Last updated: 2020-04-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.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

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Rmd 9676d72 brimittleman 2020-04-06 updated anno
html f02217e brimittleman 2020-01-28 Build site.
Rmd 86aabf2 brimittleman 2020-01-28 add expression overlap with APA exploration

In this analysis I will do as much as I can do compare APA and expression. I will look at the genes we have data for both.

library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library("gplots")

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library("scales")

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library(ggpubr)
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    extract

I will start with a full correlation matrix using the counts for the top used PAS (mean human and chimp).

TopUsedPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header=T,stringsAsFactors = F) %>% mutate(MeanUsage=(Chimp+Human)/2) %>% group_by(gene) %>% arrange(desc(MeanUsage)) %>% slice(1) %>% ungroup() %>% dplyr::select(PAS, chr, start, end) %>% rename("Chr"= chr,"Start"= start, "End"= end)

Filter these in the counts file:

#Both:human74:chr1:944201:944401:+:NOC2L_utr3


HumanCounts=read.table("../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc",stringsAsFactors = F,header = T) %>% inner_join(TopUsedPAS,by=c("Chr", "Start", "End")) %>% separate(Geneid, into=c("d", "pas", "chr","start","end","strand", "geneID"), sep=":") %>% separate(geneID,into=c("gene","loc"),sep="_") %>% dplyr::select(PAS, gene, contains("_N"))

ChimpCounts=read.table("../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc",stringsAsFactors = F,header = T) %>% separate(Geneid, into=c("d", "PAS", "chr","start","end","strand", "geneID"), sep=":") %>% separate(geneID,into=c("gene","loc"),sep="_") %>% inner_join(TopUsedPAS,by=c("PAS")) %>% dplyr::select(PAS, gene, contains("_N"))
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [48528,
48529, 48530, 92432].
AllCounts=HumanCounts %>% inner_join(ChimpCounts, by=c("gene", "PAS")) %>% dplyr::select(-PAS)


colnames(AllCounts)=paste("APA" ,colnames(AllCounts), sep="_")


AllCounts=AllCounts %>% rename("gene"= APA_gene)

Expression counts:

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
HumanRNA=read.table("../Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)

ChimpRNA=read.table("../Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)


RNACounts=HumanRNA %>% inner_join(ChimpRNA,by="Geneid") %>% rename("Gene_stable_ID"=Geneid) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"= Gene.name) %>% group_by(gene) %>% slice(1) %>% ungroup()


colnames(RNACounts)=paste("RNA" ,colnames(RNACounts), sep="_")

RNACounts=RNACounts %>% rename("gene"= RNA_gene)

Add all together:

BothPhenoCount=  RNACounts %>% inner_join(AllCounts, by="gene")%>% column_to_rownames(var="gene")

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

Species=c(rep("Human",6), rep("Chimp",6),rep("Human",6),rep("Chimp",6))
Meta=as.data.frame(cbind(Names,Species )) %>% mutate(Pheno=ifelse(grepl("RNA",Names),"RNA", "APA" )) 

cors <- cor(BothPhenoCount, method="spearman", use="pairwise.complete.obs")
colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)
heatmap.2( cors, scale="none", col = colors, margins = c(12, 12), trace='none',ColSideColors=pal[as.integer(as.factor(Meta$Species))], RowSideColors=pal[as.integer(as.factor(Meta$Pheno))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))

Version Author Date
f02217e brimittleman 2020-01-28

Pvalues:

DE= 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_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name)

APA=read.table(c("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt"),header = T,stringsAsFactors = F) 
GenesWithapa=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)

DEandAPA= DE %>% inner_join(APA, by="gene") %>% mutate(HasDPAU=ifelse(gene %in% GenesWithapa$gene, "Yes","No"))

Plot:

ggplot(DEandAPA,aes(x=HasDPAU, y=abs(logFC)))+ geom_boxplot() + stat_compare_means() + labs(x="Gene has significant dPAS", y="Absolute value of DE log effect size", title="DE effect size by dAPA gene")

Version Author Date
f02217e brimittleman 2020-01-28 
ggplot(DEandAPA,aes(x=HasDPAU, y=adj.P.Val))+ geom_boxplot() + stat_compare_means() + labs(x="Gene has significant dPAS", y="Adjusted DE Pvalue", title="DE pvalue dAPA gene")

Version Author Date
f02217e brimittleman 2020-01-28

Correlation between effect sizes:

ggplot(DEandAPA,aes(x=logFC, y=logef,col=HasDPAU)) + geom_point() + labs(x="DE log Effect size", y="APA effect size",title="Relationship between effect sizes")  + geom_density2d(col="black")

Version Author Date
f02217e brimittleman 2020-01-28 
ggplot(DEandAPA,aes(x=abs(logFC), y=abs(logef))) + geom_point(aes(col=HasDPAU) )+ labs(x="abs(DE log Effect size)", y="abs(APA effect size)",title="Relationship between absolute values of effect sizes") + geom_smooth(method="lm") +annotate("text", label="Beta= 0.158339 , R2= 0.01",y=40,x=5)

Version Author Date
f02217e brimittleman 2020-01-28 
summary(lm(abs(DEandAPA$logef)~abs(DEandAPA$logFC)))

Call:
lm(formula = abs(DEandAPA$logef) ~ abs(DEandAPA$logFC))

Residuals:
   Min     1Q Median     3Q    Max 
-1.413 -0.425 -0.201  0.149 55.658 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.544923   0.007152   76.19   <2e-16 ***
abs(DEandAPA$logFC) 0.164967   0.008537   19.32   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1 on 33757 degrees of freedom
Multiple R-squared:  0.01094,   Adjusted R-squared:  0.01091 
F-statistic: 373.4 on 1 and 33757 DF,  p-value: < 2.2e-16

Subset the PAS in the top used:

TopUsedPASlow= TopUsedPAS %>% rename("chr"=Chr, "start"=Start, "end"=End)
DEandAPA_filt= DEandAPA %>% semi_join(TopUsedPASlow,by=c("chr","start","end"))

Plot:

ggplot(DEandAPA_filt,aes(x=HasDPAU, y=abs(logFC)))+ geom_boxplot() + stat_compare_means() + labs(x="Gene has significant dPAS", y="Absolute value of DE log effect size", title="DE effect size by dAPA gene\n most used PAS")

Version Author Date
f02217e brimittleman 2020-01-28 
ggplot(DEandAPA_filt,aes(x=HasDPAU, y=adj.P.Val))+ geom_boxplot() + stat_compare_means() + labs(x="Gene has significant dPAS", y="Adjusted DE Pvalue", title="DE pvalue dAPA gene \n most used PAS")

Version Author Date
f02217e brimittleman 2020-01-28

Correlation between effect sizes:

ggplot(DEandAPA_filt,aes(x=logFC, y=logef,col=HasDPAU)) + geom_point() + labs(x="DE log Effect size", y="APA effect size", title="Relationship between effect sizes \n most used PAS")

Version Author Date
f02217e brimittleman 2020-01-28 
ggplot(DEandAPA_filt,aes(x=abs(logFC), y=abs(logef))) + geom_point(aes(col=HasDPAU) )+ labs(x="abs(DE log Effect size)", y="abs(APA effect size)",title="Relationship between absolute values of effect sizes \n most used PAS") + geom_smooth(method="lm") +annotate("text", label="Beta= 0.14957 , R2= 0.01",y=30,x=5)

Version Author Date
f02217e brimittleman 2020-01-28 
summary(lm(abs(DEandAPA_filt$logef)~abs(DEandAPA_filt$logFC)))

Call:
lm(formula = abs(DEandAPA_filt$logef) ~ abs(DEandAPA_filt$logFC))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3368 -0.3804 -0.1669  0.1408 28.1151 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)               0.49645    0.01307   37.99   <2e-16 ***
abs(DEandAPA_filt$logFC)  0.15997    0.01568   10.20   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8612 on 7402 degrees of freedom
Multiple R-squared:  0.01387,   Adjusted R-squared:  0.01374 
F-statistic: 104.1 on 1 and 7402 DF,  p-value: < 2.2e-16

Number of significant PAS and de

DEandAPA_sig= DEandAPA %>% filter(SigPAU2 == "Yes") %>% group_by(gene) %>% summarise(NsigPAS=n())

DEandAPA_sigWithDE= DE %>% inner_join(DEandAPA_sig,by="gene" )


summary(lm(data=DEandAPA_sigWithDE, abs(logFC)~NsigPAS))

Call:
lm(formula = abs(logFC) ~ NsigPAS, data = DEandAPA_sigWithDE)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0640 -0.4540 -0.2528  0.1870  6.3543 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.42475    0.04497   9.444  < 2e-16 ***
NsigPAS      0.14271    0.02904   4.915 9.66e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7568 on 1875 degrees of freedom
Multiple R-squared:  0.01272,   Adjusted R-squared:  0.01219 
F-statistic: 24.16 on 1 and 1875 DF,  p-value: 9.659e-07
ggplot(DEandAPA_sigWithDE, aes(x=NsigPAS, y=abs(logFC))) +geom_point() + geom_smooth(method = "lm") + annotate("text",label="beta=.15, r2=0.014, pvalue-2.93e-7",x=4, y=6) + labs(x="Number of differentially used PAS 20%",title="Relationship between DE effect size and dAPA PAS")

Version Author Date
f02217e brimittleman 2020-01-28 
cor( DEandAPA_sigWithDE$NsigPAS, DEandAPA_sigWithDE$logFC, method = "spearman")
[1] -0.02790666
cor(DEandAPA_sigWithDE$B, DEandAPA_sigWithDE$NsigPAS, method = "spearman")
[1] 0.0811877

Maybe use the variance in effect sizes for dAPA:

APA_var= APA %>% group_by(gene) %>% summarise(VarLogef=var(logef), VarDeltaPAU=var(deltaPAU))

APA_varDE= APA_var %>% inner_join(DE, by="gene")
cor(APA_varDE$VarDeltaPAU, APA_varDE$B, method = "spearman")
[1] 0.03424864
cor(APA_varDE$VarDeltaPAU, APA_varDE$logFC, method = "spearman")
[1] -0.01472494
cor(APA_varDE$VarLogef, APA_varDE$B, method = "spearman")
[1] 0.0751887
cor(APA_varDE$VarLogef, APA_varDE$logFC, method = "spearman")
[1] 0.01055614

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] ggpubr_0.2         magrittr_1.5       RColorBrewer_1.1-2
 [4] scales_1.0.0       gplots_3.0.1       forcats_0.3.0     
 [7] stringr_1.3.1      dplyr_0.8.0.1      purrr_0.3.2       
[10] readr_1.3.1        tidyr_0.8.3        tibble_2.1.1      
[13] ggplot2_3.1.1      tidyverse_1.2.1   

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       glue_1.3.0        
[13] withr_2.1.2        modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] workflowr_1.6.0    cellranger_1.1.0   rvest_0.3.2       
[22] caTools_1.17.1.1   evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       broom_0.5.1       
[28] Rcpp_1.0.2         KernSmooth_2.23-15 promises_1.0.1    
[31] backports_1.1.2    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] bitops_1.0-6       cli_1.1.0          tools_3.5.1       
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    MASS_7.3-51.1      xml2_1.2.0        
[49] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[52] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[55] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1