Last updated: 2019-10-04

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd d6e07c1 brimittleman 2019-10-04 subset 5 perc pas and pheno
html 2a20597 brimittleman 2019-10-04 Build site.
Rmd d7ac788 brimittleman 2019-10-04 subset 5 perc pas and pheno
html fafaf61 brimittleman 2019-10-04 Build site.
Rmd ca39a2a brimittleman 2019-10-04 finish annoatation and quantification
html 4f7e30d brimittleman 2019-10-03 Build site.
Rmd 8033ddb brimittleman 2019-10-03 get to pheno to find prob
html e0ac227 brimittleman 2019-10-03 Build site.
Rmd e3f0cdf brimittleman 2019-10-03 add annotation analysis 
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(reshape2)

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

    smiths

I will annotate the PAS that passed the liftover. These PAS are in ../data/cleanPeaks_lifted

Map PAS to these annoations:

mkdir ../data/cleanPeaks_anno
bedtools map -a ../data/cleanPeaks_lifted/AllPAS_postLift.sort.bed -b  /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation.sort.bed -c 4 -S -o distinct > ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed

Chose annotation if PAS in multiple and create bed. I will have to lift this back to chimp then make saf files for both to do the feature count

python chooseAnno2Bed.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed

Lift this so I have it with chimp coordinates:

sbatch LiftOrthoPAS2chimp.sh

bed 2 SAF

python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.SAF

python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED_chimpLoc.bed  ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED_chimpLoc.SAF

Use feature counts to quantify:

mkdir  ../Human/data/CleanLiftedPeaks_FC/
mkdir ../Chimp/data/CleanLiftedPeaks_FC/

sbatch quantLiftedPAS.sh

Fix header:

python fixFChead_bothfrac.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc


python fixFChead_bothfrac.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc

#make file ID

python makeFileID.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp ../Chimp/data/CleanLiftedPeaks_FC/ChimpFileID.txt

python makeFileID.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt

Make phenotypes from these:

mkdir ../Human/phenotype/
mkdir ../Chimp/phenotype/
python makePheno.py  ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt ../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt

python makePheno.py  ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc ../Chimp/data/CleanLiftedPeaks_FC/ChimpFileID.txt ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt

Convert these to numeric:

Rscript pheno2countonly.R -I ../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt -O ../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt

Rscript pheno2countonly.R -I ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt -O ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt


python convertNumeric.py ../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt ../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt

python convertNumeric.py ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt

Plot usages to see if 5% is a good cutoff for this analysis as well.

HumanAnno=read.table("../Human/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/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)

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

HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))
ChimpAnno=read.table("../Chimp/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="-")
IndH=colnames(ChimpAnno)[9:ncol(ChimpAnno)]

ChimpUsage=read.table("../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndH)

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

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

Mean both:

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

BothMeanM=melt(BothMean,id.vars =c("chr","start","end","gene"   ,"strand", "loc", "disc","PAS" ),variable.name = "Species", value.name = "MeanUsage" ) %>% filter(loc !="008559")

Plot:

ggplot(BothMeanM, aes(x=loc, y=MeanUsage,by=Species,fill=Species)) + geom_boxplot()  + scale_fill_brewer(palette = "Dark2")

Version Author Date
2a20597 brimittleman 2019-10-04 
ggplot(BothMeanM, aes(x=MeanUsage,by=Species,col=Species)) + stat_ecdf(geom = "point", alpha=.25)  + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative Distribution plot for PAS Usage", x="Mean Usage- both fractions", y="F(Mean Usage)")

Version Author Date
2a20597 brimittleman 2019-10-04

This is good. Globally the usages are similar across species.

ggplot(BothMeanM, aes(x=log10(MeanUsage + .00001),by=Species,fill=Species)) + geom_histogram(alpha=.5, bins=30,position="dodge")  + scale_fill_brewer(palette = "Dark2") + geom_vline(xintercept = log10(0.05))

Version Author Date
2a20597 brimittleman 2019-10-04

Looks like 5% in either species is a good set.

Filter to PAS with 5% usage

BothMean mean in human or chimp > 0.5

BothMean_5= BothMean %>% filter(Chimp >=0.05 | Human >= 0.05)  
BothMean_5M=melt(BothMean_5,id.vars =c("chr","start","end","gene"   ,"strand", "loc", "disc","PAS" ),variable.name = "Species", value.name = "MeanUsage" ) %>% filter(loc !="008559")

ggplot(BothMean_5M, aes(x=loc, y=MeanUsage,by=Species,fill=Species)) + geom_boxplot()  + scale_fill_brewer(palette = "Dark2")

Version Author Date
2a20597 brimittleman 2019-10-04 
ggplot(BothMean_5M, aes(x=MeanUsage,by=Species,col=Species)) + stat_ecdf(geom = "point", alpha=.25)  + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative Distribution plot for PAS Usage at 5%", x="Mean Usage- both fractions", y="F(Mean Usage)")

Version Author Date
2a20597 brimittleman 2019-10-04 
ggplot(BothMean_5M, aes(x=MeanUsage,by=Species,fill=Species)) + geom_histogram(alpha=.5, bins=30, position = "dodge")  + scale_fill_brewer(palette = "Dark2")

Version Author Date
2a20597 brimittleman 2019-10-04

Write this out this way and as a bed files with human and chimp scores:

mkdir ../data/Peaks_5perc
mkdir ../data/Pheno_5perc
BothMean_5_out=BothMean_5 %>% select(PAS,disc, Chimp, Human)
write.table(BothMean_5_out, "../data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", row.names = F, col.names = T, quote = F)
#write bed with human coord for igv
BothMean_5_bed=BothMean_5 %>% select(chr, start, end, PAS, Human, strand)
write.table(BothMean_5_bed, "../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed", row.names = F, col.names = T, quote = F)

I can filter the phenotypes and PAS with this list.

ggplot(BothMean_5_out, aes(x=disc, fill=disc))+  geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")

Version Author Date
2a20597 brimittleman 2019-10-04 
BothMean_5_outmean= BothMean_5_out %>% mutate(meanUsage=(Human+Chimp)/2)
ggplot(BothMean_5M, aes(x=disc, by= Species, fill=Species, y=MeanUsage)) + geom_boxplot() + scale_y_log10()+ scale_fill_brewer(palette = "Dark2")
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Removed 1531 rows containing non-finite values (stat_boxplot).

ChimpUsage_anno_5perc= ChimpUsage_anno %>% filter(PAS %in% BothMean_5$PAS)

write.table(ChimpUsage_anno_5perc, "../data/Pheno_5perc/Chimp_Pheno_5perc.txt", row.names = F, col.names = T, quote = F)

HumaUsage_anno_5perc= HumanUsage_anno %>% filter(PAS %in% BothMean_5$PAS)

write.table(HumaUsage_anno_5perc, "../data/Pheno_5perc/Human_Pheno_5perc.txt", row.names = F, col.names = T, quote = F)

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] reshape2_1.4.3  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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] pillar_1.3.1       compiler_3.5.1     git2r_0.25.2      
 [7] plyr_1.8.4         workflowr_1.4.0    tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] backports_1.1.2    scales_1.0.0       htmltools_0.3.6   
[43] rvest_0.3.2        assertthat_0.2.0   colorspace_1.3-2  
[46] labeling_0.3       stringi_1.2.4      lazyeval_0.2.1    
[49] munsell_0.5.0      broom_0.5.1        crayon_1.3.4