Differential Used Intronic

Last updated: 2020-02-23

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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library(workflowr)

This is workflowr version 1.6.0
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()

For this analysis I will look at the differentially used PAS in introns and ask if I can used information from DE and dribosome and dprotien to better understand these. I subset intornic because I believe the intronic and utr mechanisms are different.

Meta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) %>% filter(loc=="intron")
DiffIsoSig= DiffIso %>% filter(SigPAU2=="Yes")

742 of the 11228 intronic PAS are significant. I can compare the effect sizes with these genes in the DE.

Compare with expression

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% select(Gene_stable_ID, Gene.name)

DE=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" ,  "P.Value" ,  "adj.P.Val", "B"  )) %>% inner_join(nameID,by="Gene_stable_ID") %>% rename('gene'=Gene.name) %>% select(-Gene_stable_ID)

First do all of the genes:

DeandAPA= DiffIso %>% inner_join(DE, by="gene")

This pas I will include each PAS

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

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.69365 -0.06405 -0.00194  0.05663  0.76837 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.041100   0.001120  36.680  < 2e-16 ***
DeandAPA$logFC -0.009831   0.001272  -7.727 1.22e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1042 on 8716 degrees of freedom
Multiple R-squared:  0.006804,  Adjusted R-squared:  0.00669 
F-statistic: 59.71 on 1 and 8716 DF,  p-value: 1.22e-14

cor.test(DeandAPA$deltaPAU,DeandAPA$logFC)


    Pearson's product-moment correlation

data:  DeandAPA$deltaPAU and DeandAPA$logFC
t = -7.7273, df = 8716, p-value = 1.22e-14
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.10330034 -0.06160219
sample estimates:
        cor 
-0.08248736

Just the genes with significant differences in PAS

DeandAPA_sigAPA= DeandAPA %>% filter(SigPAU2=="Yes")

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

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.82159  0.01339  0.05347  0.11130  0.60587 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)            0.183773   0.009599  19.144  < 2e-16 ***
DeandAPA_sigAPA$logFC -0.028174   0.009024  -3.122  0.00188 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2311 on 578 degrees of freedom
Multiple R-squared:  0.01659,   Adjusted R-squared:  0.01488 
F-statistic: 9.748 on 1 and 578 DF,  p-value: 0.001885

cor.test(DeandAPA_sigAPA$deltaPAU,DeandAPA_sigAPA$logFC)


    Pearson's product-moment correlation

data:  DeandAPA_sigAPA$deltaPAU and DeandAPA_sigAPA$logFC
t = -3.1222, df = 578, p-value = 0.001885
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.20801803 -0.04787348
sample estimates:
       cor 
-0.1287853

Sig both:

DeandAPA_sigAPAandE= DeandAPA %>% filter(SigPAU2=="Yes",  adj.P.Val<.05)

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

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.81354 -0.00442  0.06000  0.11909  0.60013 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.17922    0.01591  11.264  < 2e-16 ***
DeandAPA_sigAPAandE$logFC -0.03770    0.01048  -3.598 0.000392 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2444 on 234 degrees of freedom
Multiple R-squared:  0.05241,   Adjusted R-squared:  0.04836 
F-statistic: 12.94 on 1 and 234 DF,  p-value: 0.0003918

cor.test(DeandAPA_sigAPAandE$deltaPAU,DeandAPA_sigAPAandE$logFC)


    Pearson's product-moment correlation

data:  DeandAPA_sigAPAandE$deltaPAU and DeandAPA_sigAPAandE$logFC
t = -3.5977, df = 234, p-value = 0.0003918
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3465098 -0.1042883
sample estimates:
       cor 
-0.2289398

Is there a correlation between pValues:

cor.test(DeandAPA$p.adjust, DeandAPA$adj.P.Val)


    Pearson's product-moment correlation

data:  DeandAPA$p.adjust and DeandAPA$adj.P.Val
t = 2.6159, df = 8716, p-value = 0.008914
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.007021126 0.048971892
sample estimates:
       cor 
0.02800884

cor.test(DeandAPA_sigAPA$p.adjust, DeandAPA_sigAPA$adj.P.Val)


    Pearson's product-moment correlation

data:  DeandAPA_sigAPA$p.adjust and DeandAPA_sigAPA$adj.P.Val
t = -0.49878, df = 578, p-value = 0.6181
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.10198363  0.06077433
sample estimates:
        cor 
-0.02074207

cor.test(DeandAPA_sigAPAandE$p.adjust, DeandAPA_sigAPAandE$adj.P.Val)


    Pearson's product-moment correlation

data:  DeandAPA_sigAPAandE$p.adjust and DeandAPA_sigAPAandE$adj.P.Val
t = 0.13628, df = 234, p-value = 0.8917
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1189273  0.1364537
sample estimates:
        cor 
0.008908498

No correlation here. This may be due to the multiple PAS.

Choose most Sig PAS

To break ties I will use the top average usage

DeandAPA_topPAS= DeandAPA %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()

Plot the correlation in effect size

ggplot(DeandAPA_topPAS,aes(y=deltaPAU, x=logFC)) + geom_point() + geom_smooth(method="lm") + geom_density2d(col="red")

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.70414 -0.06984 -0.00066  0.06638  0.74199 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)            0.061581   0.001945  31.659  < 2e-16 ***
DeandAPA_topPAS$logFC -0.015287   0.002280  -6.704 2.32e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1229 on 4023 degrees of freedom
Multiple R-squared:  0.01105,   Adjusted R-squared:  0.0108 
F-statistic: 44.94 on 1 and 4023 DF,  p-value: 2.316e-11

cor.test(DeandAPA_topPAS$deltaPAU,DeandAPA_topPAS$logFC)


    Pearson's product-moment correlation

data:  DeandAPA_topPAS$deltaPAU and DeandAPA_topPAS$logFC
t = -6.7036, df = 4023, p-value = 2.316e-11
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1355590 -0.0744509
sample estimates:
       cor 
-0.1051042

DeandAPA_topPASsigAPA= DeandAPA_topPAS %>% filter(SigPAU2=="Yes")

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

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.83216  0.00555  0.04632  0.10897  0.59333 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                  0.194844   0.010717   18.18  < 2e-16 ***
DeandAPA_topPASsigAPA$logFC -0.029540   0.009846   -3.00  0.00284 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2325 on 469 degrees of freedom
Multiple R-squared:  0.01883,   Adjusted R-squared:  0.01674 
F-statistic:     9 on 1 and 469 DF,  p-value: 0.002843

cor.test(DeandAPA_topPASsigAPA$deltaPAU,DeandAPA_topPASsigAPA$logFC)


    Pearson's product-moment correlation

data:  DeandAPA_topPASsigAPA$deltaPAU and DeandAPA_topPASsigAPA$logFC
t = -3.0001, df = 469, p-value = 0.002843
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2247846 -0.0474553
sample estimates:
       cor 
-0.1372192

Sig both:

DeandAPA_topPASsigAPAandE= DeandAPA_topPASsigAPA %>% filter(SigPAU2=="Yes",  adj.P.Val<.05)

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

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.82506 -0.01782  0.05480  0.11924  0.58620 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      0.19136    0.01810  10.570  < 2e-16 ***
DeandAPA_topPASsigAPAandE$logFC -0.03936    0.01154  -3.412 0.000789 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2508 on 190 degrees of freedom
Multiple R-squared:  0.05772,   Adjusted R-squared:  0.05276 
F-statistic: 11.64 on 1 and 190 DF,  p-value: 0.0007892

cor.test(DeandAPA_topPASsigAPAandE$deltaPAU,DeandAPA_topPASsigAPAandE$logFC)


    Pearson's product-moment correlation

data:  DeandAPA_topPASsigAPAandE$deltaPAU and DeandAPA_topPASsigAPAandE$logFC
t = -3.4115, df = 190, p-value = 0.0007892
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3692937 -0.1021150
sample estimates:
       cor 
-0.2402492

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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.6.0

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] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

Differential Used Intronic

Briana Mittleman

2/23/2020

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