Last updated: 2020-03-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    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.

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Rmd e85defb brimittleman 2020-03-17 add res from diff mis prime filters 
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(cowplot)
Loading required package: ggplot2

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

    ggsave
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter()   masks stats::filter()
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I reran my analysis to identify and quantified PAS. I will uses this analysis to evaluate the results and too see if the distributions for dominant pAS have changed.

Start with the least stringent.

Original:42,319 60%= 36,899 50%= 29829 40%= 22446

PAS_original=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)

PAS_6filt=read.table("../../Misprime6/data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", header = T,stringsAsFactors = F)

PAS_5filt=read.table("../../Misprime5/data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", header = T,stringsAsFactors = F)

PAS_4filt=read.table("../../Misprime4/data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", header = T,stringsAsFactors = F)


original= ggplot(PAS_original,aes(x=loc, fill=loc)) + geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+ labs(x="", y="PAS count", title="Original filter Location \n (n=42,319)")+ theme(legend.position = "none")

loc6= ggplot(PAS_6filt,aes(x=loc, fill=loc)) + geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+ labs(x="", y="PAS count", title="60% T filter Location \n (n=36,899)")+ theme(legend.position = "none")

loc5= ggplot(PAS_5filt,aes(x=loc, fill=loc)) + geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+ labs(x="", y="PAS count", title="50% T filter Location \n (n=29,829)")+ theme(legend.position = "none")

loc4= ggplot(PAS_4filt,aes(x=loc, fill=loc)) + geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+ labs(x="", y="PAS count", title="40% T filter Location \n (n=22,446)")+ theme(legend.position = "none")


plot_grid(original,loc6,loc5,loc4, nrow = 1)

Domminance structure:

60% filter.

ChimpPASwMean_6 =PAS_6filt %>% dplyr::select(-Human)
HumanPASwMean_6 =PAS_6filt %>% dplyr::select(-Chimp)
Chimp_Dom_6= ChimpPASwMean_6 %>%
  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_6= HumanPASwMean_6 %>%
  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_6= Chimp_Dom_6 %>% inner_join(Human_Dom_6,by="gene")

SameDom_6= BothDom_6 %>% filter(ChimpPAS==HumanPAS)

nrow(SameDom_6)
[1] 7792
SameDom_6_g= SameDom_6 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
sameplot6=ggplot(SameDom_6_g, aes(x=Location, by=Species, fill=Species))+ geom_bar(stat="count",position = "Dodge") + labs(x="Location", y="Number of Genes", title="Matching Dominant PAS for genes \n 60% (n=7792)")+scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
DiffDom_6=BothDom_6 %>% filter(ChimpPAS!=HumanPAS)
nrow(DiffDom_6)
[1] 1636
DiffDom_6_g= DiffDom_6 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)

diffplot6=ggplot(DiffDom_6_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS \n 60% (n=1636)") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad

50% filter.

ChimpPASwMean_5 =PAS_5filt %>% dplyr::select(-Human)
HumanPASwMean_5 =PAS_5filt %>% dplyr::select(-Chimp)
Chimp_Dom_5= ChimpPASwMean_5 %>%
  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_5= HumanPASwMean_5 %>%
  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_5= Chimp_Dom_5 %>% inner_join(Human_Dom_5,by="gene")

SameDom_5= BothDom_5 %>% filter(ChimpPAS==HumanPAS)

nrow(SameDom_5)
[1] 7531
SameDom_5_g= SameDom_5 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
sameplot5=ggplot(SameDom_5_g, aes(x=Location, by=Species, fill=Species))+ geom_bar(stat="count",position = "Dodge") + labs(x="Location", y="Number of Genes", title="Matching Dominant PAS for genes\n 50% (n=7531)")+scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
DiffDom_5=BothDom_5 %>% filter(ChimpPAS!=HumanPAS)
nrow(DiffDom_5)
[1] 1352
DiffDom_5_g= DiffDom_5 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)

diffplot5=ggplot(DiffDom_5_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS \n 50% (n=1352)") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad

40% filter

ChimpPASwMean_4 =PAS_4filt %>% dplyr::select(-Human)
HumanPASwMean_4 =PAS_4filt %>% dplyr::select(-Chimp)
Chimp_Dom_4= ChimpPASwMean_4 %>%
  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_4= HumanPASwMean_4 %>%
  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_4= Chimp_Dom_4 %>% inner_join(Human_Dom_4,by="gene")

SameDom_4= BothDom_4 %>% filter(ChimpPAS==HumanPAS)

nrow(SameDom_4)
[1] 6949
SameDom_4_g= SameDom_4 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
sameplot4=ggplot(SameDom_4_g, aes(x=Location, by=Species, fill=Species))+ geom_bar(stat="count",position = "Dodge") + labs(x="Location", y="Number of Genes", title="Matching Dominant PAS for genes \n 40% (n=6949)")+scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
DiffDom_4=BothDom_4 %>% filter(ChimpPAS!=HumanPAS)
nrow(DiffDom_4)
[1] 1099
DiffDom_4_g= DiffDom_4 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)

diffplot4=ggplot(DiffDom_4_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS \n 40% (n=1099)") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
plot_grid(sameplot6, sameplot5, sameplot4, nrow=1)

plot_grid(diffplot6,diffplot5, diffplot4,nrow=1)

Different dominant differnet location

DiffDomDiffLoc_6=BothDom_6%>% filter(ChimpPAS!=HumanPAS, ChimpLoc!=HumanLoc)
nrow(DiffDomDiffLoc_6)
[1] 768
DiffDomDiffLoc_6_g= DiffDomDiffLoc_6 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)

diffloc6=ggplot(DiffDomDiffLoc_6_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS (diff loc) \n 60% (n=768)") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
DiffDomDiffLoc_5=BothDom_5%>% filter(ChimpPAS!=HumanPAS, ChimpLoc!=HumanLoc)
nrow(DiffDomDiffLoc_5)
[1] 586
DiffDomDiffLoc_5_g= DiffDomDiffLoc_5 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)

diffloc5=ggplot(DiffDomDiffLoc_5_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS (diff loc) \n 50% (n=586)") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
DiffDomDiffLoc_4=BothDom_4%>% filter(ChimpPAS!=HumanPAS, ChimpLoc!=HumanLoc)
nrow(DiffDomDiffLoc_4)
[1] 490
DiffDomDiffLoc_4_g= DiffDomDiffLoc_4 %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)

diffloc4=ggplot(DiffDomDiffLoc_4_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS (diff loc) \n 40% (n=490)") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad
plot_grid(diffloc6,diffloc5,diffloc4,nrow=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] 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] cowplot_0.9.4   ggplot2_3.1.1   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         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        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1