Last updated: 2020-12-15

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.Rmd
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    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/ResultsNoUnlifted.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/df_QC.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/infoContent.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.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ConsPhlop20.Rmd) and HTML (docs/ConsPhlop20.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd b82f567 brimittleman 2020-12-15 add enrichments and QTL overlap
html d5eaf0b brimittleman 2020-12-11 Build site.
Rmd a43b269 brimittleman 2020-12-11 add phylop20 analysis 
library(tidyverse)
── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.2.1     ✓ purrr   0.3.4
✓ tibble  2.1.3     ✓ dplyr   0.8.3
✓ tidyr   1.1.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.4.0
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(ggpubr)
library(cowplot)

********************************************************
Note: As of version 1.0.0, cowplot does not change the
  default ggplot2 theme anymore. To recover the previous
  behavior, execute:
  theme_set(theme_cowplot())
********************************************************

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

    get_legend
library(workflowr)
This is workflowr version 1.6.2
Run ?workflowr for help getting started

Copy and edit extractPhylopGeneral.py for phylop 20 way.

#extractPhylopGeneral_20way.py

python extractPhylopGeneral_20way.py  ../data/PhyloP/PAS_200downpregions2.bed ../data/PhyloP/PAS_phyloP20_200downstream2.txt 

python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200downpregions3.bed ../data/PhyloP/PAS_phyloP20_200downstream3.txt 
python extractPhylopGeneral_20way.py  ../data/PhyloP/PAS_200downpregions.bed ../data/PhyloP/PAS_phyloP20_200downstream.txt 

python extractPhylopGeneral_20way.py  ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed ../data/PhyloP/PAS_Phylop20.txt

python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200upregions.bed ../data/PhyloP/PAS_phyloP20_200upstream.txt
python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200upregions2.bed ../data/PhyloP/PAS_phyloP20_200upstream2.txt
python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200upregions3.bed ../data/PhyloP/PAS_phyloP20_200upstream3.txt
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene, loc)

phylores=read.table("../data/PhyloP/PAS_Phylop20.txt", col.names = c("chr","start","end","PAS", "phyloP"), stringsAsFactors = F) %>% drop_na()


Down=read.table("../data/PhyloP/PAS_phyloP20_200downstream.txt",col.names = c("chr", "start", "end", "PAS", "Down"),stringsAsFactors = F) %>% select(PAS, Down)%>% drop_na()
Down2=read.table("../data/PhyloP/PAS_phyloP20_200downstream2.txt",col.names = c("chr", "start", "end", "PAS", "Down2"),stringsAsFactors = F) %>% select(PAS, Down2)%>% drop_na()
Down3=read.table("../data/PhyloP/PAS_phyloP20_200downstream3.txt",col.names = c("chr", "start", "end", "PAS", "Down3"),stringsAsFactors = F) %>% select(PAS, Down3)%>% drop_na()


Up=read.table("../data/PhyloP/PAS_phyloP_200upstream.txt",col.names = c("chr", "start", "end", "PAS", "Up"),stringsAsFactors = F) %>% select(PAS, Up)%>% drop_na()
Up2=read.table("../data/PhyloP/PAS_phyloP_200upstream2.txt",col.names = c("chr", "start", "end", "PAS", "Up2"),stringsAsFactors = F) %>% select(PAS, Up2)%>% drop_na()
Up3=read.table("../data/PhyloP/PAS_phyloP_200upstream3.txt",col.names = c("chr", "start", "end", "PAS", "Up3"),stringsAsFactors = F) %>% select(PAS, Up3)%>% drop_na()

ResUpdownAll= phylores %>% inner_join(Down, by="PAS")%>% inner_join(Down2, by="PAS") %>% inner_join(Down3, by="PAS") %>% inner_join(Up, by="PAS") %>% inner_join(Up3, by="PAS")%>% inner_join(Up2, by="PAS") %>% select(-chr, -start,-end)


ResUpdownAll_gather= ResUpdownAll %>% gather("Set", "PhyloP", -PAS)

ResUpdownAll_gather$Set=factor(ResUpdownAll_gather$Set, levels=c("Up3", "Up2","Up", "phyloP","Down", "Down2", "Down3" ))
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") +theme_classic()+ theme(legend.position = "none")

Version Author Date
d5eaf0b brimittleman 2020-12-11 
PASMetaSm=PASMeta %>% select(PAS,loc)
ResUpdownAll_loc=ResUpdownAll_gather %>% inner_join(PASMetaSm,by="PAS") %>% mutate(region=ifelse(Set=="phyloP", "Yes", "No"))
cons20loc=ggplot(ResUpdownAll_loc, aes(x=Set, by=Set,fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs from PAS", title="PAS are more conserved than surrounding regions", y= "Phylop 20") + guides(fill = FALSE) + theme_classic() + facet_grid(~loc) + theme(axis.text.x =element_text(angle = 90))
cons20=ggplot(ResUpdownAll_loc, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions", y="Phylop 20") +theme_classic()+ theme(legend.position = "none")
phylop20both=plot_grid(cons20,cons20loc, nrow = 2,scale = c(.9,.9), labels=c("a","b"))
phylop20both

Version Author Date
d5eaf0b brimittleman 2020-12-11 
pdf("../output/revisions/phylop20.pdf", height=8, width=6)
phylop20both
dev.off()
png 
  2

Compare significant and non significant PAS:

DiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)
DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))



NucReswPhy=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr","start","end"))
ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2", name="Signficant")

ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", name="Signficant PAS") + labs(title="Mean PhyloP scores for tested PAS") + annotate("text",label="Wilcoxan, p=0.0016",x=0,y=.75)

more <0 than expected:

x=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
m= nrow(NucReswPhy %>% filter(phyloP<0))
n=nrow(NucReswPhy %>% filter(phyloP>=0))
k=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 239
#actual:
x
[1] 326
#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 4.083027e-09
b=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
n=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhy %>% filter(phyloP<0))
N=nrow(NucReswPhy)

(b/n)/(B/N)
[1] 1.360191

This is even more enriched than the 100 way phyloP.


sessionInfo()
R version 3.6.1 (2019-07-05)
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_1.6.2 cowplot_1.0.0   ggpubr_0.3.0    forcats_0.4.0  
 [5] stringr_1.4.0   dplyr_0.8.3     purrr_0.3.4     readr_1.3.1    
 [9] tidyr_1.1.0     tibble_2.1.3    ggplot2_3.2.1   tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] httr_1.4.1         jsonlite_1.6       carData_3.0-2     
 [4] modelr_0.1.8       assertthat_0.2.1   cellranger_1.1.0  
 [7] yaml_2.2.0         pillar_1.4.2       backports_1.1.4   
[10] lattice_0.20-38    glue_1.3.1         digest_0.6.20     
[13] RColorBrewer_1.1-2 promises_1.0.1     ggsignif_0.5.0    
[16] rvest_0.3.5        colorspace_1.4-1   htmltools_0.3.6   
[19] httpuv_1.5.1       plyr_1.8.4         pkgconfig_2.0.2   
[22] broom_0.5.2        haven_2.3.1        scales_1.1.0      
[25] whisker_0.3-2      openxlsx_4.1.0.1   later_0.8.0       
[28] rio_0.5.16         git2r_0.26.1       generics_0.0.2    
[31] farver_2.0.1       car_3.0-5          ellipsis_0.2.0.1  
[34] withr_2.1.2        lazyeval_0.2.2     cli_2.2.0         
[37] magrittr_1.5       crayon_1.3.4       readxl_1.3.1      
[40] evaluate_0.14      fs_1.3.1           fansi_0.4.0       
[43] nlme_3.1-140       rstatix_0.5.0      xml2_1.3.2        
[46] foreign_0.8-71     tools_3.6.1        data.table_1.13.2 
[49] hms_0.5.3          lifecycle_0.1.0    munsell_0.5.0     
[52] reprex_0.3.0       zip_2.0.3          compiler_3.6.1    
[55] rlang_0.4.6        grid_3.6.1         rstudioapi_0.10   
[58] labeling_0.3       rmarkdown_1.13     gtable_0.3.0      
[61] abind_1.4-5        DBI_1.1.0          curl_3.3          
[64] reshape2_1.4.3     R6_2.4.0           lubridate_1.7.4   
[67] knitr_1.23         rprojroot_1.3-2    stringi_1.4.3     
[70] Rcpp_1.0.5         vctrs_0.3.0        dbplyr_1.4.2      
[73] tidyselect_1.1.0   xfun_0.8