Mediation analysis

Last updated: 2020-01-14

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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File	Version	Author	Date	Message
Rmd	3cce3da	brimittleman	2020-01-14	start mediation analysis

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(limma)

In this analysis, I will ask if differrecnes in APA or splicing are causal for differences in expression. I will use midiation analysis as implemented in (https://lauren-blake.github.io/Regulatory_Evol/analysis/Final_effect_size_human_chimp.html) using Joyces package mediation.

Notes:

need one value for gene, sample, expression values, mol pheno values (normalized and non normalized)
uses ashR,vashr,medinome package
df= sample -2 (4)
run for DE and non DE genes seperatly

Ittai’s version of the analysis: https://ittaieres.github.io/HiCiPSC/gene_expression.html#now,_get_the_appropriate_data_and_actually_run_the_mediation_analysis

For this version I do not need the package. I only need limma. I will try this first.

To deal with multiple phenotypes per gene I will take the highest absolute effect size cluster or PAS for splicing and APA respectively.

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

Now I need to pull in the expression, apa, and splicing data.
- expression= log2RPKM, and adju.P.val - normalized nuclear apa phenotype
- splicing normalized clusters

Expression and APA

#expression
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID,Gene.name)
ExpRes=read.table("../data/DiffExpression/DEtested_allres.txt", header = F, stringsAsFactors = F, col.names = c("Gene_stable_ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name,adj.P.Val )
ExpNorm=read.table("../data/DiffExpression/NormalizedExpressionPassCutoff.txt", header = T, stringsAsFactors = F) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID)
#join indiv with adjust pval
Exp=ExpRes %>% inner_join(ExpNorm, by="Gene.name") %>% rename("gene"= Gene.name) %>% select(gene, adj.P.Val, NA18498, NA18499, NA18502, NA18504, NA18510,NA18523,NA18358, NA3622,NA3659,NA4973,NAPT30,NAPT91)

#Chr    start   end ID  NA18498_N   NA18499_N   NA18502_N   NA18504_N   NA18510_N   NA18523_N   NA18358_N   NA3622_N    NA3659_N    NA4973_N    NApt30_N    NApt91_N
#apa:  
apaN= read.table("../data/Pheno_5perc_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Nuclear.txt.gz.phen_AllChrom", col.names = c("chr", "start", "end", "id","NA18498_APA", "NA18499_APA", "NA18502_APA", "NA18504_APA", "NA18510_APA","NA18523_APA", "NA18358_APA", "NA3622_APA","NA3659_APA", "NA4973_APA","NAPT30_APA","NAPT91_APA")) %>% separate(id, into=c("ch", "st", "en","id2"),sep=":") %>% separate(id2, into=c("gene", "strand","id3"),sep="_") %>% separate(id3, into=c("loc", "disc", "PAS"), sep="-") %>% select(gene, PAS,contains("NA"))
PASMeta=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene)
apaRes= read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") %>% separate(cluster, into=c("chr","gene"),sep=":")
apaPASres=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Human', 'Chimp','deltaPAU')) %>% filter(intron != "intron") %>% separate(intron, into=c("chr","start", "end","gene"), sep=":") 
apaPASres$start=as.integer(apaPASres$start)
apaPASres$end=as.integer(apaPASres$end)
apaPASres$deltaPAU=as.numeric(apaPASres$deltaPAU)
apaPASres=apaPASres%>% inner_join(PASMeta,by=c("chr", "start", "end", "gene"))
#problem if there are 2 pas then the are opposite but same value - do with all one direction for 
apaPASres_topPos= apaPASres %>% group_by(gene) %>% top_n(1,abs(deltaPAU)) %>% top_n(1,deltaPAU)
apaPASres_topNeg= apaPASres %>% group_by(gene) %>% top_n(1,abs(deltaPAU)) %>% top_n(-1,deltaPAU)
#join with pvalue: 
apaPASres_topPos_pval=apaPASres_topPos %>%  inner_join(apaRes, by=c("gene"))  %>% dplyr::select( PAS, p.adjust)

Adding missing grouping variables: `gene`

apaPASres_topNeg_pval=apaPASres_topNeg %>%  inner_join(apaRes, by=c("gene")) %>% dplyr::select( PAS, p.adjust)

Adding missing grouping variables: `gene`

#looks like the naming convention is not consistent...
apaPASres_topPos_pval_normAPA=apaPASres_topPos_pval %>% inner_join(apaN,by=c("PAS","gene")) %>% dplyr::select(-PAS,-p.adjust)
apaPASres_topNeg_pval_normAPA=apaPASres_topNeg_pval %>% inner_join(apaN,by=c("PAS","gene"))%>% dplyr::select(-PAS,-p.adjust)


#create a dataframe with expression and apa
APA_posandExp=Exp %>% inner_join(apaPASres_topPos_pval_normAPA, by="gene")
APA_negandExp=Exp %>% inner_join(apaPASres_topNeg_pval_normAPA,by="gene")

First do the positive:

Seperate De and not De

is_de <- which(APA_posandExp$adj.P.Val < .05)
isnot_de <- which(APA_posandExp$adj.P.Val >= .05)

gvec <- APA_posandExp$genes
Exp_pos <- APA_posandExp %>% select(-gene,-adj.P.Val, -contains("APA"))
APA_pos <- APA_posandExp %>% select(contains("APA"))

# metadata label
species <- factor(c("H","H","H","H","H","H","C","C","C","C","C","C"))
batch <- factor(c("A", "A", "B", "A", "B", "B", "B", "B","A", "A","A","B"))

metadata <- data.frame(sample=names(Exp_pos),
                       species=species,
                       batch=batch)

Compute indirect effects:

fit_de_pos <- test_mediation(exprs = Exp_pos[is_de,], 
                         fixed_covariates = list(species=metadata$species,
                                                 batch=metadata$batch),
                         varying_covariate = APA_pos[is_de,])

fit_node_pos <- test_mediation(exprs = Exp_pos[isnot_de,], 
                           fixed_covariates = list(species=metadata$species,
                                                   batch=metadata$batch),
                           varying_covariate = APA_pos[isnot_de,])

DEdat <- data.frame(bf=fit_de_pos$tau, af=fit_de_pos$tau_prime)
#DEdat$color <- ifelse(DEdat$significance==TRUE, "red", "black")
plot(x=DEdat$bf, y=DEdat$af, ylab="Effect Size After Controlling for APA", xlab="Effect Size Before Controlling for APA", main="Effect of APA on Expression Divergence in DE genes", pch=16, cex=0.6, xlim=c(-10, 10), ylim=c(-10,10))

abline(0, 1)
abline(h=0)
abline(v=0)

Not DE:

#Visualization for non DE genes:
noDEdat <- data.frame(bf=fit_node_pos$tau, af=fit_node_pos$tau_prime)
#noDEdat$color <- ifelse(noDEdat$significance==TRUE, "red", "black")
plot(x=noDEdat$bf, y=noDEdat$af, ylab="Effect Size After Controlling for APA", xlab="Effect Size Before Controlling for APA", main="Effect of APA on Expression Divergence in non-DE genes", pch=16, cex=0.6, xlim=c(-10, 10), ylim=c(-10,10), adj=0.6)
abline(0, 1)
abline(h=0)
abline(v=0)

Negative: