Last updated: 2020-04-03

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/SpliceSiteStrength.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dAPAandapaQTL_DF.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/upsetter_DF.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 8269a9a brimittleman 2020-04-03 update post sno
html 8cb8b0a brimittleman 2020-04-01 Build site.
Rmd 7447f2d brimittleman 2020-04-01 add norm dom and add filter comp plots
html 2d84fb1 brimittleman 2020-01-23 Build site.
Rmd 908f02d brimittleman 2020-01-23 add compare filter and write out for top SS 
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.6.0
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()

Prefilter:

pre=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt",header = T, stringsAsFactors = F) %>% filter(loc != "008559")

nrow(pre)
[1] 77148
prebygene=pre %>% group_by(gene) %>% summarise(n=n())
nrow(prebygene)
[1] 19704
ggplot(pre,aes(x=loc,fill=loc) ) + geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+ labs(y="Number of PAS", title="Pre filter location of PAS",x="")

Version Author Date
2d84fb1 brimittleman 2020-01-23 
nrow(prebygene)
[1] 19704
ggplot(prebygene, aes(x=n))+ geom_bar() + labs(x="Number of PAS per gene", title="Number of PAS per gene before filter")

Version Author Date
2d84fb1 brimittleman 2020-01-23

Post:

post=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T, stringsAsFactors = F)

nrow(post)
[1] 46494
postbygene=post %>% group_by(gene) %>% summarise(n=n())
nrow(postbygene)
[1] 9717
ggplot(post,aes(x=loc,fill=loc) ) + geom_bar(stat="count")+ scale_fill_brewer(palette = "Dark2") + labs(y="Number of PAS", title="Post filter location of PAS", x="")

Version Author Date
2d84fb1 brimittleman 2020-01-23 
ggplot(postbygene, aes(x=n))+ geom_bar() + labs(x="Number of PAS per gene", title="Number of PAS per gene after filter")

Version Author Date
2d84fb1 brimittleman 2020-01-23

Look at the usage distribution:

post_loc=post %>% select(PAS,loc,Chimp,Human) %>% gather("species", "MeanUsage",-PAS, -loc)

ggplot(post_loc, aes(x=loc, y=MeanUsage, by=species, fill=species)) + geom_boxplot() + stat_compare_means(label = 'p.signif') + scale_fill_brewer(palette = "Dark2") + labs(y="Mean PAS Usage", x="", title="Difference in mean usage for PAS\n 5% Usage and Expression filter")

Version Author Date
8cb8b0a brimittleman 2020-04-01

Cummulative distribution plot:

ggplot(post_loc, aes(x=MeanUsage,col=species)) + stat_ecdf() +labs(x="Mean Usage", y="CDF", title="Cumulative distribution for filtered PAS") + scale_color_brewer(palette = "Dark2")

Version Author Date
8cb8b0a brimittleman 2020-04-01 
wilcox.test(post$Human, post$Chimp, paired = F, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  post$Human and post$Chimp
W = 1133500000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0

Is it a problem with counts:

Indivdual Usage:

post_chrom=post%>% mutate(chrom=paste(chr,start,end,gene, sep=":")) %>% select(chrom,PAS)

human=read.table("../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc", stringsAsFactors = F, header = T) %>% rownames_to_column(var="chrom") %>% inner_join(post_chrom, by="chrom")
human_meanCount=human %>% gather("ind","count", -PAS,-chrom) %>% group_by(PAS) %>% summarise(MeanCountHuman=mean(count), VarCountHuman=var(count))

chimp=read.table("../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc", stringsAsFactors = F, header = T)%>% rownames_to_column(var="chrom")%>%  inner_join(post_chrom, by="chrom")
chimp_meanCount=chimp %>% gather("ind","count", -PAS,-chrom) %>% group_by(PAS) %>% summarise(MeanCountChimp=mean(count), VarCountChimp=var(count))

BothMeanVar=human_meanCount %>% inner_join(chimp_meanCount, by="PAS")

BothMean= BothMeanVar %>% select(-contains("Var")) %>% gather("Species", "Mean", -PAS)

BothVar= BothMeanVar %>% select(-contains("Mean")) %>% gather("Species", "Var", -PAS)
ggplot(BothMean, aes(x=log10(Mean+1), by=Species, color=Species))+ stat_ecdf()+ scale_color_brewer(palette = "Dark2") + labs(title="log10(Mean Count) for filtered PAS",y="CDF")

Version Author Date
8cb8b0a brimittleman 2020-04-01 
wilcox.test(BothMeanVar$MeanCountHuman, BothMeanVar$MeanCountChimp, paired = F, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  BothMeanVar$MeanCountHuman and BothMeanVar$MeanCountChimp
W = 836210000, p-value = 0.003147
alternative hypothesis: true location shift is greater than 0
ggplot(BothVar, aes(x=log10(Var+1), by=Species, color=Species))+ stat_ecdf()+ scale_color_brewer(palette = "Dark2") + labs(title="log10(Mean Variance) for filtered PAS",y="CDF")

Version Author Date
8cb8b0a brimittleman 2020-04-01 
wilcox.test(BothMeanVar$VarCountHuman, BothMeanVar$VarCountChimp, paired = F, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  BothMeanVar$VarCountHuman and BothMeanVar$VarCountChimp
W = 870680000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0

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_1.6.0 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