Last updated: 2020-01-13

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd 1121102 brimittleman 2020-01-13 add dpau and de overlap with dom intronic 
library(tidyverse)
── Attaching packages ────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started

In this analysis I want to further explore the PAS that are dominant in human introns. For these PAS the dominant intron in the chimps are in the 3’ UTR. I want to look to see if these are differentially used and if these are in differentially expressed genes.

HumanIntronChimpUTR=read.table("../data/DominantPAS/Nuclear_HumanIntronicChimpUTR.txt",stringsAsFactors = F, header = T ) %>% rename("PAS"=HumanPAS) 
SameDom= read.table( "../data/DominantPAS/Nuclear_SameDom.txt",  header = T, stringsAsFactors = F) %>% rename("PAS"=HumanPAS) 
HumanUTRChimpIntron= read.table( "../data/DominantPAS/Nuclear_HumanUTRChimpIntronic.txt",  header = T, stringsAsFactors = F) %>% rename("PAS"=HumanPAS)

Diff APA

Differentially used PAS:

DiffUsage=read.table("../data/DiffIso_Nuclear/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)

This does not have the PAS number. I need to add them by joining by location.

PASMeta=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene)
DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))

Number overlapping with different dom.

HumanIntronChimpUTR_diffUsed=HumanIntronChimpUTR %>% inner_join(DiffUsagePAS, by="PAS")
nrow(HumanIntronChimpUTR_diffUsed)
[1] 227
nrow(HumanIntronChimpUTR_diffUsed)/nrow(HumanIntronChimpUTR)
[1] 0.2451404

Number overlapping in same:

SameDom_diffUsed=SameDom %>%inner_join(DiffUsagePAS, by="PAS")
nrow(SameDom_diffUsed)
[1] 864
nrow(SameDom_diffUsed)/nrow(SameDom)
[1] 0.06274054
prop.test(x=c(nrow(HumanIntronChimpUTR_diffUsed),nrow(SameDom_diffUsed)), n=c(nrow(HumanIntronChimpUTR),nrow(SameDom)),alternative = "greater")

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(HumanIntronChimpUTR_diffUsed), nrow(SameDom_diffUsed)) out of c(nrow(HumanIntronChimpUTR), nrow(SameDom))
X-squared = 417.4, df = 1, p-value < 2.2e-16
alternative hypothesis: greater
95 percent confidence interval:
 0.1583244 1.0000000
sample estimates:
    prop 1     prop 2 
0.24514039 0.06274054

Better background is random PAS, not the same used.

Diff Expression

In my first paper I saw a correlation between effect size for intronic PAS and eQTL effect size. I want to see if there is a correlation between the dAPA effect size and DE effect size for the intronic dominant PAS.

I want to include the dPAU for all of the PAS.

effectsize=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=":")
effectsize$start=as.numeric(effectsize$start)
effectsize$end=as.numeric(effectsize$end)

effectsize_PAS=effectsize%>% inner_join(PASMeta, by=c("start", "end","chr", "gene")) %>% dplyr::select(PAS, deltaPAU)
HumanIntronChimpUTR_dpau= HumanIntronChimpUTR %>% inner_join(effectsize_PAS,by="PAS")
SameDom_dpau= SameDom %>% inner_join(effectsize_PAS,by="PAS")

HumanUTRChimpIntron_dpau= HumanUTRChimpIntron %>% inner_join(effectsize_PAS,by="PAS")

Pull in the expression values.

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)


DEgenes=read.table("../data/DiffExpression/DEtested_allres.txt", header =T, col.names = c("Gene_stable_ID", "logFC", "AveExpr","t", "P.Value", "adj.Pval", "B"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% rename("gene"=Gene.name)

Join:

HumanIntronChimpUTR_dpau_DE= HumanIntronChimpUTR_dpau %>% inner_join(DEgenes, by="gene")
HumanIntronChimpUTR_dpau_DE$deltaPAU = as.numeric(HumanIntronChimpUTR_dpau_DE$deltaPAU)

ggplot(HumanIntronChimpUTR_dpau_DE,aes(x=deltaPAU, y=logFC)) +geom_point()+  geom_smooth(method = "lm")

summary(lm(HumanIntronChimpUTR_dpau_DE$logFC~HumanIntronChimpUTR_dpau_DE$deltaPAU))

Call:
lm(formula = HumanIntronChimpUTR_dpau_DE$logFC ~ HumanIntronChimpUTR_dpau_DE$deltaPAU)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1362 -0.3502  0.0593  0.4531  3.5541 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)
(Intercept)                           0.14624    0.06555   2.231  0.02606
HumanIntronChimpUTR_dpau_DE$deltaPAU -1.10141    0.30561  -3.604  0.00034
                                        
(Intercept)                          *  
HumanIntronChimpUTR_dpau_DE$deltaPAU ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9048 on 586 degrees of freedom
Multiple R-squared:  0.02168,   Adjusted R-squared:  0.02001 
F-statistic: 12.99 on 1 and 586 DF,  p-value: 0.0003401
SameDom_dpau_DE= SameDom_dpau %>% inner_join(DEgenes, by="gene")
SameDom_dpau_DE$deltaPAU = as.numeric(SameDom_dpau_DE$deltaPAU)

ggplot(SameDom_dpau_DE,aes(x=deltaPAU, y=logFC)) +geom_point()+  geom_smooth(method = "lm")

summary(lm(SameDom_dpau_DE$logFC~SameDom_dpau_DE$deltaPAU))

Call:
lm(formula = SameDom_dpau_DE$logFC ~ SameDom_dpau_DE$deltaPAU)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.0774 -0.3396 -0.0298  0.3282  7.0806 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)               0.02922    0.01168   2.502   0.0124 *
SameDom_dpau_DE$deltaPAU -0.03098    0.08570  -0.361   0.7178  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8288 on 5389 degrees of freedom
Multiple R-squared:  2.424e-05, Adjusted R-squared:  -0.0001613 
F-statistic: 0.1306 on 1 and 5389 DF,  p-value: 0.7178
HumanUTRChimpIntron_dpau_de= HumanUTRChimpIntron_dpau %>% inner_join(DEgenes, by="gene")
HumanUTRChimpIntron_dpau_de$deltaPAU = as.numeric(HumanUTRChimpIntron_dpau_de$deltaPAU)

ggplot(HumanUTRChimpIntron_dpau_de,aes(x=deltaPAU, y=logFC)) +geom_point()+  geom_smooth(method = "lm")

summary(lm(HumanUTRChimpIntron_dpau_de$logFC~HumanUTRChimpIntron_dpau_de$deltaPAU))

Call:
lm(formula = HumanUTRChimpIntron_dpau_de$logFC ~ HumanUTRChimpIntron_dpau_de$deltaPAU)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3936 -0.6196 -0.1195  0.4382  3.7021 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)
(Intercept)                            0.2858     0.1531   1.867   0.0653
HumanUTRChimpIntron_dpau_de$deltaPAU   0.1503     0.6346   0.237   0.8134
                                      
(Intercept)                          .
HumanUTRChimpIntron_dpau_de$deltaPAU  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.053 on 87 degrees of freedom
Multiple R-squared:  0.0006441, Adjusted R-squared:  -0.01084 
F-statistic: 0.05607 on 1 and 87 DF,  p-value: 0.8134

Looks like this is the opposite direction. Here we have greater delta pau correlated with greater gene expression for human when intronic…


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] workflowr_1.5.0 forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4