Last updated: 2020-04-08

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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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 2f5d4d5 brimittleman 2020-04-08 change number and add 3; utr
html a7536f9 brimittleman 2020-04-03 Build site.
Rmd bf79d5e brimittleman 2020-04-03 update catalot
html 5525b39 brimittleman 2020-01-21 Build site.
Rmd 2e66af9 brimittleman 2020-01-21 add ss and PAS num DF

library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started

Nuclear fraction

In this analysis I will look at thenumber of PAS per species at a gene level. I am only including PAS in chr1-22. These results use mean usage accross fraction.

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

I am looking at PAS at .05 or greater in either species.

PAS_sm=PAS %>% dplyr::select(gene, Chimp, Human) 

PAS_m= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.1) %>% group_by(species, gene) %>% summarise(nPAS=n())

#pos = more human, neg = more chimp 
PAS_spread=PAS_m %>% spread(species, nPAS, fill=0) %>% mutate(DiffPAS=Human-Chimp)
summary(PAS_spread$DiffPAS)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-4.00000  0.00000  0.00000  0.05956  1.00000  5.00000 
#more human
nrow(PAS_spread[PAS_spread$DiffPAS>0,])
[1] 2599
#more chimp
nrow(PAS_spread[PAS_spread$DiffPAS<0,])
[1] 2210
#same
nrow(PAS_spread[PAS_spread$DiffPAS==0,])
[1] 4744
#all
nrow(PAS_spread)
[1] 9553
ggplot(PAS_spread, aes(x=DiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_spread$DiffPAS),col="red") + labs(title="Difference in number of PAS at 10% Human vs Chimp", y="Genes", x="N Human PAS - N Chimp PAS")

Version Author Date
a7536f9 brimittleman 2020-04-03
5525b39 brimittleman 2020-01-21

Plot distribution of N pas by species:

Wilcoxan test to see if there is a difference in this distribution.

ChimpNPAS=PAS_m %>% filter(species=="Chimp")
HumanNPAS=PAS_m %>% filter(species=="Human")


wilcox.test(HumanNPAS$nPAS,ChimpNPAS$nPAS )

    Wilcoxon rank sum test with continuity correction

data:  HumanNPAS$nPAS and ChimpNPAS$nPAS
W = 46201000, p-value = 1.34e-15
alternative hypothesis: true location shift is not equal to 0
PAS_m$nPAS=as.factor(PAS_m$nPAS)
ggplot(PAS_m,aes(x=nPAS, by=species, fill=species)) + geom_histogram(stat="count",position = "dodge") +  scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS at 10% ", x="Number of PAS",y="Genes")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
a7536f9 brimittleman 2020-04-03
5525b39 brimittleman 2020-01-21

Look at number without a filter:

PAS_m5= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.05) %>% group_by(species, gene) %>% summarise(nPAS=n())
PAS_m5$nPAS=as.factor(PAS_m5$nPAS)
ggplot(PAS_m5,aes(x=nPAS, by=species, fill=species)) + geom_histogram(stat="count",position = "dodge") +  scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS at 5% Nuclear Fraction ", x="Number of PAS",y="Genes")
Warning: Ignoring unknown parameters: binwidth, bins, pad


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] workflowr_1.6.0 reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

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        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