Last updated: 2019-12-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.Rmd

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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 0edf719 brimittleman 2019-12-17 update pantro6
html f4bcae9 brimittleman 2019-10-15 Build site.
Rmd 25a8b1e brimittleman 2019-10-15 fix name bug add number PAS analysis 
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)

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

    smiths
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started

Both 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/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt", stringsAsFactors = F, header = T)

I want to look at the number of PAS at 5% in each gene by human and chimp.

PAS_sm=PAS %>% dplyr::select(gene, Chimp, Human) 
PAS_m= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.05) %>% 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$DiffPAS)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-8.00000 -1.00000  0.00000  0.04132  1.00000  8.00000 
#more human
nrow(PAS_spread[PAS_spread$DiffPAS>0,])
[1] 5783
#more chimp
nrow(PAS_spread[PAS_spread$DiffPAS<0,])
[1] 5082
#same
nrow(PAS_spread[PAS_spread$DiffPAS==0,])
[1] 9175
#all
nrow(PAS_spread)
[1] 20040
prop.test(x=c(5783, 5082), n=c(20040,20040), alternative ="greater")

    2-sample test for equality of proportions with continuity
    correction

data:  c(5783, 5082) out of c(20040, 20040)
X-squared = 61.871, df = 1, p-value = 1.833e-15
alternative hypothesis: greater
95 percent confidence interval:
 0.02763141 1.00000000
sample estimates:
   prop 1    prop 2 
0.2885729 0.2535928 
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 5% Human vs Chimp", y="Genes", x="N Human PAS - N Chimp PAS")

Version Author Date
f4bcae9 brimittleman 2019-10-15

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 ,alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  HumanNPAS$nPAS and ChimpNPAS$nPAS
W = 190470000, p-value = 2.033e-08
alternative hypothesis: true location shift is greater than 0
ggplot(PAS_m,aes(x=nPAS, by=species, fill=species)) + geom_density(stat="count",alpha=.5) +  scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS >= 5%", x="Number of PAS",y="Genes")

Version Author Date
f4bcae9 brimittleman 2019-10-15

Total fraction

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) %>% dplyr::select(contains("_T"))


HumanMeanTotal=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(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) %>% dplyr::select(contains("_T"))

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

Filter 5% and group by gene

BothMean_total=HumanMeanTotal %>% inner_join(ChimpMeanTotal,by=c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene")) %>% filter(Chimp >=.05 | Human >=0.05) 

BothMean_total_M=melt(BothMean_total, id.vars = c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene"), value.name = "Total_Usage", variable.name = "Species" )

BothMean_total_gene=BothMean_total_M %>% filter(Total_Usage>=0.05) %>% group_by(Species, gene) %>% summarise(nPASTotal=n()) 


#pos = more human, neg = more chimp 
PAS_Total_spread=BothMean_total_gene %>% spread(Species, nPASTotal, fill=0) %>% mutate(TotalDiffPAS=Human-Chimp)
ggplot(PAS_Total_spread, aes(x=TotalDiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_Total_spread$TotalDiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp \n Total Fraction", y="Genes", x="N Human PAS - N Chimp PAS")

Version Author Date
f4bcae9 brimittleman 2019-10-15 
ggplot(BothMean_total_gene,aes(x=nPASTotal, by=Species, fill=Species)) + geom_density(stat="count",alpha=.5) +  scale_fill_brewer(palette = "Dark2")  + labs(title="Distribution for number of PAS >= 5%\n Total Fraction", x="Number of PAS",y="Genes")

Version Author Date
f4bcae9 brimittleman 2019-10-15

Nuclear fraction

HumanUsageNuclear=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH) %>% dplyr::select(contains("_N"))


HumanMeanNuclear=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsageNuclear))) 
ChimpUsageNuclear=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC) %>% dplyr::select(contains("_N"))

ChimpMeanNuclear=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsageNuclear)))

Filter 5% and group by gene

BothMean_nuclear=HumanMeanNuclear %>% inner_join(ChimpMeanNuclear,by=c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene")) %>% filter(Chimp >=.05 | Human >=0.05) 

BothMean_nuclear_M=melt(BothMean_nuclear, id.vars = c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene"), value.name = "Nuclear_Usage", variable.name = "Species" )



BothMean_nuclear_gene=BothMean_nuclear_M %>% filter(Nuclear_Usage>=0.05) %>% group_by(Species, gene) %>% summarise(nPASNuclear=n()) 


#pos = more human, neg = more chimp 
PAS_Nuclear_spread=BothMean_nuclear_gene %>% spread(Species, nPASNuclear, fill=0) %>% mutate(NuclearDiffPAS=Human-Chimp)
ggplot(PAS_Nuclear_spread, aes(x=NuclearDiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_Nuclear_spread$NuclearDiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp \n Nuclear Fraction", y="Genes", x="N Human PAS - N Chimp PAS")

Version Author Date
f4bcae9 brimittleman 2019-10-15 
ggplot(BothMean_nuclear_gene,aes(x=nPASNuclear, by=Species, fill=Species)) + geom_density(stat="count",alpha=.5) +  scale_fill_brewer(palette = "Dark2")  + labs(title="Distribution for number of PAS >= 5%\n Nuclear Fraction", x="Number of PAS",y="Genes")

Version Author Date
f4bcae9 brimittleman 2019-10-15

compare total and nuclear

Compare total and nuclear.

mean(PAS_Total_spread$TotalDiffPAS)
[1] -0.02378543
mean(PAS_Nuclear_spread$NuclearDiffPAS)
[1] 0.05957276
t.test(PAS_Total_spread$TotalDiffPAS, PAS_Nuclear_spread$NuclearDiffPAS,alternative = "greater") 

    Welch Two Sample t-test

data:  PAS_Total_spread$TotalDiffPAS and PAS_Nuclear_spread$NuclearDiffPAS
t = -5.8039, df = 39509, p-value = 1
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
 -0.1069829        Inf
sample estimates:
  mean of x   mean of y 
-0.02378543  0.05957276 

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_1.5.0 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