Last updated: 2020-04-09

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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.

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File Version Author Date Message
Rmd fc93eb9 brimittleman 2020-04-09 red for no 18499

It looks like NA18499 nuclear fraction data is of poor quality. I will run some of the analysis without this individual.

library(reshape2)
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(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
Nuclear=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T,stringsAsFactors = F) %>% select(PAS, Chimp, Human) %>% rename(ChimpNuclear=Chimp, HumanNuclear=Human)
Total=read.table("../data/TotalFractionPAS/TotalFraction_PASmeta.txt",header = T, stringsAsFactors = F)%>% select(PAS, ChimpTot, HumanTot) %>% rename(ChimpTotal=ChimpTot, HumanTotal=HumanTot)


BothFrac= Nuclear %>% inner_join(Total,by="PAS")
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)
HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage )) %>% select(-chr,-start,-end, -gene, -strand, -loc, -disc,-NA18499_N, -NA18499_T) %>% filter(PAS %in% Nuclear$PAS)


HumanUsageGather= HumanUsage_anno%>% gather("Line", "Usage", -PAS) %>% mutate(fraction=ifelse(grepl("_N", Line),"NuclearHuman", "TotalHuman"),indiv=substr(Line,1,nchar(Line)-2))
HumanUsageGatherMeans=HumanUsageGather %>% group_by(PAS, fraction) %>% summarise(meanUsage=mean(Usage)) %>% spread( fraction, meanUsage)

chimp

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_anno=as.data.frame(cbind(ChimpAnno[,1:8],ChimpUsage ))%>% select(-chr,-start,-end, -gene, -strand, -loc, -disc ) %>% filter(PAS %in% Nuclear$PAS)

ChimpUsageGather= ChimpUsage_anno%>% gather("Line", "Usage", -PAS) %>% mutate(fraction=ifelse(grepl("_N", Line),"NuclearChimp", "TotalChimp"),indiv=substr(Line,1,nchar(Line)-2))
ChimpUsageGatherMeans=ChimpUsageGather %>% group_by(PAS, fraction) %>% summarise(meanUsage=mean(Usage)) %>% spread( fraction, meanUsage)

Both fractions:

BothMean=ChimpUsageGatherMeans %>% inner_join(HumanUsageGatherMeans,by="PAS")

Look recalculate the mean usage for these.

ggplot(BothMean,aes(x=NuclearHuman, y=TotalHuman)) + geom_point() + geom_smooth(method="lm") + labs(title="Human mean usage correlation between fractions",x= "Nuclear", y="Total")

cor.test(BothMean$NuclearHuman, BothMean$TotalHuman)

    Pearson's product-moment correlation

data:  BothMean$NuclearHuman and BothMean$TotalHuman
t = 573.75, df = 46492, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9349463 0.9371962
sample estimates:
      cor 
0.9360808 
summary(lm(BothMean$NuclearHuman~BothMean$TotalHuman))

Call:
lm(formula = BothMean$NuclearHuman ~ BothMean$TotalHuman)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.46121 -0.03133  0.00037  0.03249  0.52478 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.018499   0.000411   45.02   <2e-16 ***
BothMean$TotalHuman 0.835818   0.001457  573.75   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.07106 on 46492 degrees of freedom
Multiple R-squared:  0.8762,    Adjusted R-squared:  0.8762 
F-statistic: 3.292e+05 on 1 and 46492 DF,  p-value: < 2.2e-16
ggplot(BothMean,aes(x=NuclearChimp, y=TotalChimp)) + geom_point() + geom_smooth(method="lm") + labs(title="Chimp mean usage correlation between fractions",x= "Nuclear", y="Total")

cor.test(BothMean$NuclearChimp, BothMean$TotalChimp)

    Pearson's product-moment correlation

data:  BothMean$NuclearChimp and BothMean$TotalChimp
t = 751.89, df = 46492, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9605587 0.9619402
sample estimates:
      cor 
0.9612555 
summary(lm(BothMean$NuclearChimp~BothMean$TotalChimp))

Call:
lm(formula = BothMean$NuclearChimp ~ BothMean$TotalChimp)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.41422 -0.02140  0.00066  0.02535  0.42995 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.010779   0.000337   31.99   <2e-16 ***
BothMean$TotalChimp 0.876831   0.001166  751.89   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.05828 on 46492 degrees of freedom
Multiple R-squared:  0.924, Adjusted R-squared:  0.924 
F-statistic: 5.653e+05 on 1 and 46492 DF,  p-value: < 2.2e-16
NA18358= cor(ChimpUsage_anno$NA18358_N, ChimpUsage_anno$NA18358_T)
NA3622= cor(ChimpUsage_anno$NA3622_N, ChimpUsage_anno$NA3622_T)
NA3659= cor(ChimpUsage_anno$NA3659_N, ChimpUsage_anno$NA3659_T)
NA4973= cor(ChimpUsage_anno$NA4973_N, ChimpUsage_anno$NA4973_T)
NApt30= cor(ChimpUsage_anno$NApt30_N, ChimpUsage_anno$NApt30_T)
NApt91= cor(ChimpUsage_anno$NApt91_N, ChimpUsage_anno$NApt91_T)

NA18498= cor(HumanUsage_anno$NA18498_N, HumanUsage_anno$NA18498_T)
NA18502= cor(HumanUsage_anno$NA18502_N, HumanUsage_anno$NA18502_T)
NA18504= cor(HumanUsage_anno$NA18504_N, HumanUsage_anno$NA18504_T)
NA18510= cor(HumanUsage_anno$NA18510_N, HumanUsage_anno$NA18510_T)
NA18523= cor(HumanUsage_anno$NA18523_N, HumanUsage_anno$NA18523_T)


AllTvN=as.data.frame(cbind(Species=c(rep("Chimp",6), rep("Human",5)), Val=c(NA18358,NA3622,NA3659, NA4973,NApt30, NApt91,NA18498,NA18502,NA18504,  NA18510,NA18523), Ind=c('NA18358','NA3622','NA3659', 'NA4973','NApt30', 'NApt91','NA18498','NA18502','NA18504',  'NA18510','NA18523')))

AllTvN$Val=as.numeric(as.character(AllTvN$Val))
ggplot(AllTvN, aes(x=Ind, y=Val, fill=Species)) + geom_bar(stat = "identity") + scale_fill_brewer(palette = "Dark2")+ theme(axis.text.x = element_text(angle = 90)) + labs(x="",y="Correlation between Total and Nuclear Usage", title="Correlation between total and nuclear usage \nfor PAS over 5% in both fractions: Remove 18499")

allPheno=ChimpUsage_anno %>%inner_join(HumanUsage_anno,by="PAS")


allPheno_nuc= allPheno %>% select(contains("_N"))
allPhenoNuc_matrix=as.matrix(allPheno_nuc)

Count_corrNuc= round(cor(allPhenoNuc_matrix),2)

Count_corrNuc_melt=melt(Count_corrNuc)


ggplot(data = Count_corrNuc_melt, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +theme(axis.text.x = element_text(angle = 90))+ scale_fill_distiller(palette = "Blues", direction=1)+ geom_text(aes(label = value))

This is more balanced.

Redo dominance:

allPAS= read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F) %>% inner_join(BothMean,by="PAS")
Chimp_Dom= allPAS %>%
  group_by(gene) %>%
  top_n(1,NuclearChimp) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  dplyr::select(gene,loc,PAS,NuclearChimp) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)

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


#merge

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 : Remove 18499")
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: Remove 18499") + scale_fill_brewer(palette = "Dark2")+theme(legend.position='bottom')
Warning: Ignoring unknown parameters: binwidth, bins, pad

PASpregene=allPAS %>% group_by(gene) %>% summarize(nPAS=n())
PASmore2=PASpregene %>% filter(nPAS>1)
SameDom=SameDom %>% mutate(DiffinDom=NuclearChimp-NuclearHuman) %>% filter(gene %in% PASmore2$gene)
ggplot(SameDom,aes(x=DiffinDom))+ geom_histogram(bins=100)

PASMeta_humanDom_diff=allPAS %>% filter(PAS %in% DiffDom$HumanPAS) %>% mutate(Diff=NuclearHuman-NuclearChimp)

PASMeta_ChimpDom_diff=allPAS %>% filter(PAS%in%DiffDom$ChimpPAS) %>% mutate(Diff=NuclearHuman-NuclearChimp)


ggplot(PASMeta_humanDom_diff,aes(x=Diff))+geom_histogram(bins=100, fill="#D95F02",alpha=.5) + labs(title="Human Usage - Chimp Usage \n Colored by dominant : Remove 18499") + geom_histogram(data=PASMeta_ChimpDom_diff,aes(x=Diff), bins = 100, fill="#1B9E77", alpha=.5) + geom_vline(xintercept = mean(PASMeta_ChimpDom_diff$Diff), col="#1B9E77")+ geom_vline(xintercept = mean(PASMeta_humanDom_diff$Diff), col="#D95F02") + geom_histogram(bins=100, data=SameDom, aes(x=DiffinDom), alpha=.3)+ geom_vline(xintercept = mean(SameDom$DiffinDom))

DiffDomfromH= DiffDom %>% select(NuclearHuman, ChimpPAS) %>% rename(PAS=ChimpPAS, humanDom=NuclearHuman) %>% inner_join(allPAS, by=c("gene","PAS"))%>% mutate(Diff=humanDom-NuclearHuman,Dom="Human") %>% select(gene,Dom, Diff) %>% inner_join(PASpregene, by="gene")%>% filter(nPAS<6)
Adding missing grouping variables: `gene`
DiffDomfromC= DiffDom %>% select(NuclearChimp, HumanPAS) %>% rename(PAS=HumanPAS, ChimpDom=NuclearChimp) %>% inner_join(allPAS,  by=c("gene","PAS"))%>% mutate(Diff=ChimpDom-NuclearChimp,Dom="Chimp")%>% select(gene,Dom, Diff) %>% inner_join(PASpregene, by="gene") %>% filter(nPAS<6)
Adding missing grouping variables: `gene`
ggplot(DiffDomfromH,aes(x=Diff))+ geom_histogram(bins=50, fill="#D95F02",alpha=.5) + geom_histogram(data=DiffDomfromC, bins=50,fill="#1B9E77", alpha=.5 ) + facet_grid(~nPAS) + labs(x="Difference in Mean usage", title="Dominant PAS in species - same species value for \nthe 'dominant' in other species : Remove 18499")

Ok this looks good. Seems like a lot of this


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 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 reshape2_1.4.3 

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] backports_1.1.2    scales_1.0.0       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