Last updated: 2019-06-18

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Knit directory: apaQTL/analysis/

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Rmd 01bc8aa brimittleman 2019-06-18 add verify first inton res

In the previous analysis I saw that most of my intronic pas are in the first intron and skew toward the beginning of long introns. I will further explore this result here.

library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::lag()    masks stats::lag()
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started

Nuclear

These are the nuclear intronic PAS

pas2intron=read.table("../data/intron_analysis/IntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand"),stringsAsFactors = F) %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage) %>% mutate(intronLength=intronEnd-intronStart , distance2PAS= ifelse(strand=="+", PASloc-intronStart, intronEnd-PASloc), propIntron=distance2PAS/intronLength) %>% mutate(LengthCat=ifelse(intronLength<=3929, "first", ifelse(intronLength>3929 &intronLength<=9220, "second", ifelse(intronLength>9220 &intronLength<=24094, "third", "fourth"))))

pas2intron$LengthCat <- factor(pas2intron$LengthCat, levels=c("first", "second", "third", "fourth"))

Beginning of introns

I want to plot the absolute distance rather than the standardized distance to the 5’ ss.

ggplot(pas2intron,aes(x=distance2PAS, fill=LengthCat)) + geom_histogram(bins=100)  + facet_grid(~LengthCat) + xlim(0,5000)
Warning: Removed 6143 rows containing non-finite values (stat_bin).
Warning: Removed 8 rows containing missing values (geom_bar).

ggplot(pas2intron,aes(x=distance2PAS, fill=LengthCat))  + facet_grid(~LengthCat) + xlim(0,5000) +  stat_ecdf(aes(col=LengthCat)) 
Warning: Removed 6143 rows containing non-finite values (stat_ecdf).

First intron

This is not the correct analysis. I need to actually look at which intron from all of them.

this is the file I created to get the introns. I need to remove genes with only 1 introm.

introns=read.table("/project2/gilad/briana/apaQTL/data/intron_analysis/transcriptsMinusExons.sort.bed",stringsAsFactors = F, col.names = c("chrom", "intronStart", "intronEnd", "gene", "score", "strand")) %>% group_by(gene)  %>% filter(!grepl("hap",chrom)) %>% mutate(Intronid=ifelse(strand=="+",  1:n(),n():1), nintron=n()) %>% filter(nintron>2)

Join with PAS:

pas2intron_intron=pas2intron %>% inner_join(introns, by=c("intronStart","intronEnd","gene", "strand" ))
pas2intron_intron$Intronid=as.factor(pas2intron_intron$Intronid)
ggplot(pas2intron_intron,aes(x=Intronid)) +  geom_bar(stat="count") + labs(title="intron ID for nuclear intronic pas", x="intron ID")

summary(pas2intron_intron$Intronid)
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
3147 2111 1585 1278  896  725  633  503  432  384  287  238  188  178  145 
  16   17   18   19   20   21   22   23   24   25   26   27   28   29   30 
 119  113  106   83   71   56   63   48   58   40   36   31   18   14    9 
  31   32   33   34   35   36   37   38   39   40   41   42   43   44   45 
  11   12   13    6    3    6    5   10    7   11    9    8    5    9    6 
  46   47   48   49   50   51   52   53   55   57   58   59   60   64   66 
   2    3    1    1    6    4    7    4    1    3    6    3    1    1    5 
  67   68   69   70   72   73   74   76   77   79   85   88   89   90   94 
   4    4    5    1    3    2    1    1    2    1    1    3    2    1    2 
  96  160  165 
   2    1    2

I want to see if the usage is the same over this:

pas2intron_intron_usagecat= pas2intron_intron %>% mutate(UsageCat=ifelse(meanUsage<=.1, "<.1", ifelse(meanUsage>.1 &meanUsage<=.2, "<.2", ifelse(meanUsage>.2 &meanUsage<=.3, "<.3", ">.3"))))
pas2intron_intron_usagecat$Intronid=as.numeric(as.character(pas2intron_intron_usagecat$Intronid))
ggplot(pas2intron_intron_usagecat,aes(x=Intronid, fill=UsageCat)) +  geom_bar(stat="count") + labs(title="intron ID for nuclear intronic pas", x="intron ID") + facet_grid(~UsageCat)+ xlim(0,10)
Warning: Removed 2108 rows containing non-finite values (stat_count).
Warning: Removed 4 rows containing missing values (geom_bar).

Maybe by the number of introns?

summary(pas2intron_intron_usagecat$nintron)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.00    6.00   11.00   14.41   18.00  171.00 
pas2intron_intron_usagecat_introncat= pas2intron_intron_usagecat %>% mutate(IntronCat=ifelse(nintron<=6, "first (<6)", ifelse(nintron>6 &nintron<=11, "second (6-11)", ifelse(nintron>11 &nintron<=18, "third (11-18)", "fourth (>18)"))))

pas2intron_intron_usagecat_introncat$IntronCat <- factor(pas2intron_intron_usagecat_introncat$IntronCat, levels=c("first (<6)", "second (6-11)", "third (11-18)", "fourth (>18)"))
ggplot(pas2intron_intron_usagecat_introncat,aes(x=Intronid, fill=IntronCat)) +  geom_bar(stat="count") + labs(title="intron ID for nuclear intronic pas", x="intron ID") + facet_grid(~IntronCat) + xlim(0,10)
Warning: Removed 2108 rows containing non-finite values (stat_count).
Warning: Removed 3 rows containing missing values (geom_bar).

nuclear_cdf=ggplot(pas2intron_intron_usagecat_introncat,aes(x=Intronid, fill=IntronCat)) +  stat_ecdf(aes(col=IntronCat)) + labs(title="intron ID for Nuclear intronic pas", x="intron ID") + xlim(0,10)+ geom_vline(xintercept = 2)

Total

First intron

pas2intronTot=read.table("../data/intron_analysis/TotalIntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand"),stringsAsFactors = F) %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage) %>% mutate(intronLength=intronEnd-intronStart , distance2PAS= ifelse(strand=="+", PASloc-intronStart, intronEnd-PASloc), propIntron=distance2PAS/intronLength)

pas2intronTot_intron=pas2intronTot %>% inner_join(introns, by=c("intronStart","intronEnd","gene", "strand" ))
summary(pas2intronTot_intron$nintron)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.00    6.00   11.00   14.62   18.00  171.00 
pas2intronTot_intron_usagecat_introncat= pas2intronTot_intron %>% mutate(IntronCat=ifelse(nintron<=6, "first (<6)", ifelse(nintron>6 &nintron<=11, "second (6-11)", ifelse(nintron>11 &nintron<=18, "third (11-18)", "fourth (>18)"))))

ggplot(pas2intronTot_intron_usagecat_introncat,aes(x=Intronid, fill=IntronCat)) +  geom_bar(stat="count") + labs(title="intron ID for Total intronic pas", x="intron ID") + facet_grid(~IntronCat) + xlim(0,10)
Warning: Removed 1327 rows containing non-finite values (stat_count).
Warning: Removed 3 rows containing missing values (geom_bar).

totalcdf=ggplot(pas2intronTot_intron_usagecat_introncat,aes(x=Intronid, fill=IntronCat)) +  stat_ecdf(aes(col=IntronCat)) + labs(title="intron ID for Total intronic pas", x="intron ID") + xlim(0,10) + geom_vline(xintercept = 2)

Both fracitons

Plot both:

pas2intronTot_intron_usagecat_introncat_frac=pas2intronTot_intron_usagecat_introncat %>% mutate(fraction="Total") %>% select(Intronid,IntronCat,fraction)

pas2intron_intron_usagecat_introncat_frac=pas2intron_intron_usagecat_introncat%>% mutate(fraction="Nuclear") %>% select(Intronid,IntronCat,fraction)

intronidboth=bind_rows(pas2intronTot_intron_usagecat_introncat_frac,pas2intron_intron_usagecat_introncat_frac)
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
ggplot(intronidboth,aes(x=Intronid)) +  stat_ecdf(aes(col=fraction)) + labs(title="intron ID for intronic pas", x="intron ID") + xlim(0,10) + facet_grid(~IntronCat)
Warning: Removed 3435 rows containing non-finite values (stat_ecdf).

plot_grid(nuclear_cdf,totalcdf)
Warning: Removed 2108 rows containing non-finite values (stat_ecdf).
Warning: Removed 1327 rows containing non-finite values (stat_ecdf).

Usage in both fractions.

TotalIntronicUsage=pas2intronTot_intron_usagecat_introncat %>% mutate(fraction="Total") %>% select(meanUsage,fraction)

NuclearIntronicUsage=pas2intron_intron_usagecat_introncat%>% mutate(fraction="Nuclear") %>% select(meanUsage,fraction)

bothIntronicUsage=bind_rows(TotalIntronicUsage,NuclearIntronicUsage)
ggplot(bothIntronicUsage, aes(x=meanUsage))  +  stat_ecdf(aes(col=fraction)) 


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_1.3.0 cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   reshape2_1.4.3   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4