Last updated: 2020-06-01

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 4e6fd67 brimittleman 2020-06-01 add unlifted analysis

Test if the non-lifted sites would effect the analysis. I will assign the unlifted human sites to genes with the lifted sites and filter for those that would pass the cutoff.

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(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':

    get_legend
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    ggsave
mkdir ../data/UnliftedSites

First concatenate the unlifted human sites with the final lifted sties. This is important because I need to compare the final sites with those not lifted. I cannot just use the original sites.

final sites: ../data/cleanPeaks_lifted/AllPAS_postLift.bed

Unlifted primary:
../data/primaryLift/human_APApeaks_primarylift2Chimp_UNLIFTED.bed ../data/reverseLift/human_APApeaks_primarylift2Human_rev2Human_UNLIFTED.bed

PrimUnlifted=read.table("../data/primaryLift/human_APApeaks_primarylift2Chimp_UNLIFTED.bed", header = F, col.names = c("chr", 'start','end','PAS','score', 'strand'),stringsAsFactors = F)
SecUnlift=read.table("../data/reverseLift/human_APApeaks_primarylift2Human_rev2Human_UNLIFTED.bed", header = F, col.names = c("chr", 'start','end','PAS','score', 'strand'),stringsAsFactors = F)

AllUnlift=PrimUnlifted %>% bind_rows(SecUnlift) %>% mutate(name=paste("unlif", PAS, sep=":")) %>% select(chr, start,end, name,score, strand)


Passing=read.table("../data/cleanPeaks_lifted/AllPAS_postLift.bed",header = F, col.names = c("chr",'start','end','name','score','strand'),stringsAsFactors = F) 


LiftandNot=AllUnlift %>% bind_rows(Passing)

First compare scores: (is is even before usage)

AllUnliftSet=AllUnlift %>% mutate(set='Unlifted')
PassingSet= Passing %>% mutate(set='lifted')

LiftandNotSet= AllUnliftSet %>% bind_rows(PassingSet)

LiftandNotSet %>% group_by(set) %>% summarise(mean=mean(score))
# A tibble: 2 x 2
  set       mean
  <chr>    <dbl>
1 lifted    36.5
2 Unlifted  23.2
ggplot(LiftandNotSet, aes(y=log10(score),x=set)) + geom_boxplot() + stat_compare_means()

LiftandNotSet %>% group_by(set) %>% summarise(n())
# A tibble: 2 x 2
  set       `n()`
  <chr>     <int>
1 lifted   445944
2 Unlifted  10077

Write these out so I can assign to genes:

write.table(LiftandNot, "../data/UnliftedSites/PAS_liftedandNonLiftedHuman.bed", col.names = F, row.names = F, quote = F, sep = "\t")

Sort:

sort -k1,1 -k2,2n ../data/UnliftedSites/PAS_liftedandNonLiftedHuman.bed > ../data/UnliftedSites/PAS_liftedandNonLiftedHuman.sort.bed

bedtools map -a ../data/UnliftedSites/PAS_liftedandNonLiftedHuman.sort.bed -b  ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation_noSNO.Resort.bed -c 4 -S -o distinct > ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnno.bed 

python chooseAnno2Bed.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnno.bed  ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.bed

Look at how many of the unlifted are lost due to no gene annotation:

GeneLoc=read.table("../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.bed", header=F, col.names=c("chr",'start','end','id', 'score', 'strand'),stringsAsFactors = F) %>% separate(id, into=c("set", "PAS", "chr2",'start2','end2','strand2','geneloc' ),sep=":") %>% separate(geneloc, into=c("gene", "loc"),sep="_") %>% select(set, PAS, gene, loc)
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [51056,
51057, 51058, 97083].
GeneLoc %>% group_by(set) %>% summarise(n())
# A tibble: 4 x 2
  set    `n()`
  <chr>  <int>
1 Both  165194
2 Chimp  99550
3 Human 130240
4 unlif   6264

3813 of the 10K lost due to no overlap.

make SAF for FC

python bed2SAF_gen.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.bed ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.SAF

featureCounts -O -a ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed.SAF -F SAF -o  ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human ../Human/data/sort_clean/*.bam -s 1

python fixFChead_bothfrac.py  ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human  ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_fixed.fc 



python makePheno.py  ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_fixed.fc  ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno.txt

Rscript pheno2countonly.R -I ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno.txt -O ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnly.txt

python convertNumeric.py ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnly.txt ../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnlyNumeric.txt

HumanAnno=read.table("../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>%  separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% 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("../data/UnliftedSites/PAS_liftedandNonLiftedHuman_LocAnnoParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)

HumanUsage_nuclear= HumanUsage %>% select(contains("_N"))

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

HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))

Filter 5% then i can filter the low expressed genes:

HumanMean_5= HumanMean %>% filter(Human >=0.05)

HumanMean_5 %>% group_by(disc) %>% summarise(n())
# A tibble: 4 x 2
  disc  `n()`
  <chr> <int>
1 Both  38111
2 Chimp  6992
3 Human 14766
4 unlif  1069

Now there are only 1K unlifted. Are any of these in the set of genes that pass the expression cutoff.

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

HumanMean_5_passing=HumanMean_5 %>% filter(gene %in% PassingGenes$genes)


HumanMean_5_passing%>% group_by(disc) %>% summarise(n())
# A tibble: 4 x 2
  disc  `n()`
  <chr> <int>
1 Both  29253
2 Chimp  2486
3 Human  4473
4 unlif   386

Only 386 PAS.

HumanMean_5_passing_unlif= HumanMean_5_passing %>% filter(disc=="unlif")

HumanMean_5_passing_unlif %>% group_by(gene) %>% summarise(n()) %>% nrow()
[1] 353

353 genes.

ggplot(HumanMean_5_passing_unlif,aes(x=Human))+ stat_ecdf()

Usage by loc:

ulifloc=ggplot(HumanMean_5_passing_unlif,aes(x=loc))+ geom_bar(stat="count") +geom_text(stat='count', aes(label=..count..), vjust=1, col="white") + labs(x="genic loc", y="Number of PAS", title="Location of Unlifted PAS")

Plot usage:

HumanMean_5_passing= HumanMean_5_passing %>% mutate(Lifted=ifelse(disc=="unlif", "No", "Yes"))

uselift=ggplot(HumanMean_5_passing, aes(y=Human,x=loc, fill=Lifted))+ geom_boxplot() + scale_fill_brewer(palette = "Set1") + labs(y="Human Usage",x="", title="PAS usage by gene location" )

useliftloc=ggplot(HumanMean_5_passing, aes(y=Human,x=Lifted, fill=Lifted))+ geom_boxplot() + scale_fill_brewer(palette = "Set1") + labs(y="Human Usage",x="", title="PAS usage" )

Unlifted origial: 10077 After Anno: 6264 5%: 1069 passing genes:386

liftStats=c(3813, 5195,683, 386)
val=c("Not Annotated","Low Usage", "Not in Gene filter","PassedFilters")



statDF=as.data.frame(cbind(val, liftStats)) 
statDF$liftStats=as.numeric(as.character(statDF$liftStats))
statDF$val=as.character(statDF$val)

statDF=statDF%>% mutate(sum=sum(liftStats), Prop=liftStats/sum)


propplot=ggplot(statDF,aes(by=val, y=Prop, x="", fill=val)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_brewer(palette = "Set1",name="") +labs(title="Of the 10,077 Unlifted PAS") + theme(legend.position = "bottom")
propplot

plot_grid(propplot,ulifloc,uselift, useliftloc)


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   ggpubr_0.2      magrittr_1.5    forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     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         utf8_1.1.4         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[16] readxl_1.1.0       plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       workflowr_1.6.0    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        lazyeval_0.2.1    
[43] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[46] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[55] compiler_3.5.1