Redo Expression Effect Size plot- Chimp Negative on both scales

Last updated: 2020-05-31

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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    Untracked:  output/Total_DEeffectsize
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    Untracked:  output/Total_DEeffectsizeNotJusttop.pdf
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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.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	033290c	brimittleman	2020-05-31	add main figures 1-4
html	88ca1ad	brimittleman	2020-05-21	Build site.
Rmd	89a7189	brimittleman	2020-05-21	add fix effect dir and intronic plots

Untill now I have been plotting effect size correlations between human and chimp for expression, translation, and APA using inconsitent directions.

I will flip the expression effect sizes to match the apa delta PAU.

Chimp will be negative Human will be postivie

library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

#stat cor is pearson 
library(qvalue)
library(tidyverse)

── Attaching packages ───────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0

── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

library("viridis")

Loading required package: viridisLite

library(cowplot)


Attaching package: 'cowplot'

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

    get_legend

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

    ggsave

Load data

APA data:

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% dplyr::select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) %>% filter(loc %in% c("intron","utr3"))

DiffIsoTop=DiffIso %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()

I will use the top used PAS for all plots (colored and not colored). This way there is no bias in the set.
###Expression data

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)

DE=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" ,  "P.Value" ,  "adj.P.Val", "B"  )) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID) %>% mutate(CorrectedlogFC=-1*logFC)

Translation data:

In this analysis - was already up regulated in chimp

Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name)

Plot data

APA and expression

All data:

DeandAPA= DiffIsoTop %>% inner_join(DE, by="gene")

nrow(DeandAPA)

[1] 7391

DE_all=ggplot(DeandAPA,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v DE", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

DE_all

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

Seperate by size:

DE_split=ggplot(DeandAPA,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="APA v DE", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -1) +theme_classic(base_size = 12)

DE_split

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

DeandAPA %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron  1313
2 utr3    6078

Subset to significant in both:

DeandAPASig= DeandAPA %>% filter(SigPAU2=="Yes", adj.P.Val<=0.05)

nrow(DeandAPASig)

[1] 408

DE_sig_all=ggplot(DeandAPASig,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Significant differences in APA and expression", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

DE_sig_all

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

Seperate by size:

DE_sig_split=ggplot(DeandAPASig,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in APA and expression", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -1) +theme_classic(base_size = 12)

DE_sig_split

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

DeandAPASig %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron    65
2 utr3     343

plot together:

plot_grid(DE_all, DE_split,DE_sig_all,DE_sig_split)

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

Write out data for figures:

write.table(DeandAPA, "../output/DEandAPA.txt", col.names = T, row.names = F, quote = F)
write.table(DeandAPASig, "../output/DEandAPA_sig.txt", col.names = T, row.names = F, quote = F)

APA and translation

RiboandAPA= DiffIsoTop %>% inner_join(Ribo, by="gene")

nrow(RiboandAPA)

[1] 6407

TE_all=ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v TE", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

TE_all

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

Seperate by size:

TE_split=ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="APA v TE", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -1) +theme_classic(base_size = 12)

TE_split

Number of intronic and 3’ UTR

RiboandAPA %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron  1138
2 utr3    5269

Subset to significant in both:

RiboandAPASig= RiboandAPA %>% filter(SigPAU2=="Yes", HvC.FDR<=0.05)

nrow(RiboandAPASig)

[1] 214

TE_sig_all=ggplot(RiboandAPASig,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Significant differences in APA and translation", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

TE_sig_all

Version	Author	Date
88ca1ad	brimittleman	2020-05-21

Seperate by size:

TE_sig_split=ggplot(RiboandAPASig,aes(y=deltaPAU, x=HvC.beta, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in APA and translation", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -1) +theme_classic(base_size = 12)

TE_sig_split

RiboandAPASig %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron    37
2 utr3     177

plot together:

plot_grid(TE_all, TE_split,TE_sig_all,TE_sig_split)

plot DE and TE

plot_grid(DE_sig_all,TE_sig_all,DE_sig_split, TE_sig_split)

Plot with logef rather than deltaPAU

ggplot(DeandAPASig,aes(y=logef, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v DE", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

ggplot(DeandAPASig,aes(y=logef, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in APA and expression", x="DE log effect size", y="APA log effect size") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -10) +theme_classic(base_size = 12)

consistent sign.

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] cowplot_0.9.4     viridis_0.5.1     viridisLite_0.3.0
 [4] forcats_0.3.0     stringr_1.3.1     dplyr_0.8.0.1    
 [7] purrr_0.3.2       readr_1.3.1       tidyr_0.8.3      
[10] tibble_2.1.1      tidyverse_1.2.1   qvalue_2.14.0    
[13] ggpubr_0.2        magrittr_1.5      ggplot2_3.1.1    

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     splines_3.5.1     
 [4] haven_1.1.2        lattice_0.20-38    colorspace_1.3-2  
 [7] generics_0.0.2     htmltools_0.3.6    yaml_2.2.0        
[10] utf8_1.1.4         rlang_0.4.0        later_0.7.5       
[13] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[16] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[19] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[22] workflowr_1.6.0    cellranger_1.1.0   rvest_0.3.2       
[25] evaluate_0.12      labeling_0.3       knitr_1.20        
[28] httpuv_1.4.5       fansi_0.4.0        broom_0.5.1       
[31] Rcpp_1.0.4.6       promises_1.0.1     scales_1.0.0      
[34] backports_1.1.2    jsonlite_1.6       fs_1.3.1          
[37] gridExtra_2.3      hms_0.4.2          digest_0.6.18     
[40] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[43] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[46] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[49] xml2_1.2.0         lubridate_1.7.4    rstudioapi_0.10   
[52] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[55] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[58] compiler_3.5.1

Redo Expression Effect Size plot- Chimp Negative on both scales

Briana Mittleman

5/21/2020

Load data

APA data:

Translation data:

Plot data

APA and expression

APA and translation

plot DE and TE