Last updated: 2020-01-15

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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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
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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 
 2815    93 
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 
 4859   102 
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$color <- ifelse(noDEdat$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