Verify first intron result

Last updated: 2019-11-08

Checks: 6 1

Knit directory: apaQTL/analysis/

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    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
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    Modified:   code/apaQTLsnake.err
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    Deleted:    reads_graphs.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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File	Version	Author	Date	Message
Rmd	d72c604	brimittleman	2019-11-08	add first intron analysis
html	e22e31c	brimittleman	2019-06-20	Build site.
Rmd	b2def08	brimittleman	2019-06-20	first intron in sites with ss
html	5a02775	brimittleman	2019-06-18	Build site.
Rmd	078b340	brimittleman	2019-06-18	add first intron length
html	b3328b6	brimittleman	2019-06-18	Build site.
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 ──

✔ 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()

library(cowplot)


Attaching package: 'cowplot'

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

    ggsave

library(workflowr)

This is workflowr version 1.4.0
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 5347 rows containing non-finite values (stat_bin).

Warning: Removed 8 rows containing missing values (geom_bar).

Version	Author	Date
b3328b6	brimittleman	2019-06-18

ggplot(pas2intron,aes(x=distance2PAS, fill=LengthCat))  + facet_grid(~LengthCat) + xlim(0,5000) +  stat_ecdf(aes(col=LengthCat))

Warning: Removed 5347 rows containing non-finite values (stat_ecdf).

Version	Author	Date
b3328b6	brimittleman	2019-06-18

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)


write.table(pas2intron_intron, "../data/intron_analysis/NuclearIntronPASwithWhichintron.txt", col.names = T, row.names = F, quote = F, sep="\t")

ggplot(pas2intron_intron,aes(x=Intronid)) +  geom_bar(stat="count") + labs(title="intron ID for nuclear intronic pas", x="intron ID")

Version	Author	Date
b3328b6	brimittleman	2019-06-18

summary(pas2intron_intron$Intronid)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
2775 1840 1377 1110  828  677  547  439  380  347  259  221  181  164  137 
  16   17   18   19   20   21   22   23   24   25   26   27   28   29   30 
 100   99   90   75   57   44   54   49   56   36   28   28   17   13   10 
  31   32   33   34   35   36   37   38   39   40   41   42   43   44   45 
   6   10    9    3    5    6    3    7    8   11   10    7    5    9    6 
  46   47   48   49   50   52   55   57   58   59   60   63   64   69   72 
   3    2    1    1    2    2    1    3    6    3    1    1    3    1    2 
  74   76   77   79   84   85   90   94   96  164  165 
   2    1    2    1    1    1    1    2    1    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 1870 rows containing non-finite values (stat_count).

Warning: Removed 4 rows containing missing values (geom_bar).

Version	Author	Date
b3328b6	brimittleman	2019-06-18

Maybe by the number of introns?

summary(pas2intron_intron_usagecat$nintron)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    3.0     6.0    11.0    14.2    18.0   171.0

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 1870 rows containing non-finite values (stat_count).

Warning: Removed 3 rows containing missing values (geom_bar).

Version	Author	Date
b3328b6	brimittleman	2019-06-18

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)

Number of introns in each and normalize by average intron size.

pas2intron_intron_grouped=pas2intron_intron %>% group_by(Intronid) %>% summarise(nBin=n(), meanSize=mean(intronLength))  %>% mutate(normNBin=nBin/meanSize) 


pas2intron_intron_grouped$Intronid=as.integer(as.character(pas2intron_intron_grouped$Intronid))

ggplot(pas2intron_intron_grouped, aes(x=Intronid, y=normNBin)) +geom_bar(stat="identity") + labs(title="PAS by Intron", y="normalized number in intron category", x="intron category")

#zoom in 1-10  
pas2intron_intron_grouped_small=pas2intron_intron_grouped %>% filter(Intronid <=10)
ggplot(pas2intron_intron_grouped_small, aes(x=Intronid, y=normNBin)) +geom_bar(stat="identity") + labs(title="PAS by Intron", y="normalized number in intron category", x="intron category")

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) %>%  mutate(LengthCat=ifelse(intronLength<=3785, "first", ifelse(intronLength>3785 &intronLength<=8872, "second", ifelse(intronLength>8872 &intronLength<=22928, "third", "fourth"))))

pas2intronTot$LengthCat <- factor(pas2intronTot$LengthCat, levels=c("first", "second", "third", "fourth"))
pas2intronTot_intron=pas2intronTot %>% inner_join(introns, by=c("intronStart","intronEnd","gene", "strand" ))

write.table(pas2intronTot_intron, "../data/intron_analysis/TotalIntronPASwithWhichintron.txt", col.names = T, row.names = F, quote = F, sep="\t")

summary(pas2intronTot_intron$nintron)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.00    6.00   11.00   14.66   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 1219 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 3089 rows containing non-finite values (stat_ecdf).

Version	Author	Date
b3328b6	brimittleman	2019-06-18

plot_grid(nuclear_cdf,totalcdf)

Warning: Removed 1870 rows containing non-finite values (stat_ecdf).

Warning: Removed 1219 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))

Final plot:
first intron (conditioned on the intron being > 2KB) shows no signal (plotting the first 2kb only)

firstintron_nuclear=pas2intron_intron_usagecat_introncat %>% filter(Intronid==1,intronLength>2000) 

firstintron_total=pas2intronTot_intron_usagecat_introncat %>% filter(Intronid==1,intronLength>2000)

ggplot(firstintron_nuclear,aes(x=distance2PAS, fill=LengthCat)) + geom_histogram(bins=50) +xlim(0,2000) + facet_grid(~LengthCat)+ labs(title="Nuclear intronic PAS in first intron (3025)")

Warning: Removed 1970 rows containing non-finite values (stat_bin).

Warning: Removed 8 rows containing missing values (geom_bar).

Version	Author	Date
5a02775	brimittleman	2019-06-18

ggplot(firstintron_total,aes(x=distance2PAS, fill=LengthCat)) + geom_histogram(bins=50) +xlim(0,2000) + facet_grid(~LengthCat) + labs(title="Total intronic PAS in first intron (1804)")

Warning: Removed 1069 rows containing non-finite values (stat_bin).

Warning: Removed 8 rows containing missing values (geom_bar).

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_1.4.0 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_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        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   reshape2_1.4.3  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 labeling_0.3     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Verify first intron result

Briana Mittleman

6/18/2019

Nuclear

Beginning of introns

First intron

Total

First intron

Both fracitons