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Last updated: 2020-01-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.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	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 ──

✔ 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()

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

Signal Site Distributions

Briana Mittleman

1/9/2020