

Summary

We wanted to ensure that the genes of interest found multiple times across GO terms were significant in our models by looking at their posterior inclusion probability in each subset. The cutoff for associated genes was set to 0.5 while the cutoff for non-associated genes was set to 0.7. We then tallied the filtered genes as subsets of their individual distribution and then altogether to find genes that appeared scarcely in both.

Our findings here are mostly consistent with initial findings. Notably, the Adipokinetic hormone Receptor(AkhR) is a top hit while the hormone itself(Akh) is not found in either sex.

Female Genes

kable(fGO_top, caption= 'GO genes', 'simple') %>%
  kable_styling(full_width = FALSE, position = "float_left")

Warning in kable_styling(., full_width = FALSE, position = "float_left"):
Please specify format in kable. kableExtra can customize either HTML or LaTeX
outputs. See https://haozhu233.github.io/kableExtra/ for details.

GO genes
gene	count	name
FBgn0025595	8	AkhR
FBgn0000575	7	emc
FBgn0262738	4	norpA
FBgn0003205	4	Ras85D
FBgn0003731	4	Egfr
FBgn0003463	3	sog
FBgn0003719	3	tld
FBgn0036449	3	bmm

kable(fNON_top, caption= 'non-GO genes', 'simple') %>%
  kable_styling(full_width = FALSE, position = "float_right")

Warning in kable_styling(., full_width = FALSE, position = "float_right"):
Please specify format in kable. kableExtra can customize either HTML or LaTeX
outputs. See https://haozhu233.github.io/kableExtra/ for details.

non-GO genes
gene	count	name
FBgn0025595	8	AkhR
FBgn0000575	7	emc
FBgn0262738	4	norpA
FBgn0003731	4	Egfr
FBgn0003463	3	sog

kable(fALL_topGenes, caption = 'Top Female Genes', "simple")

Top Female Genes
gene	count	name
FBgn0025595	16	AkhR
FBgn0000575	14	emc
FBgn0262738	8	norpA
FBgn0003731	8	Egfr
FBgn0003463	6	sog
FBgn0003205	4	Ras85D
FBgn0004635	4	rho
FBgn0039114	4	Lsd-1
FBgn0003218	4	rdgB
FBgn0026207	3	mbo
FBgn0003719	3	tld
FBgn0036449	3	bmm

Male Genes

kable(mGO_top, caption= 'GO genes',  'simple') %>%
  kable_styling(full_width = FALSE, position = "float_left")

Warning in kable_styling(., full_width = FALSE, position = "float_left"):
Please specify format in kable. kableExtra can customize either HTML or LaTeX
outputs. See https://haozhu233.github.io/kableExtra/ for details.

GO genes
gene	count	name
FBgn0261873	9	sdt
FBgn0261854	6	aPKC
FBgn0025595	6	AkhR
FBgn0026192	5	par-6
FBgn0265778	5	PDZ-GEF
FBgn0036046	5	Ilp2
FBgn0086687	4	Desat1
FBgn0011661	3	Moe
FBgn0036449	3	bmm
FBgn0000163	3	baz
FBgn0003205	3	Ras85D

kable(mNON_top, caption= 'non-GO genes', 'simple') %>%
  kable_styling(full_width = FALSE, position = "float_right")

Warning in kable_styling(., full_width = FALSE, position = "float_right"):
Please specify format in kable. kableExtra can customize either HTML or LaTeX
outputs. See https://haozhu233.github.io/kableExtra/ for details.

non-GO genes
gene	count	name
FBgn0261873	9	sdt
FBgn0025595	6	AkhR
FBgn0265778	5	PDZ-GEF

kable(mALL_topGenes, caption = 'Top Female Genes', "simple")

Top Female Genes
gene	count	name
FBgn0261873	18	sdt
FBgn0025595	12	AkhR
FBgn0265778	10	PDZ-GEF
FBgn0261854	6	aPKC
FBgn0036046	6	Ilp2
FBgn0086687	6	Desat1
FBgn0026192	5	par-6
FBgn0032264	4	Lip4
FBgn0020386	4	Pdk1
FBgn0033205	4	CG2064
FBgn0011661	3	Moe
FBgn0036449	3	bmm
FBgn0000163	3	baz
FBgn0003205	3	Ras85D

Posterior Inclusion Of Probability Plots

Below are the PIP plots from the top GO terms separated by sex. These were made on the full model( all 198 lines, no CV) rather than cross validation for prediction. The left column contains PIP plots for the distribution associated with the GO-related genes, while the right column contains PIP plot for all other genes. Each row is a GO term.

Female Plots

sex <- 'f'
plotList <- list.files(path=paste0('snake/data/go/26_pip/sex', sex), full.names = TRUE)

ggF <- lapply(plotList, readRDS)

for(i in 1:(length(ggF)/2)){
  b <- 2*i
  a <- b - 1
  print(plot_grid(ggF[[a]], ggF[[b]], ncol=2))
}

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Version	Author	Date
43af6b9	nklimko	2023-12-05

Male Plots

sex <- 'm'
plotList <- list.files(path=paste0('snake/data/go/26_pip/sex', sex), full.names = TRUE)

ggM <- lapply(plotList, readRDS)

for(i in 1:(length(ggM)/2)){
  b <- 2*i
  a <- b - 1
  print(plot_grid(ggM[[a]], ggM[[b]], ncol=2))
}

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12
43af6b9	nklimko	2023-12-05

Version	Author	Date
959eff1	nklimko	2023-12-12

Version	Author	Date
959eff1	nklimko	2023-12-12

Version	Author	Date
959eff1	nklimko	2023-12-12

Version	Author	Date
959eff1	nklimko	2023-12-12

Version	Author	Date
959eff1	nklimko	2023-12-12

Version	Author	Date
959eff1	nklimko	2023-12-12

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

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