Connection between APA and RNA decay

Last updated: 2019-03-18

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Knit directory: threeprimeseq/analysis/

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File	Version	Author	Date	Message
Rmd	52ab386	Briana Mittleman	2019-03-18	look at gene 1 SD outside mean decay
html	1283669	Briana Mittleman	2019-03-15	Build site.
Rmd	5d6ac93	Briana Mittleman	2019-03-15	add decay analysis

I want to ask if more nuclear specific transcripts compared to total is associated with RNA decay.

library(tidyverse)

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✔ tidyr   0.8.3       ✔ stringr 1.4.0  
✔ readr   1.3.1       ✔ forcats 0.4.0

Warning: package 'tibble' was built under R version 3.5.2

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── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

decay=read.table(file = "../data/RNAdecay/tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>% select(gene_id,contains("RNAdecay"))

Change gene names:

geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F)

decay_geneNames=decay %>% inner_join(geneNames, by="gene_id") %>% select(GeneName, contains("RNAdecay"))

decay_geneNames_long=melt(decay_geneNames,id.vars = "GeneName", value.name = "RNA_Decay", variable.name = "Decay_Ind") %>% separate(Decay_Ind, into=c("type", "ind"), sep="_") %>% mutate(Individual=paste("X" , ind, sep="")) %>% select(GeneName, Individual, RNA_Decay)

Prepare apa value:

For each gene I need to get nuclear counts/nuclear + counts

I want to use the filtered 5% peak counts.

/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc

/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc

Make a dictionary from the individuals in the first line. I want them to have NA##### format

makepheno4decayComparison.py

nucCounts="/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc"

totCounts="/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc"

#top key is individual
OutPutdic={}


#problem keeping ind connected to column

Try in R

Nuclear first:

NucAPA=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc", stringsAsFactors = F, header = T) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chrom", "start", "end", "strand", "GeneName"), sep=":") %>% select(-chrom, -start, -end, -strand)

NucApaMelt=melt(NucAPA, id.vars =c( "peak", "GeneName"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_") %>% select(peak, GeneName, Individual, count)


NucAPA_bygene= NucApaMelt %>% group_by(GeneName,Individual) %>% summarise(NuclearSum=sum(count))

Total first:

TotAPA=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", stringsAsFactors = F, header = T) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chrom", "start", "end", "strand", "GeneName"), sep=":") %>% select(-chrom, -start, -end, -strand)

TotApaMelt=melt(TotAPA, id.vars =c( "peak", "GeneName"), value.name="count", variable.name="Ind")  %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_") %>% select(peak, GeneName, Individual, count)


TotAPA_bygene=TotApaMelt %>% group_by(GeneName,Individual) %>% summarise(TotalSum=sum(count))

Sum these together:

Apa_all=TotAPA_bygene %>% inner_join(NucAPA_bygene, by=c("GeneName", "Individual")) %>% filter(NuclearSum>0 |TotalSum>0 )  %>% mutate(APAvalue=NuclearSum/(NuclearSum+TotalSum)) %>% select(GeneName, Individual, APAvalue)

Join ith decay

APAandDecay=decay_geneNames_long %>% inner_join(Apa_all, by=c('GeneName', 'Individual'))


ngenes=APAandDecay %>% select(GeneName) %>% unique() %>% nrow()
ngenes

[1] 7888

plot it:

summary(lm(data=APAandDecay, APAvalue~RNA_Decay))


Call:
lm(formula = APAvalue ~ RNA_Decay, data = APAandDecay)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.46459 -0.15044 -0.01135  0.13392  0.58497 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.4373568  0.0003228 1354.83   <2e-16 ***
RNA_Decay   -0.0257699  0.0019255  -13.38   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2017 on 398991 degrees of freedom
Multiple R-squared:  0.0004487, Adjusted R-squared:  0.0004462 
F-statistic: 179.1 on 1 and 398991 DF,  p-value: < 2.2e-16

APAdecalAllindplot=ggplot(APAandDecay, aes(y=APAvalue, x=RNA_Decay)) + geom_point(aes(col=Individual)) +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(method="lm") + annotate("text", label="Estimated Slope= -.026", y=1, x=-1) + labs(title="Relationship between RNA decay \nand APA fraction counts", x=" mRNA decay rate/h", y= "Nuclear/(Nuclear + Total)")

APAdecalAllindplot

Version	Author	Date
1283669	Briana Mittleman	2019-03-15

ggsave(APAdecalAllindplot, file="../output/plots/APAandRNADecay_allInd.png", height = 7, width=15)

1 individual:

APAandDecay_18498= APAandDecay %>% filter(Individual=="X18498")

APAdecay_18498=ggplot(APAandDecay_18498, aes(y=APAvalue, x=RNA_Decay)) + geom_point() +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + annotate("text", label="Estimated Slope= -.133", y=0, x=-.8) + geom_smooth(method="lm")+ labs(title="Relationship between RNA decay \nand APA fraction counts", x=" mRNA decay rate/h", y= "Nuclear/(Nuclear + Total)")



APAdecay_18498

Version	Author	Date
1283669	Briana Mittleman	2019-03-15

ggsave(APAdecay_18498, file="../output/plots/APAandRNADecay_18498.png")

Saving 7 x 5 in image

summary(lm(data=APAandDecay_18498, APAvalue~RNA_Decay))


Call:
lm(formula = APAvalue ~ RNA_Decay, data = APAandDecay_18498)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.63123 -0.17159  0.00659  0.17479  0.47142 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.581252   0.002667 217.933  < 2e-16 ***
RNA_Decay   -0.133867   0.016938  -7.903 3.09e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2324 on 7766 degrees of freedom
Multiple R-squared:  0.007979,  Adjusted R-squared:  0.007851 
F-statistic: 62.46 on 1 and 7766 DF,  p-value: 3.094e-15

Look at most variable decay values

Most of the genes have a similar decay rate. To se if there is a trend I need to look at the genes with >1sd outside of the mean.

decay_zscore=decay_geneNames_long  %>% mutate(mean=mean(RNA_Decay), sd=sd(RNA_Decay)) %>%  group_by(GeneName) %>% mutate(geneMean=mean(RNA_Decay)) %>% mutate(Zscore=(geneMean-mean)/sd) %>% select(GeneName, Zscore) %>% unique() 



decay_1sd= decay_zscore %>% filter(abs(Zscore)>1) %>% select(GeneName)

Filter the apa and decay for these genes.

APAandDecay_1sd= APAandDecay %>% filter(GeneName %in% decay_1sd$GeneName)

APAandDecay_1sd %>% select(GeneName) %>% unique() %>% nrow()

[1] 938

summary(lm(data=APAandDecay_1sd, APAvalue~RNA_Decay))


Call:
lm(formula = APAvalue ~ RNA_Decay, data = APAandDecay_1sd)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.47225 -0.13964 -0.01415  0.12495  0.63026 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.396160   0.001103  359.08   <2e-16 ***
RNA_Decay   -0.072001   0.003283  -21.93   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1868 on 47713 degrees of freedom
Multiple R-squared:  0.009982,  Adjusted R-squared:  0.009962 
F-statistic: 481.1 on 1 and 47713 DF,  p-value: < 2.2e-16

APAdecalAllindplot_zgreat1=ggplot(APAandDecay_1sd, aes(y=APAvalue, x=RNA_Decay)) + geom_point() +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(method="lm")+ labs(title="Relationship between RNA decay \nand APA fraction counts", x=" mRNA decay rate/h", y= "Nuclear/(Nuclear + Total)")

APAdecalAllindplot_zgreat1

ggsave(APAdecalAllindplot_zgreat1, file="../output/plots/APAandRNADecay1SD_allInd.png", height = 7, width=7)

APAandDecay1SD_18498= APAandDecay_1sd %>% filter(Individual=="X18498")

APAdecay1sqd_18498=ggplot(APAandDecay1SD_18498, aes(y=APAvalue, x=RNA_Decay)) + geom_point() +geom_density2d(na.rm = TRUE, size = 1, colour = 'red')+geom_smooth(method="lm")+ labs(title="Relationship between RNA decay \nand APA fraction counts", x=" mRNA decay rate/h", y= "Nuclear/(Nuclear + Total)")

summary(lm(data=APAandDecay1SD_18498, APAvalue~RNA_Decay))


Call:
lm(formula = APAvalue ~ RNA_Decay, data = APAandDecay1SD_18498)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.55209 -0.15612  0.00324  0.15925  0.53943 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.527434   0.009106  57.923  < 2e-16 ***
RNA_Decay   -0.240981   0.029133  -8.272 4.56e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2203 on 928 degrees of freedom
Multiple R-squared:  0.06867,   Adjusted R-squared:  0.06766 
F-statistic: 68.42 on 1 and 928 DF,  p-value: 4.556e-16

APAdecay1sqd_18498

ggsave(APAdecay1sqd_18498, file="../output/plots/APAandRNADecay1SD_18498.png")

Saving 7 x 5 in image

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reshape2_1.4.3  forcats_0.4.0   stringr_1.4.0   dplyr_0.8.0.1  
 [5] purrr_0.3.1     readr_1.3.1     tidyr_0.8.3     tibble_2.0.1   
 [9] ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
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 [5] compiler_3.5.1   git2r_0.24.0     workflowr_1.2.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.3.1      cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0      
[21] haven_2.1.0      xfun_0.5         withr_2.1.2      xml2_1.2.0      
[25] httr_1.4.0       knitr_1.21       hms_0.4.2        generics_0.0.2  
[29] fs_1.2.6         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.4.0         readxl_1.3.0     rmarkdown_1.11  
[37] modelr_0.1.4     magrittr_1.5     whisker_0.3-2    MASS_7.3-51.1   
[41] backports_1.1.3  scales_1.0.0     htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.4-0 labeling_0.3     stringi_1.3.1   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Connection between APA and RNA decay

Briana Mittleman

3/14/2019

Prepare apa value:

Join ith decay

Look at most variable decay values