Dominant PAS after Normalization

Last updated: 2020-04-01

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Knit directory: Comparative_APA/analysis/

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File	Version	Author	Date	Message
Rmd	7447f2d	brimittleman	2020-04-01	add norm dom and add filter comp plots

I am worried that the different dominance structure is effected by mena and variance differences. To get around this. I want to use the normalized values for each PAS from the leafcutter model. I will use the nuclear differences.

library(reshape2)
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()

Load PAS

PAS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% mutate(chrom=paste(chr,start,end,gene, sep=":"))

effectsize=read.table("../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('chrom',  'logef' ,'HumanNorm', 'ChimpNorm','deltaPAU')) %>% filter(chrom != "intron") %>% inner_join(PAS, by="chrom")

effectsize$HumanNorm=as.numeric(effectsize$HumanNorm)


effectsize$ChimpNorm=as.numeric(effectsize$ChimpNorm)

Look at the correlation between raw usage and normalized

ggplot(effectsize,aes(x=HumanNorm, y=Human)) + geom_point(alpha=.5) + geom_abline(aes(slope=1, intercept=0),col="red")

summary(lm(effectsize$HumanNorm~effectsize$Human))


Call:
lm(formula = effectsize$HumanNorm ~ effectsize$Human)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.94439 -0.06041 -0.01453  0.04418  0.93021 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      0.055551   0.000751   73.97   <2e-16 ***
effectsize$Human 0.974386   0.003173  307.08   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1129 on 40774 degrees of freedom
Multiple R-squared:  0.6981,    Adjusted R-squared:  0.6981 
F-statistic: 9.43e+04 on 1 and 40774 DF,  p-value: < 2.2e-16

ggplot(effectsize,aes(x=ChimpNorm, y=Chimp)) + geom_point(alpha=.5) + geom_abline(aes(slope=1, intercept=0),col="red")

summary(lm(effectsize$ChimpNorm~effectsize$Chimp))


Call:
lm(formula = effectsize$ChimpNorm ~ effectsize$Chimp)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.83040 -0.07120 -0.01598  0.05501  0.91349 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      0.0791791  0.0007917     100   <2e-16 ***
effectsize$Chimp 0.7999860  0.0031747     252   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.121 on 40774 degrees of freedom
Multiple R-squared:  0.609, Adjusted R-squared:  0.609 
F-statistic: 6.35e+04 on 1 and 40774 DF,  p-value: < 2.2e-16

Looks like more change in the chimps.

Pick dominant PAS

Chimp_Dom= effectsize %>%
  group_by(gene) %>%
  arrange(desc(ChimpNorm)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,ChimpNorm) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)

Human_Dom= effectsize %>%
  group_by(gene) %>%
  arrange(desc(HumanNorm)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,HumanNorm) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS)

BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")

I am looking at 8544 genes in this set.

SameDom= BothDom %>% filter(ChimpPAS==HumanPAS)
nrow(SameDom)

[1] 5846

5848 have the same dominant PAS

SameDom_g= SameDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(SameDom_g, aes(x=Location, by=Species, fill=Species))+ geom_bar(stat="count",position = "Dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species\normalized usage")+ scale_fill_brewer(palette = "Dark2")

DiffDom=BothDom %>% filter(ChimpPAS!=HumanPAS)
nrow(DiffDom)

[1] 2698

This gives more genes with different dominant PAS

DiffDom_g= DiffDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


ggplot(DiffDom_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS using normalized usage\n (n=2698)") + scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

This flips the distribution. It over corrects.

Dominant from original usage in filtered set

Chimp_Dom_Nonnorm= effectsize %>%
  group_by(gene) %>%
  arrange(desc(Chimp)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,Chimp) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)

Human_Dom_Nonnorm= effectsize %>%
  group_by(gene) %>%
  arrange(desc(Human)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,Human) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS)

BothDom_Nonnorm= Chimp_Dom_Nonnorm %>% inner_join(Human_Dom_Nonnorm,by="gene")

SameDom_Nonnorm= BothDom_Nonnorm %>% filter(ChimpPAS==HumanPAS)
nrow(SameDom_Nonnorm)

[1] 6477

6477 have the same dominant PAS

SameDom_Nonnorm_g= SameDom_Nonnorm %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(SameDom_Nonnorm_g, aes(x=Location, by=Species, fill=Species))+ geom_bar(stat="count",position = "Dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species\non normalized usage")+ scale_fill_brewer(palette = "Dark2")

DiffDom_Nonnorm=BothDom_Nonnorm %>% filter(ChimpPAS!=HumanPAS)
nrow(DiffDom_Nonnorm)

[1] 2067

2067 PAS

DiffDom_Nonnorm_g= DiffDom_Nonnorm %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


ggplot(DiffDom_Nonnorm_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS using non normalized usage (n=2067)") + scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

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 reshape2_1.4.3 

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 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] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     backports_1.1.2    scales_1.0.0      
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1

Dominant PAS after Normalization

Briana Mittleman

4/1/2020

Pick dominant PAS

Dominant from original usage in filtered set