Last updated: 2020-01-22
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
Modified: analysis/ExploredAPA.Rmd
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Modified: analysis/speciesSpecific.Rmd
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Rmd | 97ef45e | brimittleman | 2020-01-22 | add mediation DF |
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
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library(limma)
library(MASS)
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select
In this analysis, I will ask if differrecnes in APA or splicing are causal for differences in expression. I did this before the filter, this is post the double filter for expression and 10% cutoff.
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
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_DF
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
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_DF_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt.gz.qqnorm_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_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene)
apaRes= read.table("../data/DiffIso_Nuclear_DF/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_DF/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")
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$batch),
varying_covariate = APA_pos[isnot_de,])
#save as R data object:
save(fit_de_pos, fit_node_pos, is_de, isnot_de, Exp_pos, APA_pos, metadata, gvec, file = "../data/mediation_DF/positive_mediation.rda")
Montecarlo:
I will have to run this seperatly as an Rscript.
sbatch RunPosMCMediationDF.sh
Load results
mc_pos_de <- readRDS(file = "../data/mediation_DF/mc_de_postive.rds")
mc_pos_node <- readRDS(file = "../data/mediation_DF/mc_node_postive.rds")
##In DE genes
ngenes <- ncol(mc_pos_de)
ab <- fit_de_pos$alpha*fit_de_pos$beta
out <- sapply(1:ngenes, function(g) {
x <- unlist(mc_pos_de[,g])
q <- quantile(x, prob = c(.025, .975))
# q <- quantile(x, prob = c(.005, .995))
ifelse(0 > q[1] & 0 < q[2], F, T)
})
table(out)
out
FALSE TRUE
2679 85
85/(85+2679) #
[1] 0.03075253
DEdat <- data.frame(bf=fit_de_pos$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=2679)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)
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
4557 116
116/(116+4557)
[1] 0.02482345
#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$color, 0.6))
legend("topleft", legend=c("95% CI Significant (n=116)", "95% CI Non significant (n=4557)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)
Seperate De and not De
is_de_neg <- which(APA_negandExp$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_DF/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_DF/mc_de_negative.rds")
mc_neg_node <- readRDS(file = "../data/mediation_DF/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
2668 96
96/(96+2668) #
[1] 0.03473227
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$color, 0.6))
legend("topleft", legend=c("95% CI Significant (n=96)", "95% CI Non significant (n=2668)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)
Not DE:
##In not DE genes
ngenes <- ncol(mc_neg_node)
ab <- fit_node_neg$alpha*fit_node_neg$beta
out <- sapply(1:ngenes, function(g) {
x <- unlist(mc_neg_node[,g])
q <- quantile(x, prob = c(.025, .975))
# q <- quantile(x, prob = c(.005, .995))
ifelse(0 > q[1] & 0 < q[2], F, T)
})
table(out)
out
FALSE TRUE
4568 104
104/(104+4568) #
[1] 0.02226027
#Visualization for non DE genes:
noDEdat_neg <- data.frame(bf=fit_node_neg$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=104)", "95% CI Non significant (n=4568)"), col=c("red", "black"), pch=16:16, cex=0.8)
abline(0, 1)
abline(h=0)
abline(v=0)
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