Last updated: 2020-05-27
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Knit directory: Comparative_APA/analysis/
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library(workflowr)
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library(tidyverse)
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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 |
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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)
Look at this metric for all cutoffs:
BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")
BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10)
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)
BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)
I want the number and proportion of same vs different at every cutoff:
MetaPAS_genes= MetaPAS %>% group_by(gene) %>% summarise(n())
BothResAll_g= BothResAll %>% group_by(cut, Set) %>% summarise(nEach=n()) %>% ungroup() %>% group_by(cut) %>% mutate(nDom=sum(nEach), PropSame=nEach/nDom) %>% filter(Set=="Same") %>%ungroup() %>% mutate(NTested=nrow(MetaPAS_genes), PropDom=nDom/NTested)
BothResAll_g$cut=as.factor(BothResAll_g$cut)
Plot:
numberDom=ggplot(BothResAll_g,aes(x=cut,y=PropSame,fill=cut)) + geom_bar(stat="identity") +geom_text(aes(label=nDom), position=position_dodge(width=0.9), vjust=2) + scale_fill_brewer(palette = "RdYlBu")+theme(legend.position = "none") + labs(title="Most genes with a domiant PAS share the same dominant PAS", y="Proportion of Genes in Set", x="Domianance Cutoff")
numberDom
propDom=ggplot(BothResAll_g,aes(x=cut,y=PropDom,fill=cut)) + geom_bar(stat="identity") +geom_text(aes(label=nDom), position=position_dodge(width=0.9), vjust=2) + scale_fill_brewer(palette = "RdYlBu")+theme(legend.position = "none") + labs(title="Proportion of Tested Genes with a Dominant PAS", y="Proportion of Tested Genes", x="Domianance Cutoff")
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
plot_grid(propDom, numberDom)
Version | Author | Date |
---|---|---|
214c39d | brimittleman | 2020-05-04 |
pdf("../output/PropSamesdom.pdf", height=8, width=8)
numberDom
dev.off()
png
2
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] cowplot_0.9.4 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 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_1.0.4.6 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