QC metrics on double filtered PAS

Last updated: 2020-06-29

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
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    Modified:   analysis/phastCon.Rmd
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    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/unliftedsites.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	c9af660	brimittleman	2020-06-29	add sup fig write out
html	e24f543	brimittleman	2020-05-08	Build site.
Rmd	a29b1d2	brimittleman	2020-05-08	add QC double filt

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library("gplots")


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library(tidyverse)

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library(ggpubr)

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library("RColorBrewer")
library(cowplot)


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In this analysis file I will run a few QC anaylsis using the double filtered PAS phenotype. I will first test if our ability to detect PAS is assoicteated with gene exprression by comparing the number of PAS per gene to gene expression. I did a similar analysis for the revisions in my apaQTL paper. I am going to use normalized RNA seq from this study and the double filtered PAS.

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)

HumannPAS=Meta %>% select(Human,PAS, gene) %>% filter(Human>0) %>% group_by(gene) %>% summarise(HumanNPAS=n())

ChimpnPAS=Meta %>% select(Chimp,PAS, gene) %>% filter(Chimp>0) %>% group_by(gene) %>% summarise(ChimpNPAS=n())

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID,Gene.name)
NormExp= read.table("../data/DiffExpression/NoramalizedExpression.txt", header=T,stringsAsFactors = F) %>% rename(Gene_stable_ID=genes) %>% inner_join(nameID, by="Gene_stable_ID") %>% rename(gene=Gene.name)

AllInfo=NormExp %>% inner_join(ChimpnPAS, by="gene") %>% inner_join(HumannPAS, by="gene")

chimppas=ggplot(AllInfo,aes(x=ChimpNPAS, y=Chimp)) +geom_point()+ stat_cor() +geom_smooth(method="lm") +labs(x="Chimp number of PAS",y="Normalized Expression Chimp", title="Negative correlation between \nexpression and PAS number")

humanPAS=ggplot(AllInfo,aes(x=HumanNPAS, y=Human)) +geom_point()+ stat_cor() +geom_smooth(method="lm") +labs(x="Human number of PAS",y="Normalized Expression Human", title="Negative correlation between \nexpression and PAS number")


plot_grid(chimppas,humanPAS)

Version	Author	Date
e24f543	brimittleman	2020-05-08

for supplement

fig1_sup5a=ggplot(AllInfo,aes(x=ChimpNPAS, y=Chimp)) +geom_point()+ stat_cor() +geom_smooth(method="lm") +labs(x="Chimpanzee number of PAS",y="Normalized Expression Chimpanzee", title="Negative correlation between \nexpression and PAS number")+ theme_classic()+theme(axis.text.x = element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10), legend.position = "top",plot.margin = unit(c(0,0,0,0), "cm"))

fig1_sup5b=ggplot(AllInfo,aes(x=HumanNPAS, y=Human)) +geom_point()+ stat_cor() +geom_smooth(method="lm") +labs(x="Human number of PAS",y="Normalized Expression Human", title="Negative correlation between \nexpression and PAS number")+ theme_classic()+theme(axis.text.x = element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10), legend.position = "top",plot.margin = unit(c(0,0,0,0), "cm"))

pdf("../output/supplement/Fig1_figSup5.pdf", height=5, width=8)
plot_grid(fig1_sup5a,fig1_sup5b,scale = c(.9,.9), labels = c("A","B"))
dev.off()

png 
  2

Second I will run PCA and look at variation in the filtered phenotypes.

HumanAnno=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndH=colnames(HumanAnno)[9:ncol(HumanAnno)]

HumanUsage=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)

HumanMean=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage)))

HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))


ChimpAnno=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndC=colnames(ChimpAnno)[9:ncol(ChimpAnno)]

ChimpUsage=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC)

ChimpMean=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage)))

ChimpUsage_anno=as.data.frame(cbind(ChimpAnno[,1:8],ChimpUsage ))

allPheno=ChimpUsage_anno %>% full_join(HumanUsage_anno,by=c("chr", "start", "end", "gene","strand","loc", "disc", "PAS")) %>% filter(PAS %in% Meta$PAS) %>% dplyr::select(-chr, -start,-end, -gene,-strand,-loc,-disc, -PAS)


allPheno_nuc= allPheno %>% select(contains("_N"))

allPheno_nuc_matrix=as.matrix(allPheno_nuc)

pca_Pheno=prcomp(t(allPheno_nuc_matrix), scale=F)
scores = pca_Pheno$x

#PCA function (original code from Julien Roux)
#Load in the plot_scores function
plot_scores <- function(pca, scores, n, m, cols, points=F, pchs =20, legend=T){
  xmin <- min(scores[,n]) - (max(scores[,n]) - min(scores[,n]))*0.05
  if (legend == T){ ## let some room (35%) for a legend                                                                                                                                                 
    xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.50
  }
  else {
    xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.05
  }
  ymin <- min(scores[,m]) - (max(scores[,m]) - min(scores[,m]))*0.05
  ymax <- max(scores[,m]) + (max(scores[,m]) - min(scores[,m]))*0.05
  plot(scores[,n], scores[,m], xlab=paste("PC", n, ": ", round(summary(pca)$importance[2,n],3)*100, "% variance explained", sep=""), ylab=paste("PC", m, ": ", round(summary(pca)$importance[2,m],3)*100, "% variance explained", sep=""), xlim=c(xmin, xmax), ylim=c(ymin, ymax), type="n")
  if (points == F){
    text(scores[,n],scores[,m], rownames(scores), col=cols, cex=1)
  }
  else {
    points(scores[,n],scores[,m], col=cols, pch=pchs, cex=1.3)
  }
}

colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)

pal <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"), brewer.pal(12, "Set3"))

metaData=as.data.frame(cbind(ind=rownames(pca_Pheno$x), species=c(rep("Chimp", 6), rep("Human",5))))

metaData$species=as.factor(metaData$species)
for (n in 1:1){
  col.v <- pal[as.integer(metaData$species)]
  plot_scores(pca_Pheno, scores, n, n+1, col.v)
}

Version	Author	Date
e24f543	brimittleman	2020-05-08

for (n in 2:2){
  col.v <- pal[as.integer(metaData$species)]
  plot_scores(pca_Pheno, scores, n, n+1, col.v)
}

Version	Author	Date
e24f543	brimittleman	2020-05-08

eigs <- pca_Pheno$sdev^2
proportion = eigs/sum(eigs)

plot(proportion)

metaData= metaData %>% mutate(specNum=ifelse(species=="Chimp", 0, 1))

pc1=as.vector(as.numeric(pca_Pheno$x[,1]))
cor.test(pc1, metaData$specNum)


    Pearson's product-moment correlation

data:  pc1 and metaData$specNum
t = 17.492, df = 9, p-value = 2.95e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9436760 0.9963815
sample estimates:
      cor 
0.9856096

metaData=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F) %>% filter(Fraction=="Nuclear")

x.pca <- pca_Pheno

tech_factors <- metaData
tech_factors_sum <- tech_factors[,c(2:15)] %>% dplyr::select(Species,Extraction_date,Collection_person,AverageAlive,RIN,Concentration)

p_comps <- 1:6
pc_cov_cor <- matrix(nrow = ncol(tech_factors_sum), ncol = length(p_comps),
                     dimnames = list(colnames(tech_factors_sum), colnames(x.pca$x)[p_comps]))
for (pc in p_comps) {
  for (covariate in 1:ncol(tech_factors_sum)) {
    lm_result <- lm(x.pca$x[, pc] ~ tech_factors_sum[, covariate])
    r2 <- summary(lm_result)$r.squared
    pc_cov_cor[covariate, pc] <- r2
  }
}

pc_cov_pval <- matrix(nrow = ncol(tech_factors_sum), ncol = length(p_comps),
                      dimnames = list(colnames(tech_factors_sum), colnames(x.pca$x)[p_comps]))

for (pc in p_comps) {
  for (covariate_2 in 1:ncol(tech_factors_sum)) {
    lm_result_2 <- lm(x.pca$x[, pc] ~ tech_factors_sum[, covariate_2])
    pval <- anova(lm_result_2)$'Pr(>F)'[1]
    pc_cov_pval[covariate_2, pc] <- pval
  }
}

PCs <- c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6")
Tech_fac <- colnames(tech_factors_sum)
#Tech_fac <- c("Species",   "Extraction", "Person",  "Alive" , "RIN", "RNA_Concentration")

heatmap.2(as.matrix(pc_cov_cor[Tech_fac,PCs]),col=brewer.pal(4, "Blues"), trace="none",
          Rowv=FALSE, Colv=FALSE, key=T, main="Cor. PCs & tech factors", dendrogram="none",
          key.title=NA, cexRow=0.9, cexCol=0.9)

metaData %>% select(Line, Collection_person,Extraction_date)

    Line Collection_person Extraction_date
1  18498               Bri         6.29.18
2  18502               Bri          7.2.18
3  18504               Seb         6.29.18
4  18510               Bri          7.2.18
5  18523               Seb          7.2.18
6  18358               Bri          7.2.18
7   3622               Seb          7.2.18
8   3659               Seb         6.29.18
9   4973               Seb         6.29.18
10  pt30               Seb         6.29.18
11  pt91               Bri          7.2.18

supplemental figures:

pdf("../output/supplement/Fig1_figSup4A.pdf", height=4, width=4)
for (n in 1:1){
  col.v <- pal[as.integer(metaData$species)]
  plot_scores(pca_Pheno, scores, n, n+1, col.v)
}
dev.off()

png 
  2

pdf("../output/supplement/Fig1_figSup4B.pdf", height=6, width=6)
heatmap.2(as.matrix(pc_cov_cor[Tech_fac,PCs]),col=brewer.pal(4, "Blues"), trace="none",
          Rowv=FALSE, Colv=FALSE, key=T, main="Cor. PCs & tech factors", dendrogram="none",
          key.title=NA, cexRow=0.9, cexCol=0.9)
dev.off()

png 
  2

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] cowplot_0.9.4      RColorBrewer_1.1-2 ggpubr_0.2        
 [4] magrittr_1.5       forcats_0.3.0      stringr_1.3.1     
 [7] dplyr_0.8.0.1      purrr_0.3.2        readr_1.3.1       
[10] tidyr_0.8.3        tibble_2.1.1       ggplot2_3.1.1     
[13] tidyverse_1.2.1    gplots_3.0.1       workflowr_1.6.0   

loaded via a namespace (and not attached):
 [1] gtools_3.8.1       tidyselect_0.2.5   haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       withr_2.1.2       
[13] glue_1.3.0         modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        caTools_1.17.1.1  
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.4.6      
[28] KernSmooth_2.23-15 promises_1.0.1     backports_1.1.2   
[31] scales_1.0.0       gdata_2.18.0       jsonlite_1.6      
[34] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[37] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[40] cli_1.1.0          tools_3.5.1        bitops_1.0-6      
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[49] rstudioapi_0.10    assertthat_0.2.0   rmarkdown_1.10    
[52] httr_1.3.1         R6_2.3.0           nlme_3.1-137      
[55] git2r_0.26.1       compiler_3.5.1

QC metrics on double filtered PAS

Briana Mittleman

5/8/2020