Last updated: 2018-10-08
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Library
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(reshape2)
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0 ✔ purrr 0.2.5
✔ tibble 1.4.2 ✔ dplyr 0.7.6
✔ tidyr 0.8.1 ✔ stringr 1.3.1
✔ readr 1.1.1 ✔ forcats 0.3.0
── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(VennDiagram)
Loading required package: grid
Loading required package: futile.logger
library(data.table)
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
The following object is masked from 'package:purrr':
transpose
The following objects are masked from 'package:reshape2':
dcast, melt
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
Permuted Results from APA:
nuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header = T)
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T)
I want to use a buzz swarm plot to plot peak usage for some of the top QTLs. I can use the examples I gave Tony.
Nuclear:
* peak305794, sid: 7:128635754
Total:
Peak: peak228606, SID 3:150302010
Peak: peak152751, SID 19:4236475
I need to pull out the genotypes for each snp and the corresponding phenotype. I want to make a python script that I can give a snp and a peak and it will make a table with the genotypes and phenotypes for the necessary gene snp pair.
geno3_m=fread("../data/apaExamp/geno3_150302010.txt", header = T) %>% dplyr::select(starts_with("NA")) %>% t
geno3df= data.frame(geno3_m) %>% separate(geno3_m, into=c("geno", "dose", "extra"), sep=":") %>% dplyr::select(dose) %>% rownames_to_column(var="ind")
apaphen228606_m= fread("../data/apaExamp/Total.peak228606.txt", header = T) %>% dplyr::select(starts_with("NA")) %>% t
apaphen228606_df=data.frame(apaphen228606_m) %>% rownames_to_column(var="ind")
toplotAPA=geno3df %>% inner_join(apaphen228606_df, by="ind")
toplotAPA$dose= as.factor(toplotAPA$dose)
colnames(toplotAPA)= c("ind", "Genotype", "APA")
EIF2A_APAex=ggplot(toplotAPA, aes(y=APA, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="APA phenotype", title="Total APA: Peak 228606, EIF2A") + scale_fill_brewer(palette="RdPu")
ggsave("../output/plots/EIF2a_APA.png", EIF2A_APAex)
Saving 7 x 5 in image
This is in the gene EIF2A, I need to find this in the eQTL data. The ensg id for this gene is ENSG00000144895.
RNAseqEIF2A_m=read.table("../data/apaExamp/RNAseq.phenoEIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
RNAseqEIF2A_df= data.frame(RNAseqEIF2A_m) %>% rownames_to_column("ind")
plotRNA=geno3df %>% inner_join(RNAseqEIF2A_df, by="ind")
plotRNA$dose= as.factor(plotRNA$dose)
colnames(plotRNA)= c("ind", "Genotype", "Expression")
EIF2A_RNAex=ggplot(plotRNA, aes(y=Expression, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="Normalized Expression", title="Gene Expression: EIF2A") + scale_fill_brewer(palette="RdPu")
ggsave("../output/plots/EIF2a_RNA.png", EIF2A_RNAex)
Saving 7 x 5 in image
Try this in protein:
ProtEIF2A_m=read.table("../data/apaExamp/ProtEIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
ProtEIF2A_df= data.frame(ProtEIF2A_m) %>% rownames_to_column("ind")
plotProt=geno3df %>% inner_join(ProtEIF2A_df, by="ind")
plotProt$dose= as.factor(plotProt$dose)
colnames(plotProt)= c("ind", "Genotype", "Prot_level")
IF2A_Protex= ggplot(plotProt, aes(y=Prot_level, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="Normalized Protein Level", title="Protein Level: EIF2A") +scale_fill_brewer(palette="RdPu")
ggsave("../output/plots/EIF2a_Prot.png", IF2A_Protex)
Saving 7 x 5 in image
multphenoEIF2a=plot_grid(EIF2A_APAex,IF2A_Protex,EIF2A_RNAex,nrow=1)
ggsave("../output/plots/EIF2a_multpheno.png", multphenoEIF2a, width=15, height=5)
Do this with 4su 60:
have to remove the #
su60_EIF2A_m=read.table("../data/apaExamp/Foursu60EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
su60_EIF2A_df= data.frame(su60_EIF2A_m) %>% rownames_to_column("ind")
plot4su60=geno3df %>% inner_join(su60_EIF2A_df, by="ind")
plot4su60$dose= as.factor(plot4su60$dose)
colnames(plot4su60)= c("ind", "Genotype", "su60")
EIF2A_4su60ex=ggplot(plot4su60, aes(y=su60, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="4su60", title="4su 60min Value: EIF2A") + scale_fill_brewer(palette="RdPu") + theme_classic()
ggsave("../output/plots/EIF2a_4su60.png", EIF2A_4su60ex)
Saving 7 x 5 in image
Geuvadis RNA
rnaG_EIF2A_m=read.table("../data/apaExamp/RNA_GEU_EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
rnaG_EIF2A_df= data.frame(rnaG_EIF2A_m) %>% rownames_to_column("ind")
plotRNAg=geno3df %>% inner_join(rnaG_EIF2A_df, by="ind")
plotRNAg$dose= as.factor(plotRNAg$dose)
colnames(plotRNAg)= c("ind", "Genotype", "RNAg")
EIF2A_RNAgex=ggplot(plotRNAg, aes(y=RNAg, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="RNA expression", title="RNA Expression Geuvadis: EIF2A") + scale_fill_brewer(palette="RdPu")
ggsave("../output/plots/EIF2a_RNAg.png", EIF2A_RNAgex)
Saving 7 x 5 in image
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_0.9.3 data.table_1.11.8 VennDiagram_1.6.20
[4] futile.logger_1.4.3 forcats_0.3.0 stringr_1.3.1
[7] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[10] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[13] tidyverse_1.2.1 reshape2_1.4.3 workflowr_1.1.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35
[4] colorspace_1.3-2 htmltools_0.3.6 yaml_2.2.0
[7] rlang_0.2.2 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.7.0
[13] RColorBrewer_1.1-2 lambda.r_1.2.3 modelr_0.1.2
[16] readxl_1.1.0 bindrcpp_0.2.2 bindr_0.1.1
[19] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[22] cellranger_1.1.0 rvest_0.3.2 R.methodsS3_1.7.1
[25] evaluate_0.11 labeling_0.3 knitr_1.20
[28] broom_0.5.0 Rcpp_0.12.19 formatR_1.5
[31] backports_1.1.2 scales_1.0.0 jsonlite_1.5
[34] hms_0.4.2 digest_0.6.17 stringi_1.2.4
[37] rprojroot_1.3-2 cli_1.0.1 tools_3.5.1
[40] magrittr_1.5 lazyeval_0.2.1 futile.options_1.0.1
[43] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[46] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[49] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.8
[52] R6_2.3.0 nlme_3.1-137 git2r_0.23.0
[55] compiler_3.5.1
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