Last updated: 2019-06-20
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Knit directory: apaQTL/analysis/
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File | Version | Author | Date | Message |
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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 1.3.0
Run ?workflowr for help getting started
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"))
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).
Version | Author | Date |
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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 6143 rows containing non-finite values (stat_ecdf).
Version | Author | Date |
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b3328b6 | brimittleman | 2019-06-18 |
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 |
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b3328b6 | brimittleman | 2019-06-18 |
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$Intronid))
ggplot(pas2intron_intron_usagecat,aes(x=Intronid, fill=UsageCat)) + geom_bar(stat="count") + labs(title="intron ID for nuclear intronic pas", x="intron ID") + facet_grid(~UsageCat)+ xlim(0,10)
Warning: Removed 2108 rows containing non-finite values (stat_count).
Warning: Removed 4 rows containing missing values (geom_bar).
Version | Author | Date |
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b3328b6 | brimittleman | 2019-06-18 |
Maybe by the number of introns?
summary(pas2intron_intron_usagecat$nintron)
Min. 1st Qu. Median Mean 3rd Qu. Max.
3.00 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).
Version | Author | Date |
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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)
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 14.62 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).
Version | Author | Date |
---|---|---|
b3328b6 | brimittleman | 2019-06-18 |
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)
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).
Version | Author | Date |
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b3328b6 | brimittleman | 2019-06-18 |
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).
Version | Author | Date |
---|---|---|
b3328b6 | brimittleman | 2019-06-18 |
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))
Version | Author | Date |
---|---|---|
b3328b6 | brimittleman | 2019-06-18 |
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 2280 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 1277 rows containing non-finite values (stat_bin).
Warning: Removed 8 rows containing missing values (geom_bar).
Version | Author | Date |
---|---|---|
5a02775 | brimittleman | 2019-06-18 |
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