Last updated: 2020-01-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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File Version Author Date Message
Rmd b1ad90c brimittleman 2020-01-10 add proportion w/ SS plot by locaiton
html df07f1a brimittleman 2020-01-09 Build site.
Rmd 928a30d brimittleman 2020-01-09 add same and diff dom ss analysis
html ced1966 brimittleman 2020-01-09 Build site.
Rmd 1cdc21d brimittleman 2020-01-09 add initial signal site analysis

In this analysis I will look at the signal site distributions for the human and chimp PAS I have called.

library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────── tidyverse 1.2.1 ──
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✔ 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()

I am looking at 200 base pair regions for each pas. I will look for the sequence in these for now and then refine the search.

I can use bedtools nuc on both to get the sequences for the bed files in ../data/PAS.

mkdir ../data/SignalSites
sbatch PASsequences.sh

The way I did this it flipped the - strand and assayed the correct strand sequence. I will still have to make everything upper case.

Before I use python to find the occurances. I will look at the results because I gave the AATAAA pattern to the nuc program to assay.

First i have to remove the # in each file

humanRawout=read.table("../data/SignalSites/PAS_5perc_either_HumanCoordHummanUsage_nuc.txt", stringsAsFactors = F, header = T) %>% mutate(SS=ifelse(X17_user_patt_count>=1, "yes", "no"))
ChimpRawout=read.table("../data/SignalSites/PAS_5perc_either_ChimpCoordChimpUsage_nuc.txt", stringsAsFactors = F, header = T)%>% mutate(SS=ifelse(X17_user_patt_count>=1, "yes", "no"))

Histogram for the results:

ggplot(humanRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..)) +labs(title="Distribution of AATAAA pattern Human")

Version Author Date
ced1966 brimittleman 2020-01-09
ggplot(ChimpRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..))+labs(title="Distribution of AATAAA pattern Chimps")

Version Author Date
ced1966 brimittleman 2020-01-09

See if yes no segragates with usage:

ggplot(humanRawout,aes(x=SS,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test")

Version Author Date
ced1966 brimittleman 2020-01-09
ggplot(ChimpRawout,aes(x=SS,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test")

Version Author Date
ced1966 brimittleman 2020-01-09

Look at location data and bring this in.

Loc=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F) %>% rename("X4_usercol"=PAS) %>% dplyr::select(X4_usercol,loc)

ChimpRawout_withloc=ChimpRawout %>% inner_join(Loc, by="X4_usercol") %>% filter(loc!="008559")
humanRawout_withloc=humanRawout%>% inner_join(Loc, by="X4_usercol") %>% filter(loc!="008559")
ggplot(humanRawout_withloc,aes(x=loc,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

Version Author Date
ced1966 brimittleman 2020-01-09
ggplot(ChimpRawout_withloc,aes(x=loc,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Version Author Date
ced1966 brimittleman 2020-01-09

I can run the nuc command again for the other doninant signal site I found in the apaQTL analysis (ATTAAA), I can join the results.

sbatch PAS_ATTAAA.sh

remove #

human_ATTAAA=read.table("../data/SignalSites/PAS_5perc_either_HumanCoordHummanUsage_ATTAAA.txt",stringsAsFactors = F,header = T) %>% mutate(SS2=ifelse(X17_user_patt_count>=1, "yes", "no"))

chimp_ATTAAA=read.table("../data/SignalSites/PAS_5perc_either_ChimpCoordChimpUsage_ATTAAA.txt",stringsAsFactors = F,header = T) %>% mutate(SS2=ifelse(X17_user_patt_count>=1, "yes", "no"))


human_both=human_ATTAAA %>% inner_join(humanRawout_withloc, by=c("X1_usercol", "X2_usercol", "X3_usercol", "X4_usercol", "X5_usercol", "X6_usercol", "X7_pct_at", "X8_pct_gc", "X9_num_A", "X10_num_C", "X11_num_G", "X12_num_T", "X13_num_N", "X14_num_oth", "X15_seq_len", "X16_seq")) %>% mutate(anySS=ifelse(SS == "yes" | SS2 =="yes", "yes", "no"))

chimp_both=chimp_ATTAAA %>% inner_join(ChimpRawout_withloc, by=c("X1_usercol", "X2_usercol", "X3_usercol", "X4_usercol", "X5_usercol", "X6_usercol", "X7_pct_at", "X8_pct_gc", "X9_num_A", "X10_num_C", "X11_num_G", "X12_num_T", "X13_num_N", "X14_num_oth", "X15_seq_len", "X16_seq")) %>% mutate(anySS=ifelse(SS == "yes" | SS2 =="yes", "yes", "no"))
ggplot(human_both,aes(x=loc,y=X5_usercol,by=SS2, fill=SS2)) + geom_boxplot() + labs(x="Presence of  ATTAAA", y="Human mean usage",title="Human usage by presense of at least 1  ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

Version Author Date
ced1966 brimittleman 2020-01-09
ggplot(chimp_both,aes(x=loc,y=X5_usercol,by=SS2, fill=SS2)) + geom_boxplot() + labs(x="Presence of ATTAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1  ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Version Author Date
ced1966 brimittleman 2020-01-09
ggplot(human_both,aes(x=loc,y=X5_usercol,by=anySS, fill=anySS)) + geom_boxplot() + labs(x="Presence of AATAAA or ATTAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA or ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

Version Author Date
ced1966 brimittleman 2020-01-09
ggplot(chimp_both,aes(x=loc,y=X5_usercol,by=anySS, fill=anySS)) + geom_boxplot() + labs(x="Presence of AATAAA or ATTAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA or ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Version Author Date
ced1966 brimittleman 2020-01-09

Plot percentage either by loc:

human_both_loc= human_both %>% group_by(loc, anySS) %>% summarise(count=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(count),Human=count/nLoc) %>%ungroup() %>%  dplyr::select(loc, anySS,Human)

chimp_both_loc= chimp_both %>% group_by(loc, anySS) %>% summarise(count=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(count),Chimp=count/nLoc)%>% ungroup() %>% dplyr::select(loc, anySS,Chimp)

bothSpeciesLoc=chimp_both_loc %>% inner_join(human_both_loc,by=c("loc", "anySS")) %>% gather(key="species", value="propSS", -loc, -anySS) %>% filter(anySS=="yes")


ggplot(bothSpeciesLoc, aes(x=loc, fill=species,y=propSS)) + geom_bar(stat="identity",position = "dodge") +  scale_fill_brewer(palette = "Dark2") + labs(title="Presence of top 2 signal sites by location", x="Proportion with signal site", x="location")

Write out information about SS so i can use it for other anaylsis.

human_write=human_both %>% dplyr::select(X4_usercol,SS,SS2,anySS) %>% rename("PAS"=X4_usercol)

write.table(human_write, "../data/SignalSites/HumanPresenceofSS.txt", col.names = T, row.names = F, quote = F)

chimp_write=chimp_both %>% dplyr::select(X4_usercol,SS,SS2,anySS) %>% rename("PAS"=X4_usercol)

write.table(chimp_write,"../data/SignalSites/ChimpPresenceofSS.txt", col.names = T, row.names = F, quote = F)

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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    tidyverse_1.2.1
 [9] workflowr_1.5.0 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  

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         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[49] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[52] compiler_3.5.1