Conservation and dominance structure
Briana Mittleman
3/24/2020
Last updated: 2020-04-10
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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| html | c2a0778 | brimittleman | 2020-04-06 | Build site. |
| Rmd | 9676d72 | brimittleman | 2020-04-06 | updated anno |
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| Rmd | 34a5358 | brimittleman | 2020-03-25 | add same diff dom |
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| 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 startedlibrary(tidyverse)── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.1 ✔ purrr 0.3.2
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✖ 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_namesThe following object is masked from 'package:tidyr':
extractLoad 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] 210Human_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")
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")
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")
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")
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") 
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") 
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%") 
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%") 
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")
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 vectorDiffHumanPhy=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 vectorDiffChimpPhy=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 vectorNotDom=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")
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 0.64
3 NotDominant 0.64
4 SameDominant 0.92This 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