Annotate and assess peak usage

Last updated: 2020-03-16

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
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    Modified:   analysis/upsetter_DF.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.

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
html	61894c9	brimittleman	2019-12-17	Build site.
Rmd	a89d80b	brimittleman	2019-12-17	wflow_publish(“analysis/annotatePAS.Rmd”)
html	f4bcae9	brimittleman	2019-10-15	Build site.
Rmd	25a8b1e	brimittleman	2019-10-15	fix name bug add number PAS analysis
html	cf0de17	brimittleman	2019-10-09	Build site.
Rmd	190a655	brimittleman	2019-10-09	small changes 9.9
html	62b5285	brimittleman	2019-10-07	Build site.
Rmd	8466418	brimittleman	2019-10-07	add human and chimp tvN res
html	ad4ea95	brimittleman	2019-10-04	Build site.
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/data/phenotype/
mkdir ../Chimp/data/phenotype/
python makePheno.py  ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt ../Human/data/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/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt

Convert these to numeric:

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

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


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

python convertNumeric.py ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt ../Chimp/data/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/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)

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/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)

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
61894c9	brimittleman	2019-12-17
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
61894c9	brimittleman	2019-12-17
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
61894c9	brimittleman	2019-12-17
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
61894c9	brimittleman	2019-12-17
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
61894c9	brimittleman	2019-12-17
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
61894c9	brimittleman	2019-12-17
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 %>% dplyr::select(PAS,disc, gene, loc,chr, start, end,Chimp, Human)
write.table(BothMean_5_out, "../data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", row.names = F, col.names = T, quote = F)

BothMean_5_out_noUN=BothMean_5 %>% dplyr::select(PAS,disc, gene, loc,chr, start, end,Chimp, Human) %>% filter(!grepl("Un",chr))

write.table(BothMean_5_out_noUN, "../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt", row.names = F, col.names = T, quote = F)
#write bed with human coord for igv
BothMean_5_bed=BothMean_5 %>% dplyr::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
61894c9	brimittleman	2019-12-17
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 6678 rows containing non-finite values (stat_boxplot).

Version	Author	Date
61894c9	brimittleman	2019-12-17
ad4ea95	brimittleman	2019-10-04

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] 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] workflowr_1.6.0    cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1

Annotate and assess peak usage

Briana Mittleman

10/3/2019