Last updated: 2020-01-23

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd 1673f18 brimittleman 2020-01-24 start the prot mediation cant do diff indiv 
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(qvalue)
library(edgeR)
Loading required package: limma
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(limma)
library(MASS)

Attaching package: 'MASS'
The following object is masked from 'package:dplyr':

    select

In this analysis, I will ask if differrecnes in APA are causal for differences in protein.

To deal with multiple phenotypes per gene I will take the highest absolute effect size PAS.

mkdir ../data/mediation_prot
source("../code/mediation_test.R") #Obtain necessary functions

I cant do this because we dont have the protein effect sizes. I need to remodel

ProtComp=read.csv("../data/Khan_prot/Khan_TableS4.csv", stringsAsFactors = F, header = T) %>% dplyr::select(gene.symbol, human.GM18505.protein,human.GM18507.protein,human.GM18516.protein,human.GM19193.protein,human.GM19204.protein,chimp.18358.protein,chimp.18359.protein ,chimp.3659.protein ,chimp.4973.protein,chimp.Pt91.protein ) %>% column_to_rownames(var="gene.symbol") %>% drop_na()

These are normalized values and filtered values. I will create a design matrix and test for differential protein.

ProtMeta=as.data.frame(cbind(indiv=colnames(ProtComp), Species=c(rep("H",5), rep("C",5))))

Species <- factor(ProtMeta$Species)
design <- model.matrix(~ 0 + Species)
head(design)
  SpeciesC SpeciesH
1        0        1
2        0        1
3        0        1
4        0        1
5        0        1
6        1        0
colnames(design) <- gsub("Species", "", dput(colnames(design)))
c("SpeciesC", "SpeciesH")

Make a object

labels <- paste(ProtMeta$Species,ProtMeta$indiv, sep=" ")

Use classic linear model:

gene1_lm= summary(lm(as.numeric(ProtComp[1,])~Species))
gene1_lm$coefficients[2,1]
[1] -0.007560936
gene1_lm$coefficients[2,4]
[1] 0.9405155

human is 1.

Effect size is -0.007561, pvalue is .9405

Run for all genes:

Ppvals <- c()
Peffect <- c()
for (i in 1:dim(ProtComp)[1]) {
model <- summary(lm(as.numeric(ProtComp[i,]) ~ Species))
pval <-model$coefficients[2,4]
effect= model$coefficients[2,1]
Ppvals <- c(Ppvals, pval)
Peffect <- c(Peffect,effect)
}

Join results:

Pres=as.data.frame(cbind(genes=rownames(ProtComp), P=Ppvals, Ef=Peffect))
Pres$P=as.numeric(as.character(Pres$P))

Pres$Ef=as.numeric(as.character(Pres$Ef))

adjP=qvalue(Pres$P)
PresF= Pres %>% mutate(adjPval=adjP$pvalues)

SigP=PresF %>% filter(adjPval <=.05)
nrow(SigP)
[1] 838
NotSigP=PresF %>% filter(adjPval >.05)
nrow(NotSigP)
[1] 1830

I can use this for the mediation. - effect size in down in human.

The individuals are not the same. I cannot do this analysis.


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:
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 [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] MASS_7.3-51.1   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 edgeR_3.24.0    limma_3.38.2   
[13] qvalue_2.14.0   workflowr_1.5.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 locfit_1.5-9.1   reshape2_1.4.3   splines_3.5.1   
 [5] haven_1.1.2      lattice_0.20-38  colorspace_1.3-2 generics_0.0.2  
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[13] pillar_1.3.1     withr_2.1.2      glue_1.3.0       modelr_0.1.2    
[17] readxl_1.1.0     plyr_1.8.4       munsell_0.5.0    gtable_0.2.0    
[21] cellranger_1.1.0 rvest_0.3.2      evaluate_0.12    knitr_1.20      
[25] httpuv_1.4.5     broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  rstudioapi_0.10  assertthat_0.2.0
[49] rmarkdown_1.10   httr_1.3.1       R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1