Last updated: 2020-04-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/speciesSpecific.Rmd

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Rmd 637094a brimittleman 2020-04-06 look at total dominance 
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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The dominant PAS when you look at the nuclear fraction show an interesting result. I want to refilter the PAS with 5% in total fraction then look to see if the genes with the different dominant PAS have the same pattern.

I am starting the lifted and annotated PAS before I filter for 5%. I will do this filter then the same gene expression filter.

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)
#seperate total and nuclear  
HumanUsage_total= HumanUsage %>% select(contains("_T"))
HumanUsage_nuclear= HumanUsage %>% select(contains("_N"))

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

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)
ChimpUsage_total= ChimpUsage %>% select(contains("_T"))
ChimpUsage_nuclear= ChimpUsage %>% select(contains("_N"))


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

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

Both together:

BothMeanboth=ChimpMean %>% full_join(HumanMean, by=c("chr","start","end","gene"   ,"strand", "loc", "disc","PAS" ))

BothMean =BothMeanboth %>% select(-Chimp, -Human)

BothMean_5= BothMean %>% filter(ChimpTot >=0.05 | HumanTot >= 0.05)

Filter passing gene

PassingGenes=read.table("../data/OverlapBenchmark/genesPassingCuttoff.txt", header = T, stringsAsFactors = F, col.names = c("gene"))


BothMean_5_genefilt= BothMean_5 %>% semi_join(PassingGenes,by="gene")

Get the dominant PAS:

Chimp_Dom= BothMean_5_genefilt %>%
  group_by(gene) %>%
  top_n(1,ChimpTot) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  select(gene,loc,PAS,ChimpTot) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS, Chimp=ChimpTot)

Human_Dom= BothMean_5_genefilt %>% 
  group_by(gene) %>% 
  top_n(1, HumanTot) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  dplyr::select(gene,loc,PAS,HumanTot) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS,Human=HumanTot)

BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")
SameDom=BothDom %>% filter(ChimpPAS==HumanPAS) 
ggplot(SameDom, aes(x=HumanLoc))+ geom_histogram(stat="count") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species \n Total Usage ")
Warning: Ignoring unknown parameters: binwidth, bins, pad

DiffDom=BothDom %>% filter(ChimpPAS!=HumanPAS) 
DiffDom_g= DiffDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(DiffDom_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") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad

Proportion of genes with same and different:
of 9458

nrow(SameDom)
[1] 7154
nrow(SameDom)/nrow(BothDom)
[1] 0.7563967
nrow(DiffDom)
[1] 2304
nrow(DiffDom)/nrow(BothDom)
[1] 0.2436033

numbers in nuclear (of 9479):

[1] 6558 [1] 0.6918451 [1] 2921 [1] 0.3081549

Difference of proportion test for different dominance:

prop.test(x=c(2304,2921), n=c(9458,9479))

    2-sample test for equality of proportions with continuity
    correction

data:  c(2304, 2921) out of c(9458, 9479)
X-squared = 98.419, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.07735517 -0.05174797
sample estimates:
   prop 1    prop 2 
0.2436033 0.3081549

Difference of proportion test for same dominance:

prop.test(x=c(7154,6558), n=c(9458,9479))

    2-sample test for equality of proportions with continuity
    correction

data:  c(7154, 6558) out of c(9458, 9479)
X-squared = 98.419, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.05174797 0.07735517
sample estimates:
   prop 1    prop 2 
0.7563967 0.6918451

Find the genes with the same dominant in total but different in nuclear.

NuclearDiffDom=read.table("../data/DominantPAS_DF/Nuclear_DiffDom.txt",header = T,stringsAsFactors = F)
NuclearSameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T,stringsAsFactors = F)

I want genes in same dom for total and different for nuclear. First make sure it is in the tested set.

NuclearDiffDom_sameTot= NuclearDiffDom %>% semi_join(BothDom,by="gene") %>% anti_join(SameDom, by="gene")

nrow(NuclearDiffDom_sameTot)
[1] 1678

Plot location of these:

NuclearDiffDom_sameTot_g= NuclearDiffDom_sameTot %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(NuclearDiffDom_sameTot_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 in nuclear only") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad

Compare this to those that are different in both:

NuclearDiffDom_diffTot= NuclearDiffDom %>% semi_join(BothDom,by="gene") %>% semi_join(SameDom, by="gene")
nrow(NuclearDiffDom_diffTot)
[1] 1133
NuclearDiffDom_diffTot_g= NuclearDiffDom_diffTot %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(NuclearDiffDom_diffTot_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 in both fractions") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad

Seems pretty similar.


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   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