Mediation analysis

Last updated: 2020-01-22

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	97ef45e	brimittleman	2020-01-22	add mediation DF
html	346ba87	brimittleman	2020-01-16	Build site.
Rmd	a624a7e	brimittleman	2020-01-16	normalize splice and start mediation code
html	5b4069c	brimittleman	2020-01-15	Build site.
Rmd	c7e1c1f	brimittleman	2020-01-15	add negative
html	4f5d492	brimittleman	2020-01-14	Build site.
Rmd	3cce3da	brimittleman	2020-01-14	start mediation analysis

library(workflowr)

This is workflowr version 1.5.0
Run ?workflowr for help getting started

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)
library(MASS)


Attaching package: 'MASS'

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

    select

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.

mkdir ../data/mediation

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) %>% dplyr::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="-") %>% dplyr::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 %>% dplyr::select(-gene,-adj.P.Val, -contains("APA"))
APA_pos <- APA_posandExp %>% dplyr::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,])

#save as R data object:
save(fit_de_pos, fit_node_pos, is_de, isnot_de, Exp_pos, APA_pos, metadata, gvec, file = "../data/mediation/positive_mediation.rda")

Montecarlo:

I will have to run this seperatly as an Rscript.


sbatch RunPosMCMediation.sh

Load results

mc_pos_de <- readRDS(file = "../data/mediation/mc_de_postive.rds")
mc_pos_node <- readRDS(file = "../data/mediation/mc_node_postive.rds")

##In DE genes
ngenes <- ncol(mc_pos_de)
ab <- fit_de_pos$alpha*fit_de_pos$beta
out <- sapply(1:ngenes, function(g) {
  x <- unlist(mc_pos_de[,g])
  q <- quantile(x, prob = c(.025, .975))
#  q <- quantile(x, prob = c(.005, .995))
  ifelse(0 > q[1] & 0 < q[2], F, T)
})
table(out)

out
FALSE  TRUE 
 2823    85

85/(85+2823) #

[1] 0.02922971

DEdat <- data.frame(bf=fit_de_pos$tau, af=fit_de_pos$tau_prime,significance=out)
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),col=alpha(DEdat$color, 0.6))
legend("topleft", legend=c("95% CI Significant (n=85)", "95% CI Non significant (n=2823)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)

Version	Author	Date
5b4069c	brimittleman	2020-01-15

Not DE:

##In non-DE genes
ngenes <- ncol(mc_pos_node)
ab <- fit_node_pos$alpha*fit_node_pos$beta
out <- sapply(1:ngenes, function(g) {
  x <- unlist(mc_pos_node[,g])
  q <- quantile(x, prob = c(.025, .975))
#  q <- quantile(x, prob = c(.005, .995))
  ifelse(0 > q[1] & 0 < q[2], F, T)
})
table(out)

out
FALSE  TRUE 
 4852   110

110/(110+4852)

[1] 0.02216848

#Visualization for non DE genes:
noDEdat <- data.frame(bf=fit_node_pos$tau, af=fit_node_pos$tau_prime,significance=out)
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,col=alpha(noDEdat$color, 0.6))
legend("topleft", legend=c("95% CI Significant (n=110)", "95% CI Non significant (n=4852)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)

Version	Author	Date
5b4069c	brimittleman	2020-01-15
4f5d492	brimittleman	2020-01-14

Negative:

Seperate De and not De

is_de_neg <- which(APA_negandExp$adj.P.Val < .05)
isnot_de_gen <- which(APA_negandExp$adj.P.Val >= .05)

gvec_neg <- APA_negandExp$genes
Exp_neg <- APA_negandExp %>% dplyr::select(-gene,-adj.P.Val, -contains("APA"))
APA_neg <- APA_negandExp %>%  dplyr::select(contains("APA"))

Compute indirect effects:

fit_de_neg<- test_mediation(exprs = Exp_neg[is_de_neg,], 
                         fixed_covariates = list(species=metadata$species,
                                                 batch=metadata$batch),
                         varying_covariate = APA_neg[is_de_neg,])

fit_node_neg <- test_mediation(exprs = Exp_neg[isnot_de_gen,], 
                           fixed_covariates = list(species=metadata$species,
                                                   batch=metadata$batch),
                           varying_covariate = APA_neg[isnot_de_gen,])

#save as R data object:
save(fit_de_neg, fit_node_neg, is_de_neg, isnot_de_gen, Exp_neg, APA_neg, metadata, gvec_neg, file = "../data/mediation/negative_mediation.rda")

Montecarlo:

I will have to run this seperatly as an Rscript.


sbatch RunNegMCMediation.sh

Pull in results.

mc_neg_de <- readRDS(file = "../data/mediation/mc_de_negative.rds")
mc_neg_node <- readRDS(file = "../data/mediation/mc_node_negative.rds")

##In DE genes
ngenes <- ncol(mc_neg_de)
ab <- fit_de_neg$alpha*fit_de_neg$beta
out <- sapply(1:ngenes, function(g) {
  x <- unlist(mc_neg_de[,g])
  q <- quantile(x, prob = c(.025, .975))
#  q <- quantile(x, prob = c(.005, .995))
  ifelse(0 > q[1] & 0 < q[2], F, T)
})
table(out)

out
FALSE  TRUE 
 2817    91

93/(93+2823) #

[1] 0.031893

DEdat_neg <- data.frame(bf=fit_de_neg$tau, af=fit_de_neg$tau_prime,significance=out)
DEdat_neg$color <- ifelse(DEdat_neg$significance==TRUE, "red", "black")
plot(x=DEdat_neg$bf, y=DEdat_neg$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 (Negative)", pch=16, cex=0.6, xlim=c(-10, 10), ylim=c(-10,10),col=alpha(DEdat$color, 0.6))
legend("topleft", legend=c("95% CI Significant (n=93)", "95% CI Non significant (n=2823)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)

Version	Author	Date
5b4069c	brimittleman	2020-01-15

Not DE:

##In not DE genes
ngenes <- ncol(mc_neg_node)
ab <- fit_node_neg$alpha*fit_node_neg$beta
out <- sapply(1:ngenes, function(g) {
  x <- unlist(mc_neg_node[,g])
  q <- quantile(x, prob = c(.025, .975))
#  q <- quantile(x, prob = c(.005, .995))
  ifelse(0 > q[1] & 0 < q[2], F, T)
})
table(out)

out
FALSE  TRUE 
 4853   108

102/(102+4859) #

[1] 0.02056037

#Visualization for non DE genes:
noDEdat_neg <- data.frame(bf=fit_node_neg$tau, af=fit_node_neg$tau_prime,significance=out)
noDEdat_neg$color <- ifelse(noDEdat_neg$significance==TRUE, "red", "black")
plot(x=noDEdat_neg$bf, y=noDEdat_neg$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 (negative)", pch=16, cex=0.6, xlim=c(-10, 10), ylim=c(-10,10), adj=0.6,col=alpha(noDEdat_neg$color, 0.6))
legend("topleft", legend=c("95% CI Significant (n=102)", "95% CI Non significant (n=4859)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)

Version	Author	Date
5b4069c	brimittleman	2020-01-15

Expression and Splicing

#YG-BM-S3-3622C-Total_S3_R1_001-sort.bam    YG-BM-FIXED-18498H-Total_S5_R1_001-sort.bam YG-BM-S12-18523H-Total_S12_R1_001-sort.baYG-BM-S4-3659C-Total_S4_R1_001-sort.bam    YG-BM-S2-PT91C-Total_S2_R1_001-sort.bam YG-BM-S11-18510H-Total_S11_R1_001-sort.bam  YG-BM-S9-18502H-Total_S9_R1_001-sort.bam    YG-BM-S8-18499H-Total_S8_R1_001-sort.bam    YG-BM-S1-PT30C-Total_S1_R1_001-sort.bam YG-BM-FIXED-4973C-Total_S7_R1_001-sort.bam  YG-BM-S10-18504H-Total_S10_R1_001-sort.bam  YG-BM-S6-18358C-Total_S6_R1_001-sort.bam

Splicing= read.table("../data/NormalizedClusters/MergeCombined_perind.counts.fixed.gz.qqnorm.allchr", col.names = c("chr", "start", "end", "id","NA3622_Splice", "NA18498_Splice", "NA18523_Splice", "NA3659_Splice", "NAPT91_Splice","NA18510_Splice", "NA18502_Splice", "NA18499_Splice","NAPT30_Splice", "NA4973_Splice","NA18504_Splice","NA18358_Splice")) %>% separate(id, into=c("ch", "st", "en","clus"),sep=":") 
Sp_res=read.table("../data/DiffSplice_liftedJunc/MergedRes_cluster_significance.txt",stringsAsFactors = F, header = T, sep="\t") %>% separate(cluster, into=c("chrom", "clus"),sep=":") %>% filter(status=="Success") %>% dplyr::select(p.adjust,clus, genes,loglr)

#%>% dplyr::select(genes,clus, p.adjust)
Splicing_andres=Splicing %>% inner_join(Sp_res, by=c("clus") )



#%>% dplyr::select(genes, p.adjust, NA18498_Splice, NA18499_Splice, NA18502_Splice, NA18504_Splice, NA18510_Splice,NA18523_Splice,NA18358_Splice, NA3622_Splice,NA3659_Splice,NA4973_Splice,NAPT30_Splice,NAPT91_Splice) %>% rename("gene"=genes)

#create a dataframe with expression and apa
#SpandExp=Exp %>% inner_join(Splicing_andres, by="gene")

Current problem: genes to cluster mapping is not trivial. I do not know how to chose the top DS isoform per gene.

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] MASS_7.3-51.1   limma_3.38.2    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.5.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] knitr_1.20       httpuv_1.4.5     broom_0.5.1      Rcpp_1.0.2      
[25] promises_1.0.1   scales_1.0.0     backports_1.1.2  jsonlite_1.6    
[29] fs_1.3.1         hms_0.4.2        digest_0.6.18    stringi_1.2.4   
[33] grid_3.5.1       rprojroot_1.3-2  cli_1.1.0        tools_3.5.1     
[37] magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.26.1     compiler_3.5.1

Mediation analysis

Briana Mittleman

1/13/2020

Expression and APA

First do the positive:

Negative:

Expression and Splicing