Explore human dominant intronic
Briana Mittleman
1/13/2020
Last updated: 2020-01-13
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1121102 | brimittleman | 2020-01-13 | add dpau and de overlap with dom intronic |
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(workflowr)This is workflowr version 1.5.0
Run ?workflowr for help getting startedIn this analysis I want to further explore the PAS that are dominant in human introns. For these PAS the dominant intron in the chimps are in the 3’ UTR. I want to look to see if these are differentially used and if these are in differentially expressed genes.
HumanIntronChimpUTR=read.table("../data/DominantPAS/Nuclear_HumanIntronicChimpUTR.txt",stringsAsFactors = F, header = T ) %>% rename("PAS"=HumanPAS)
SameDom= read.table( "../data/DominantPAS/Nuclear_SameDom.txt", header = T, stringsAsFactors = F) %>% rename("PAS"=HumanPAS)
HumanUTRChimpIntron= read.table( "../data/DominantPAS/Nuclear_HumanUTRChimpIntronic.txt", header = T, stringsAsFactors = F) %>% rename("PAS"=HumanPAS) Diff APA
Differentially used PAS:
DiffUsage=read.table("../data/DiffIso_Nuclear/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)This does not have the PAS number. I need to add them by joining by location.
PASMeta=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene)DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))Number overlapping with different dom.
HumanIntronChimpUTR_diffUsed=HumanIntronChimpUTR %>% inner_join(DiffUsagePAS, by="PAS")
nrow(HumanIntronChimpUTR_diffUsed)[1] 227nrow(HumanIntronChimpUTR_diffUsed)/nrow(HumanIntronChimpUTR)[1] 0.2451404Number overlapping in same:
SameDom_diffUsed=SameDom %>%inner_join(DiffUsagePAS, by="PAS")
nrow(SameDom_diffUsed)[1] 864nrow(SameDom_diffUsed)/nrow(SameDom)[1] 0.06274054prop.test(x=c(nrow(HumanIntronChimpUTR_diffUsed),nrow(SameDom_diffUsed)), n=c(nrow(HumanIntronChimpUTR),nrow(SameDom)),alternative = "greater")
2-sample test for equality of proportions with continuity
correction
data: c(nrow(HumanIntronChimpUTR_diffUsed), nrow(SameDom_diffUsed)) out of c(nrow(HumanIntronChimpUTR), nrow(SameDom))
X-squared = 417.4, df = 1, p-value < 2.2e-16
alternative hypothesis: greater
95 percent confidence interval:
0.1583244 1.0000000
sample estimates:
prop 1 prop 2
0.24514039 0.06274054 Better background is random PAS, not the same used.
Diff Expression
In my first paper I saw a correlation between effect size for intronic PAS and eQTL effect size. I want to see if there is a correlation between the dAPA effect size and DE effect size for the intronic dominant PAS.
I want to include the dPAU for all of the PAS.
effectsize=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron', 'logef' ,'Human', 'Chimp','deltaPAU')) %>% filter(intron != "intron") %>% separate(intron, into=c("chr", "start", "end", "gene"), sep=":")
effectsize$start=as.numeric(effectsize$start)
effectsize$end=as.numeric(effectsize$end)
effectsize_PAS=effectsize%>% inner_join(PASMeta, by=c("start", "end","chr", "gene")) %>% dplyr::select(PAS, deltaPAU)HumanIntronChimpUTR_dpau= HumanIntronChimpUTR %>% inner_join(effectsize_PAS,by="PAS")
SameDom_dpau= SameDom %>% inner_join(effectsize_PAS,by="PAS")
HumanUTRChimpIntron_dpau= HumanUTRChimpIntron %>% inner_join(effectsize_PAS,by="PAS")Pull in the expression values.
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DEtested_allres.txt", header =T, col.names = c("Gene_stable_ID", "logFC", "AveExpr","t", "P.Value", "adj.Pval", "B"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% rename("gene"=Gene.name) Join:
HumanIntronChimpUTR_dpau_DE= HumanIntronChimpUTR_dpau %>% inner_join(DEgenes, by="gene")
HumanIntronChimpUTR_dpau_DE$deltaPAU = as.numeric(HumanIntronChimpUTR_dpau_DE$deltaPAU)
ggplot(HumanIntronChimpUTR_dpau_DE,aes(x=deltaPAU, y=logFC)) +geom_point()+ geom_smooth(method = "lm")
summary(lm(HumanIntronChimpUTR_dpau_DE$logFC~HumanIntronChimpUTR_dpau_DE$deltaPAU))
Call:
lm(formula = HumanIntronChimpUTR_dpau_DE$logFC ~ HumanIntronChimpUTR_dpau_DE$deltaPAU)
Residuals:
Min 1Q Median 3Q Max
-4.1362 -0.3502 0.0593 0.4531 3.5541
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.14624 0.06555 2.231 0.02606
HumanIntronChimpUTR_dpau_DE$deltaPAU -1.10141 0.30561 -3.604 0.00034
(Intercept) *
HumanIntronChimpUTR_dpau_DE$deltaPAU ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9048 on 586 degrees of freedom
Multiple R-squared: 0.02168, Adjusted R-squared: 0.02001
F-statistic: 12.99 on 1 and 586 DF, p-value: 0.0003401SameDom_dpau_DE= SameDom_dpau %>% inner_join(DEgenes, by="gene")
SameDom_dpau_DE$deltaPAU = as.numeric(SameDom_dpau_DE$deltaPAU)
ggplot(SameDom_dpau_DE,aes(x=deltaPAU, y=logFC)) +geom_point()+ geom_smooth(method = "lm")
summary(lm(SameDom_dpau_DE$logFC~SameDom_dpau_DE$deltaPAU))
Call:
lm(formula = SameDom_dpau_DE$logFC ~ SameDom_dpau_DE$deltaPAU)
Residuals:
Min 1Q Median 3Q Max
-7.0774 -0.3396 -0.0298 0.3282 7.0806
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02922 0.01168 2.502 0.0124 *
SameDom_dpau_DE$deltaPAU -0.03098 0.08570 -0.361 0.7178
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8288 on 5389 degrees of freedom
Multiple R-squared: 2.424e-05, Adjusted R-squared: -0.0001613
F-statistic: 0.1306 on 1 and 5389 DF, p-value: 0.7178HumanUTRChimpIntron_dpau_de= HumanUTRChimpIntron_dpau %>% inner_join(DEgenes, by="gene")
HumanUTRChimpIntron_dpau_de$deltaPAU = as.numeric(HumanUTRChimpIntron_dpau_de$deltaPAU)
ggplot(HumanUTRChimpIntron_dpau_de,aes(x=deltaPAU, y=logFC)) +geom_point()+ geom_smooth(method = "lm")
summary(lm(HumanUTRChimpIntron_dpau_de$logFC~HumanUTRChimpIntron_dpau_de$deltaPAU))
Call:
lm(formula = HumanUTRChimpIntron_dpau_de$logFC ~ HumanUTRChimpIntron_dpau_de$deltaPAU)
Residuals:
Min 1Q Median 3Q Max
-3.3936 -0.6196 -0.1195 0.4382 3.7021
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2858 0.1531 1.867 0.0653
HumanUTRChimpIntron_dpau_de$deltaPAU 0.1503 0.6346 0.237 0.8134
(Intercept) .
HumanUTRChimpIntron_dpau_de$deltaPAU
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.053 on 87 degrees of freedom
Multiple R-squared: 0.0006441, Adjusted R-squared: -0.01084
F-statistic: 0.05607 on 1 and 87 DF, p-value: 0.8134Looks like this is the opposite direction. Here we have greater delta pau correlated with greater gene expression for human when intronic…
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_1.5.0 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] 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 later_0.7.5 git2r_0.26.1 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 modelr_0.1.2
[37] magrittr_1.5 whisker_0.3-2 backports_1.1.2 scales_1.0.0
[41] promises_1.0.1 htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5 labeling_0.3 stringi_1.2.4
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1 crayon_1.3.4