Plots to show conservations

Last updated: 2020-05-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/infoContent.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    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	0f623d6	brimittleman	2020-05-06	update with writing
html	53a7570	brimittleman	2020-04-22	Build site.
Rmd	b4e617e	brimittleman	2020-04-22	add color and prop dom, add decay

Plots to show the conservation I have been seeing:

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

library("scales")
library(limma)
library(qvalue)
library("RColorBrewer")
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() ──
✖ readr::col_factor() masks scales::col_factor()
✖ purrr::discard()    masks scales::discard()
✖ tidyr::extract()    masks magrittr::extract()
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::lag()        masks stats::lag()
✖ purrr::set_names()  masks magrittr::set_names()

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggpubr':

    get_legend

The following object is masked from 'package:ggplot2':

    ggsave

library(gplots)


Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

metaDataN=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F) %>% filter(Fraction=="Nuclear")
PASmeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F)

Plot usage correlation:

ggplot(PASmeta,aes(x=Human, y=Chimp)) + geom_point(alpha=.3)+ stat_cor(col="blue", label.x = .15, label.y=.9) + geom_smooth(method="lm") + labs(title="Usage correlation for All PAS") + geom_abline(intercept = 0, slope =1, col="red")

Version	Author	Date
53a7570	brimittleman	2020-04-22

Correlation:

cor.test(PASmeta$Chimp, PASmeta$Human)


    Pearson's product-moment correlation

data:  PASmeta$Chimp and PASmeta$Human
t = 441.32, df = 44430, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9006174 0.9040719
sample estimates:
      cor 
0.9023592

Correlation is on top of the 1v1 line.

ggplot(PASmeta,aes(y=Human, x=loc, col=loc)) + geom_boxplot(alpha=.3)  +scale_color_brewer(palette = "Dark2")

diffiso=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T)

PASmetaG=PASmeta %>% select(gene, Chimp, Human, PAS,loc) %>%gather("species","usage", -PAS, -gene, -loc)

Correlation between individuals:

HumanAnno=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndH=colnames(HumanAnno)[9:ncol(HumanAnno)]

HumanUsage=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)

HumanMean=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage)))

HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))


ChimpAnno=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndC=colnames(ChimpAnno)[9:ncol(ChimpAnno)]

ChimpUsage=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC)

ChimpMean=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage)))

ChimpUsage_anno=as.data.frame(cbind(ChimpAnno[,1:8],ChimpUsage ))

allPheno=ChimpUsage_anno %>% full_join(HumanUsage_anno,by=c("chr", "start", "end", "gene","strand","loc", "disc", "PAS")) %>% filter(PAS %in% PASmeta$PAS) %>% dplyr::select(-chr, -start,-end, -gene,-strand,-loc,-disc, -PAS)


allPheno_nuc= allPheno %>% select(contains("_N"))

allPheno_nuc_matrix=as.matrix(allPheno_nuc)

Correlation:

Count_corr= round(cor(allPheno_nuc_matrix),2)

Count_corr_melt=melt(Count_corr)

ggplot(data = Count_corr_melt, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +theme(axis.text.x = element_text(angle = 90))+ scale_fill_distiller(palette = "PuRd", direction=1)

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] gplots_3.0.1       cowplot_0.9.4      reshape2_1.4.3    
 [4] forcats_0.3.0      stringr_1.3.1      dplyr_0.8.0.1     
 [7] purrr_0.3.2        readr_1.3.1        tidyr_0.8.3       
[10] tibble_2.1.1       tidyverse_1.2.1    RColorBrewer_1.1-2
[13] qvalue_2.14.0      limma_3.38.2       scales_1.0.0      
[16] ggpubr_0.2         magrittr_1.5       ggplot2_3.1.1     
[19] workflowr_1.6.0   

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6       lubridate_1.7.4    lattice_0.20-38   
 [4] gtools_3.8.1       assertthat_0.2.0   rprojroot_1.3-2   
 [7] digest_0.6.18      R6_2.3.0           cellranger_1.1.0  
[10] plyr_1.8.4         backports_1.1.2    evaluate_0.12     
[13] httr_1.3.1         pillar_1.3.1       rlang_0.4.0       
[16] lazyeval_0.2.1     readxl_1.1.0       rstudioapi_0.10   
[19] gdata_2.18.0       whisker_0.3-2      rmarkdown_1.10    
[22] labeling_0.3       splines_3.5.1      munsell_0.5.0     
[25] broom_0.5.1        compiler_3.5.1     httpuv_1.4.5      
[28] modelr_0.1.2       pkgconfig_2.0.2    htmltools_0.3.6   
[31] tidyselect_0.2.5   crayon_1.3.4       withr_2.1.2       
[34] later_0.7.5        bitops_1.0-6       grid_3.5.1        
[37] nlme_3.1-137       jsonlite_1.6       gtable_0.2.0      
[40] git2r_0.26.1       KernSmooth_2.23-15 cli_1.1.0         
[43] stringi_1.2.4      fs_1.3.1           promises_1.0.1    
[46] xml2_1.2.0         generics_0.0.2     tools_3.5.1       
[49] glue_1.3.0         hms_0.4.2          yaml_2.2.0        
[52] colorspace_1.3-2   caTools_1.17.1.1   rvest_0.3.2       
[55] knitr_1.20         haven_1.1.2

Plots to show conservations

Briana Mittleman

4/22/2020