Last updated: 2020-04-17

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Knit directory: Comparative_APA/analysis/

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I changed the way I look at Dominance. I am looking at the difference between the top used and next used PAS. I want to look at the overall distribution of this metric. This will help when I look on same vs different dominant PAS as well. I will create a python script that writes out the top PAS the gene and how far the next PAS usage is away.

The code will be adopted from the FindDomXCutoff.py.

#test
python GetTopminus2Usage.py  ../data/DomDefGreaterX/TestFile_ZSWIM7.txt  ../data/DomDefGreaterX/TestDomAll_ZSWIM7.txt Human

#run

python GetTopminus2Usage.py ../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt ../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt Human

python GetTopminus2Usage.py ../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt ../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt Chimp

Again I want the one 1 PAS genes:

MetaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
MetaCol=colnames(MetaPAS)
Human1PASGene= MetaPAS %>% filter(Human>0) %>% group_by(gene) %>% summarise(nPAS=n()) %>% filter(nPAS==1)
Chimp1PASGene= MetaPAS %>% filter(Chimp>0) %>% group_by(gene) %>% summarise(nPAS=n()) %>% filter(nPAS==1)

MetaPAS_human1= MetaPAS %>% filter(gene %in% Human1PASGene$gene, Human >0) %>% mutate(Set="Human1")
MetaPAS_chimp1= MetaPAS %>% filter(gene %in% Chimp1PASGene$gene, Chimp >0)  %>% mutate(Set="Chimp1")

MetaPAS_human1 %>% anti_join(MetaPAS_chimp1,by="gene") %>% nrow()
[1] 9
MetaPAS_chimp1 %>% anti_join(MetaPAS_human1,by="gene") %>% nrow()
[1] 13
HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")

nrow(BothRes)
[1] 8429
BothRes_g= BothRes %>% select(gene, Chimp_DiffDom, Human_DiffDom) %>% gather("species", "diff", -gene)

I am going to lose the few genes that have 1 PAS in only one species.

ggplot(BothRes_g,aes(x=diff,fill=species, by=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", labels=c("Chimp","Human")) + labs(title="Chimp Dominant PAS are 'more Dominant'", x="Top PAS Usage - Second PAS Usage")

Version Author Date
8f48b3a brimittleman 2020-04-17 
wilcox.test(BothRes$Human_DiffDom, BothRes$Chimp_DiffDom)

    Wilcoxon rank sum test with continuity correction

data:  BothRes$Human_DiffDom and BothRes$Chimp_DiffDom
W = 31050000, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0

I can play with cutoffs through this to get a set of genes where I can look at same and different PAS.

BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"))
nrow(BothRes_40)
[1] 2728
BothRes_40_same=BothRes_40 %>% filter(Human_PAS==Chimp_PAS)
nrow(BothRes_40_same)
[1] 2546
BothRes_40_diff=BothRes_40 %>% filter(Human_PAS!=Chimp_PAS)
nrow(BothRes_40_diff)
[1] 182

Most of these share the same dominant but it is interesting to look at the distribution of the dominant PAS usage distributions.

BothRes_g_same40= BothRes_g %>% filter(gene %in% BothRes_40_same$gene)

ggplot(BothRes_g_same40,aes(x=diff,fill=species, by=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", labels=c("Chimp","Human")) + labs(title="Chimp Dominant PAS are 'more Dominant \n Same Dominant PAS at .4 cutoff'", x="Top PAS Usage - Second PAS Usage")

Version Author Date
8f48b3a brimittleman 2020-04-17 
ggplot(BothRes_40, aes(x=Human_DiffDom,Chimp_DiffDom, col= Set ))  + geom_point(alpha=.2)+ geom_abline(slope=1, intercept = 0) + geom_density2d() + scale_color_brewer(palette = "Dark2") + labs(title="Level of dominance for each PAS passing .4 filter")

Version Author Date
8f48b3a brimittleman 2020-04-17

Look at the number of PAS: (just do number in either species for now)

PASnum= MetaPAS %>% select(PAS, gene) %>%  group_by(gene) %>% summarise(nPAS=n())



BothRes_40_num= BothRes_40 %>% inner_join(PASnum,by="gene")


ggplot(BothRes_40_num, aes(x=Human_DiffDom,Chimp_DiffDom, col= Set ))  + geom_point(alpha=.2)+ geom_abline(slope=1, intercept = 0) + geom_density2d() + scale_color_brewer(palette = "Dark2") + labs(title="Level of dominance for each PAS passing .4 filter") + facet_grid(~nPAS)

Version Author Date
8f48b3a brimittleman 2020-04-17 
BothRes_40_num %>% group_by(nPAS, Set) %>% summarise(n=n()) %>% spread(Set, n)
# A tibble: 10 x 3
# Groups:   nPAS [10]
    nPAS Different  Same
   <int>     <int> <int>
 1     2        22   788
 2     3        33   694
 3     4        28   514
 4     5        27   317
 5     6        28   144
 6     7        17    58
 7     8        16    26
 8     9         6     3
 9    10         3     2
10    11         2    NA

The plot is only inforamative up to about 6. Filter this:

BothRes_40_num_filt= BothRes_40_num %>% filter(nPAS<=6)

ggplot(BothRes_40_num_filt, aes(x=Human_DiffDom,Chimp_DiffDom, col= Set ))  + geom_point(alpha=.2)+ geom_abline(slope=1, intercept = 0) + geom_density2d() + scale_color_brewer(palette = "Dark2") + labs(title="Level of dominance for each PAS passing .4 filter") + facet_grid(~nPAS)

Version Author Date
8f48b3a brimittleman 2020-04-17

Incorporate usage

metausage= MetaPAS %>% select(PAS, Human, Chimp)
BothRes_g_same40= BothRes %>% select(Human_PAS, gene, Human_DiffDom, Chimp_DiffDom) %>% rename(PAS=Human_PAS) %>% gather("Species", "Diff", -gene, -PAS) %>% filter(gene %in% BothRes_40_same$gene) %>% inner_join(metausage, by="PAS")

Plot the usage correlation

ggplot(BothRes_g_same40, aes(x=Human, y=Chimp, col=Diff)) + geom_point(alpha=.3)+ facet_grid(~Species)

Version Author Date
8f48b3a brimittleman 2020-04-17 
BothRes_g_same40s= BothRes_g_same40 %>% spread(Species, Diff) %>% gather("species", "usage", -PAS,-Chimp_DiffDom, -Human_DiffDom, -gene)

ggplot(BothRes_g_same40s, aes(x=Human_DiffDom,y=Chimp_DiffDom, col=usage)) + geom_point() + facet_grid(~species)

Version Author Date
8f48b3a brimittleman 2020-04-17

There is more of a usage effect in the Human usage.

Write out the results to use for downstream analysis:

mkdir ../data/DomStructure_4/
write.table(BothRes_40, "../data/DomStructure_4/InclusiveDominantPASat4.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    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         utf8_1.1.4        
[10] rlang_0.4.0        later_0.7.5        pillar_1.3.1      
[13] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[16] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[19] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[22] rvest_0.3.2        evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       fansi_0.4.0       
[28] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[31] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[34] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[37] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[40] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    MASS_7.3-51.1      xml2_1.2.0        
[49] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[52] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[55] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1