Last updated: 2020-04-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
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    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 R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 9676d72 brimittleman 2020-04-06 updated anno
html 7d4c862 brimittleman 2020-03-25 Build site.
Rmd 34a5358 brimittleman 2020-03-25 add same diff dom
html f180064 brimittleman 2020-03-24 Build site.
Rmd 73127b5 brimittleman 2020-03-24 add con and dom

Here I can combine 2 lines of analysis. I will look to see if hte dominant PAS are more conserved than the non dominant PAS.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract

Load results on conservation with phylop.

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, Human, Chimp)
phylores=read.table("../data/PhyloP/PAS_phyloP.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na() %>% inner_join(PASMeta, by=c("chr", "start", "end"))

For the domiant PAS I will be inclusive on ties.

allPAS= read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T) 
ChimpPASwMean =allPAS %>% dplyr::select(-Human)
HumanPASwMean =allPAS %>% dplyr::select(-Chimp)

Chimp_Dom2= ChimpPASwMean %>%
  group_by(gene) %>%
  top_n(1,Chimp) %>% 
  mutate(nPer=n()) 
nrow(Chimp_Dom2%>% filter(nPer>1) )
[1] 200
Human_Dom2= HumanPASwMean %>%
  group_by(gene) %>%
  top_n(1,Human) %>% 
  mutate(nPer=n())

Add this information.

phylores_dom = phylores %>% mutate(ChimpDom=ifelse(PAS %in% Chimp_Dom2$PAS, "Yes","No"), HumanDom=ifelse(PAS %in% Human_Dom2$PAS, "Yes", "No"))

Plot:

ggplot(phylores_dom, aes(x=ChimpDom,y=phyloP, fill=ChimpDom)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + stat_compare_means() + labs(x="Is PAS dominant in Chimp?", title="Chimp Dominant PAS are more conserved than non dominant PAS") + theme(legend.position = "none")

Version Author Date
f180064 brimittleman 2020-03-24 
ggplot(phylores_dom, aes(x=HumanDom,y=phyloP, fill=HumanDom)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + stat_compare_means() + labs(x="Is PAS dominant in Human?", title="Human Dominant PAS are more conserved than non dominant PAS") + theme(legend.position = "none")

Version Author Date
f180064 brimittleman 2020-03-24

Seperate this by mean usage.

Use the mean usag for human with dominance for human and vice versa

ggplot(phylores_dom, aes(x=Human, y=phyloP, by=HumanDom, color=HumanDom)) + geom_point(alpha=.3) + geom_smooth(method="lm") +  scale_color_brewer(palette = "Dark2") + labs(title="Human Dominance and Usage")

Version Author Date
f180064 brimittleman 2020-03-24 
ggplot(phylores_dom, aes(x=Chimp, y=phyloP, by=ChimpDom, color=ChimpDom)) + geom_point(alpha=.3) + geom_smooth(method="lm") +  scale_color_brewer(palette = "Dark2")+ labs(title="Chimp Dominance and Usage")

Version Author Date
f180064 brimittleman 2020-03-24

This is not the best way to visualize this I should group it by deciles.

phylores_domDec= phylores_dom %>% mutate(decileHuman = ntile(Human, 10),decileChimp = ntile(Chimp, 10))

phylores_domDec$decileHuman=as.factor(phylores_domDec$decileHuman)
phylores_domDec$decileChimp=as.factor(phylores_domDec$decileChimp)


ggplot(phylores_domDec, aes(x=decileHuman, y=phyloP, by=HumanDom, fill=HumanDom)) + geom_boxplot() +  scale_fill_brewer(palette = "Dark2") + labs(title="Human Dominance and Usage")

Version Author Date
f180064 brimittleman 2020-03-24 
ggplot(phylores_domDec, aes(x=decileChimp, y=phyloP, by=ChimpDom, fill=ChimpDom)) + geom_boxplot() +  scale_fill_brewer(palette = "Dark2")+ labs(title="Chimp Dominance and Usage")

Version Author Date
f180064 brimittleman 2020-03-24

This is decile but I want up to the decile.

I can do this with cumulative ifelse statements first. I will start with up to certain cutoffs.

phylores_dom_cumulative= phylores_dom %>% mutate(CutoffHuman=ifelse(Human <=.1, "ten", ifelse(Human<=.2, "twenty", ifelse(Human <=.3, "thirty", ifelse(Human <=.4 , "fourty", ifelse(Human <=.5, "fifty", ifelse(Human<=.6, "sixty", ifelse(Human <=.7, "seventy", ifelse(Human <=.8, "eighty", ifelse(Human <=.9, "neinty", "onehundred"))))))))))%>% mutate(CutoffChimp=ifelse(Chimp <=.1, "ten", ifelse(Chimp<=.2, "twenty", ifelse(Chimp <=.3, "thirty", ifelse(Chimp <=.4 , "fourty", ifelse(Chimp <=.5, "fifty", ifelse(Chimp<=.6, "sixty", ifelse(Chimp <=.7, "seventy", ifelse(Chimp <=.8, "eighty", ifelse(Chimp <=.9, "neinty", "onehundred"))))))))))

phylores_dom_cumulative$CutoffHuman=factor(phylores_dom_cumulative$CutoffHuman, levels=c("ten", "twenty", "thirty", "fourty","fifty", "sixty", "seventy", "eighty", "neinty", "onehundred"))
phylores_dom_cumulative$CutoffChimp=factor(phylores_dom_cumulative$CutoffChimp,levels=c("ten", "twenty", "thirty", "fourty","fifty", "sixty", "seventy", "eighty", "neinty", "onehundred"))
ggplot(phylores_dom_cumulative, aes(x=CutoffHuman, y=phyloP, by=HumanDom, fill=HumanDom)) + geom_boxplot() +  scale_fill_brewer(palette = "Dark2", name="PAS Dominant \nin Human") + labs(title="Conservation of Human Dominant PAS \n by different usage cutoffs",x="Human Usage Cutoff, PAS with usage up to X%")

Version Author Date
f180064 brimittleman 2020-03-24 
ggplot(phylores_dom_cumulative, aes(x=CutoffChimp, y=phyloP, by=ChimpDom, fill=ChimpDom)) + geom_boxplot() +  scale_fill_brewer(palette = "Dark2", name="PAS Dominant \nin Chimp") + labs(title="Conservation of Chimp Dominant PAS \n by different usage cutoffs",x="Chimp Usage Cutoff, PAS with usage up to X%")

Version Author Date
f180064 brimittleman 2020-03-24

When they have the same dominant:

I need to do the no ties version for this.

Chimp_Dom= ChimpPASwMean %>%
  group_by(gene) %>%
  arrange(desc(Chimp)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,Chimp) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)

Human_Dom= HumanPASwMean %>%
  group_by(gene) %>%
  arrange(desc(Human)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,Human) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS)

BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")
SameDom= BothDom %>% filter(ChimpPAS==HumanPAS)
DiffDom=BothDom %>% filter(ChimpPAS!=HumanPAS)

same domiant:

phylores_dom_samediff=phylores_dom %>% mutate(SameDom=ifelse(PAS %in%SameDom$ChimpPAS, "Yes", "No"), DiffDomChimp=ifelse(PAS %in% DiffDom$ChimpPAS, "Yes", "No" ), DiffDomHuman=ifelse(PAS %in% DiffDom$HumanPAS, "Yes","No"))
ggplot(phylores_dom_samediff, aes(x=SameDom, y=phyloP, fill=SameDom)) + geom_boxplot() +stat_compare_means()+ scale_fill_brewer(palette = "Dark2")+ labs(x="Is PAS the same dominant in both species?", title="Same dominant are more conserved") + theme(legend.position = "none")

Version Author Date
7d4c862 brimittleman 2020-03-25

Plot 3 plot- same dominant, diff dom in chimp, diff dom human

BothDomPhy= BothDom %>%ungroup() %>%  select(HumanPAS) %>% rename("PAS"=HumanPAS) %>% inner_join(phylores_dom, by="PAS") %>% mutate(set="SameDominant")
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
DiffHumanPhy=DiffDom %>%ungroup() %>%  select(HumanPAS) %>% rename("PAS"=HumanPAS) %>% inner_join(phylores_dom, by="PAS") %>% mutate(set="DiffDominant_Human")
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
DiffChimpPhy=DiffDom %>%ungroup() %>%  select(ChimpPAS) %>% rename("PAS"=ChimpPAS) %>% inner_join(phylores_dom, by="PAS") %>% mutate(set="DiffDominant_Chimp")
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
NotDom=phylores_dom %>% filter(ChimpDom=="No", HumanDom=="Yes") %>% mutate(set="NotDominant")


AlldomrePhy=BothDomPhy %>% bind_rows(DiffHumanPhy) %>% bind_rows(DiffChimpPhy) %>% bind_rows(NotDom)

ggplot(AlldomrePhy, aes(x=set, y=phyloP, fill=set)) + geom_boxplot()  +stat_compare_means() + scale_fill_brewer(palette ="Dark2") + labs(x="", title="PAS that are dominant in both species are most conserved") +theme(legend.position = "none")

Version Author Date
7d4c862 brimittleman 2020-03-25
f180064 brimittleman 2020-03-24 
AlldomrePhy %>% group_by(set) %>% summarise(meanPhylop=round(mean(phyloP),2))
# A tibble: 4 x 2
  set                meanPhylop
  <chr>                   <dbl>
1 DiffDominant_Chimp       1.16
2 DiffDominant_Human       0.65
3 NotDominant              0.66
4 SameDominant             0.9

This is interesting. the chimp dominant.


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] ggpubr_0.2      magrittr_1.5    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 workflowr_1.6.0

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