Last updated: 2021-09-26
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Knit directory: fitnessGWAS/
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library(tidyverse)
library(DT)
library(kableExtra)
kable_table <- function(df) { # cool tables
kable(df, "html") %>%
kable_styling(full_width = FALSE) %>%
scroll_box(height = "300px")
}
my_data_table <- function(df){ # Make html tables:
datatable(
df, rownames=FALSE,
autoHideNavigation = TRUE,
extensions = c("Scroller", "Buttons"),
options = list(
dom = 'Bfrtip',
deferRender=TRUE,
scrollX=TRUE, scrollY=400,
scrollCollapse=TRUE,
buttons =
list('pageLength', 'colvis', 'csv', list(
extend = 'pdf',
pageSize = 'A4',
orientation = 'landscape',
filename = 'TWAS_sig_genes')),
pageLength = 50
)
)
}
sig_transcripts_table <- read_csv("data/derived/TWAS/TWAS_results.csv") %>%
filter(LFSR_FE < 0.01 | LFSR_FL < 0.01 | LFSR_ME < 0.01 | LFSR_ML < 0.01)
tallies <- sig_transcripts_table %>%
select(starts_with("sig_")) %>%
summarise_all(~ sum(!is.na(.x))) %>%
gather(type, n_transcripts) %>%
arrange(-n_transcripts)
rnd<- function(x) format(round(x, 2), nsmall = 2)
big_table <- sig_transcripts_table %>%
select(FBID, gene_name, chromosome, AveExpr, male_bias_in_expression, everything()) %>%
select(-contains("heritability")) %>%
mutate(beta_FE = paste(beta_FE, " (", rnd(LFSR_FE), ")", sep = ""),
beta_FL = paste(beta_FL, " (", rnd(LFSR_FL), ")", sep = ""),
beta_ME = paste(beta_ME, " (", rnd(LFSR_ME), ")", sep = ""),
beta_ML = paste(beta_ML, " (", rnd(LFSR_ML), ")", sep = "")) %>%
select(-contains("LFSR")) %>%
rename(`Gene name` = gene_name,
Chromosome = chromosome,
`Female early` = beta_FE,
`Female late` = beta_FL,
`Male early` = beta_ME,
`Male late` = beta_ML,
`Male bias in expression (logFC)` = male_bias_in_expression,
`Average expression level` = AveExpr) %>%
arrange(-P_sex_antag)The table shows the 99 transcripts that were associated with one or more of the four phenotypes, with a local false sign rate < 0.01 (LFSR; computed by mashr). Columns 5-8 show the regression coefficients (adjusted with mashr in data-driven mode) that relate the line mean transcript abundance to the line mean fitnesses, while columns 9-12 give the local false sign rates that for these coefficients from mashr. The sig_ (signficant) columns provide a binary summary of whether the transcript was significantly pleiotropic between ages and sex classes (see below). Finally, the last five columns give the mixture assignment probabilities from mashr (this time, run in canonical mode).
To make the ‘significance’ columns, we defined significantly antagonistic transcripts as those where the relationship with fitness is significantly positive for one sex (or age class) and significantly negative for the other, with LFSR < 0.01. Similarly, we define significantly concordant transcripts as those where the relationship with fitness is significantly positive for one sex or age class and also significantly positive for the other (LFSR < 0.01). This is quite conservative, because a transcript needs to have a LFSR < 0.01 for two tests, giving two chances for a ‘false negative’. One can sort the table by these columns, for example to see a list of transcripts that showed a significantly antagonistic relationship with fitness in the early-life fitness assays, sort by the sig_SA_early column.
Abbreviations: AC = age concordant, SC = sexually concordant, SA = sexually antagonistic.
big_table %>% my_data_table()Abbreviations: AC = age concordant, SC = sexually concordant, SA = sexually antagonistic. We defined significantly antagonistic transcripts as those where the relationship with fitness is significantly positive for one sex (or age class) and significantly negative for the other, with LFSR < 0.01. Similarly, we define significantly concordant transcripts as those where the relationship with fitness is significantly positive for one sex or age class and also significantly positive for the other (LFSR < 0.01). This is quite conservative, because a transcript needs to have a LFSR < 0.01 for two tests, giving two chances for a ‘false negative’.
tallies %>%
mutate(x = str_remove_all(type, "sig_"),
`Number statistically significant` = n_transcripts) %>%
mutate(x = str_replace_all(x, "AC", "Age concordant"),
x = str_replace_all(x, "AA", "Age antagonistic"),
x = str_replace_all(x, "SC", "Sexually concordant"),
x = str_replace_all(x, "SA", "Sexually antagonistic"),
x = str_replace_all(x, "_females", " (in females)"),
x = str_replace_all(x, "_males", " (in males)"),
x = str_replace_all(x, "_early", " (in early life)"),
x = str_replace_all(x, "_late", " (in late life)")) %>%
rename(`Type of transcript` = x) %>%
select(`Type of transcript`, `Number statistically significant`) %>%
kable_table()| Type of transcript | Number statistically significant |
|---|---|
| Sexually concordant (in late life) | 42 |
| Age concordant (in males) | 37 |
| Age concordant (in females) | 30 |
| Sexually concordant (in early life) | 17 |
| Sexually antagonistic (in late life) | 2 |
| Sexually antagonistic (in early life) | 0 |
| Age antagonistic (in females) | 0 |
| Age antagonistic (in males) | 0 |
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] kableExtra_1.3.4 DT_0.13 forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.0 purrr_0.3.4 readr_2.0.0 tidyr_1.1.0
[9] tibble_3.0.1 ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 svglite_1.2.3 lubridate_1.7.10 lattice_0.20-41
[5] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25 R6_2.4.1
[9] cellranger_1.1.0 backports_1.1.7 reprex_0.3.0 evaluate_0.14
[13] highr_0.8 httr_1.4.1 pillar_1.4.4 gdtools_0.2.2
[17] rlang_0.4.6 readxl_1.3.1 rstudioapi_0.11 whisker_0.4
[21] blob_1.2.1 rmarkdown_2.5 webshot_0.5.2 htmlwidgets_1.5.1
[25] bit_1.1-15.2 munsell_0.5.0 broom_0.5.6 compiler_4.0.3
[29] httpuv_1.5.3.1 modelr_0.1.8 xfun_0.22 systemfonts_0.2.2
[33] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0 viridisLite_0.3.0
[37] fansi_0.4.1 crayon_1.3.4 tzdb_0.1.2 dbplyr_1.4.4
[41] withr_2.2.0 later_1.0.0 grid_4.0.3 nlme_3.1-149
[45] jsonlite_1.7.0 gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0
[49] git2r_0.27.1 magrittr_2.0.1 scales_1.1.1 vroom_1.5.3
[53] cli_2.0.2 stringi_1.5.3 fs_1.4.1 promises_1.1.0
[57] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.0
[61] tools_4.0.3 bit64_0.9-7 glue_1.4.2 crosstalk_1.1.0.1
[65] hms_0.5.3 parallel_4.0.3 yaml_2.2.1 colorspace_1.4-1
[69] rvest_0.3.5 knitr_1.32 haven_2.3.1