Last updated: 2019-09-06
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Knit directory: apaQTL/analysis/
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File | Version | Author | Date | Message |
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html | cd60f50 | brimittleman | 2019-06-20 | Build site. |
Rmd | 6df08b6 | brimittleman | 2019-06-20 | change analysis to include not tested in total as nuc spec |
html | 743c9c6 | brimittleman | 2019-06-17 | Build site. |
Rmd | bc3ddae | brimittleman | 2019-06-17 | add effect size nuc spec |
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
I am interested in seeing if the nuclear specific apaQTL snps have higher eQTL effect sizes than the shared apaQTLs. This comes from my expectation that the nuclear specific apaQTLs are more likely to be eQTLs.
I am using the nuclear specific apa QTLs including those that are not tested in total from this analysis.
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc.txt RNA ../data/NucSpeceQTLeffect/NuclearSpecficApaQTL_eQTLRes.txt
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/SharedApaQTL_nottestinc.txt RNA ../data/NucSpeceQTLeffect/SharedApaQTL_eQTLRes.txt
Evaluate results:
eqtl4nucqtl=read.table("../data/NucSpeceQTLeffect/NuclearSpecficApaQTL_eQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F) %>% mutate(set="Nuclear")
eqtl4sharedqtl=read.table("../data/NucSpeceQTLeffect/SharedApaQTL_eQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F)%>% mutate(set="Shared")
eQTLsboth=eqtl4nucqtl %>% bind_rows(eqtl4sharedqtl)
Plot:
ggplot(eQTLsboth,aes(x=set, y=abs(effect)))+ geom_boxplot()
Version | Author | Date |
---|---|---|
cd60f50 | brimittleman | 2019-06-20 |
ggplot(eQTLsboth,aes(x=set, y=pval))+ geom_boxplot()+ geom_jitter()
Version | Author | Date |
---|---|---|
cd60f50 | brimittleman | 2019-06-20 |
No effect. What if we jsut look at nominally sig snps.
eQTLsbothSig=eQTLsboth %>% filter(pval<.05)
ggplot(eQTLsbothSig,aes(x=set, y=abs(effect),fill=set))+ geom_boxplot() + geom_jitter()
Version | Author | Date |
---|---|---|
cd60f50 | brimittleman | 2019-06-20 |
t.test(abs(effect) ~ set, data = eQTLsbothSig, alternative="greater")
Welch Two Sample t-test
data: abs(effect) by set
t = 0.25731, df = 28.521, p-value = 0.3994
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
-0.1004697 Inf
sample estimates:
mean in group Nuclear mean in group Shared
0.5655281 0.5476097
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc.txt Protein ../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc_pQTLRes.txt
python phenoQTLfromlist.py ../data/NucSpeceQTLeffect/SharedApaQTL_nottestinc.txt Protein ../data/NucSpeceQTLeffect/SharedApaQTLnottestedinc_pQTLRes.txt
pqtl4nucqtl=read.table("../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc_pQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F) %>% mutate(set="Nuclear")
pqtl4sharedqtl=read.table("../data/NucSpeceQTLeffect/SharedApaQTLnottestedinc_pQTLRes.txt", col.names = c("gene", "snp", "dist", "pval", "effect"),stringsAsFactors = F)%>% mutate(set="Shared")
pQTLsboth=pqtl4nucqtl %>% bind_rows(pqtl4sharedqtl)
ggplot(pQTLsboth,aes(x=set, y=abs(effect)))+ geom_boxplot()
Version | Author | Date |
---|---|---|
cd60f50 | brimittleman | 2019-06-20 |
ggplot(pQTLsboth,aes(x=set, y=pval))+ geom_boxplot()+ geom_jitter()
Version | Author | Date |
---|---|---|
cd60f50 | brimittleman | 2019-06-20 |
pQTLsbothSig=pQTLsboth %>% filter(pval<.05)
ggplot(pQTLsbothSig,aes(x=set, y=abs(effect),fill=set))+ geom_boxplot() + geom_jitter()
Version | Author | Date |
---|---|---|
cd60f50 | brimittleman | 2019-06-20 |
Not significant.
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] workflowr_1.4.0 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 plyr_1.8.4 compiler_3.5.1
[5] pillar_1.3.1 git2r_0.25.2 highr_0.7 tools_3.5.1
[9] digest_0.6.18 lubridate_1.7.4 jsonlite_1.6 evaluate_0.12
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2
[17] rlang_0.4.0 cli_1.1.0 rstudioapi_0.10 yaml_2.2.0
[21] haven_1.1.2 withr_2.1.2 xml2_1.2.0 httr_1.3.1
[25] knitr_1.20 hms_0.4.2 generics_0.0.2 fs_1.3.1
[29] rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5 glue_1.3.0
[33] R6_2.3.0 readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2
[37] magrittr_1.5 whisker_0.3-2 backports_1.1.2 scales_1.0.0
[41] htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3 stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0
[49] broom_0.5.1 crayon_1.3.4