Human Total vs Nuclear

Last updated: 2019-10-07

Checks: 7 0

Knit directory: Comparative_APA/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190902)

The command set.seed(20190902) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 8466418

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    code/chimp_log/
    Ignored:    code/human_log/
    Ignored:    data/metadata_HCpanel.txt.sb-f4823d1e-qihGek/

Untracked files:
    Untracked:  ._.DS_Store
    Untracked:  Chimp/
    Untracked:  Human/
    Untracked:  code/._Config_chimp.yaml
    Untracked:  code/._Config_human.yaml
    Untracked:  code/._LiftOrthoPAS2chimp.sh
    Untracked:  code/._Snakefile
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilePASfilt
    Untracked:  code/._bed215upbed.py
    Untracked:  code/._bed2SAF_gen.py
    Untracked:  code/._buildStarIndex.sh
    Untracked:  code/._cleanbed2saf.py
    Untracked:  code/._cluster.json
    Untracked:  code/._extraSnakefiltpas
    Untracked:  code/._filterPASforMP.py
    Untracked:  code/._filterPostLift.py
    Untracked:  code/._fixUTRexonanno.py
    Untracked:  code/._formathg38Anno.py
    Untracked:  code/._formatpantro6Anno.py
    Untracked:  code/._intersectLiftedPAS.sh
    Untracked:  code/._liftPAS19to38.sh
    Untracked:  code/._makeSamplyGroupsHuman_TvN.py
    Untracked:  code/._maphg19.sh
    Untracked:  code/._maphg19_subjunc.sh
    Untracked:  code/._overlapapaQTLPAS.sh
    Untracked:  code/._prepareCleanLiftedFC_5perc4LC.py
    Untracked:  code/._preparePAS4lift.py
    Untracked:  code/._primaryLift.sh
    Untracked:  code/._recLiftchim2human.sh
    Untracked:  code/._revLiftPAShg38to19.sh
    Untracked:  code/._reverseLift.sh
    Untracked:  code/._runChimpDiffIso.sh
    Untracked:  code/._runHumanDiffIso.sh
    Untracked:  code/._snakemake.batch
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakePASchimp.batch
    Untracked:  code/._snakemakePAShuman.batch
    Untracked:  code/._snakemake_chimp.batch
    Untracked:  code/._snakemake_human.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._snakemakefiltPAS_chimp
    Untracked:  code/._snakemakefiltPAS_chimp.sh
    Untracked:  code/._snakemakefiltPAS_human.sh
    Untracked:  code/._submit-snakemake-chimp.sh
    Untracked:  code/._submit-snakemake-human.sh
    Untracked:  code/._submit-snakemakePAS-chimp.sh
    Untracked:  code/._submit-snakemakePAS-human.sh
    Untracked:  code/._submit-snakemakefiltPAS-chimp.sh
    Untracked:  code/._submit-snakemakefiltPAS-human.sh
    Untracked:  code/.snakemake/
    Untracked:  code/Config_chimp.yaml
    Untracked:  code/Config_human.yaml
    Untracked:  code/LiftOrthoPAS2chimp.sh
    Untracked:  code/LiftorthoPAS.err
    Untracked:  code/LiftorthoPASt.out
    Untracked:  code/Log.out
    Untracked:  code/Rev_liftoverPAShg19to38.err
    Untracked:  code/Rev_liftoverPAShg19to38.out
    Untracked:  code/SAF215upbed_gen.py
    Untracked:  code/Snakefile
    Untracked:  code/SnakefilePAS
    Untracked:  code/SnakefilePASfilt
    Untracked:  code/Upstream10Bases_general.py
    Untracked:  code/apaQTLsnake.err
    Untracked:  code/apaQTLsnake.out
    Untracked:  code/apaQTLsnakePAS.err
    Untracked:  code/apaQTLsnakePAS.out
    Untracked:  code/apaQTLsnakePAShuman.err
    Untracked:  code/bed215upbed.py
    Untracked:  code/bed2SAF_gen.py
    Untracked:  code/bed2saf.py
    Untracked:  code/bg_to_cov.py
    Untracked:  code/buildStarIndex.sh
    Untracked:  code/callPeaksYL.py
    Untracked:  code/chooseAnno2Bed.py
    Untracked:  code/chooseAnno2SAF.py
    Untracked:  code/cleanbed2saf.py
    Untracked:  code/cluster.json
    Untracked:  code/clusterPAS.json
    Untracked:  code/clusterfiltPAS.json
    Untracked:  code/convertNumeric.py
    Untracked:  code/extraSnakefiltpas
    Untracked:  code/filter5perc.R
    Untracked:  code/filter5percPheno.py
    Untracked:  code/filterBamforMP.pysam2_gen.py
    Untracked:  code/filterMissprimingInNuc10_gen.py
    Untracked:  code/filterPASforMP.py
    Untracked:  code/filterPostLift.py
    Untracked:  code/filterSAFforMP_gen.py
    Untracked:  code/filterSortBedbyCleanedBed_gen.R
    Untracked:  code/filterpeaks.py
    Untracked:  code/fixFChead.py
    Untracked:  code/fixFChead_bothfrac.py
    Untracked:  code/fixUTRexonanno.py
    Untracked:  code/formathg38Anno.py
    Untracked:  code/generateStarIndex.err
    Untracked:  code/generateStarIndex.out
    Untracked:  code/intersectAnno.err
    Untracked:  code/intersectAnno.out
    Untracked:  code/intersectLiftedPAS.sh
    Untracked:  code/liftPAS19to38.sh
    Untracked:  code/liftoverPAShg19to38.err
    Untracked:  code/liftoverPAShg19to38.out
    Untracked:  code/log/
    Untracked:  code/make5percPeakbed.py
    Untracked:  code/makeFileID.py
    Untracked:  code/makePheno.py
    Untracked:  code/makeSamplyGroupsChimp_TvN.py
    Untracked:  code/makeSamplyGroupsHuman_TvN.py
    Untracked:  code/maphg19.err
    Untracked:  code/maphg19.out
    Untracked:  code/maphg19.sh
    Untracked:  code/maphg19_sub.err
    Untracked:  code/maphg19_sub.out
    Untracked:  code/maphg19_subjunc.sh
    Untracked:  code/namePeaks.py
    Untracked:  code/overlapPAS.err
    Untracked:  code/overlapPAS.out
    Untracked:  code/overlapapaQTLPAS.sh
    Untracked:  code/peak2PAS.py
    Untracked:  code/pheno2countonly.R
    Untracked:  code/prepareCleanLiftedFC_5perc4LC.py
    Untracked:  code/preparePAS4lift.py
    Untracked:  code/primaryLift.err
    Untracked:  code/primaryLift.out
    Untracked:  code/primaryLift.sh
    Untracked:  code/quantLiftedPAS.err
    Untracked:  code/quantLiftedPAS.out
    Untracked:  code/quantLiftedPAS.sh
    Untracked:  code/recChimpback2Human.err
    Untracked:  code/recChimpback2Human.out
    Untracked:  code/recLiftchim2human.sh
    Untracked:  code/revLift.err
    Untracked:  code/revLift.out
    Untracked:  code/revLiftPAShg38to19.sh
    Untracked:  code/reverseLift.sh
    Untracked:  code/runChimpDiffIso.sh
    Untracked:  code/runHumanDiffIso.sh
    Untracked:  code/run_Chimpleafcutter_ds.err
    Untracked:  code/run_Chimpleafcutter_ds.out
    Untracked:  code/run_Humanleafcutter_ds.err
    Untracked:  code/run_Humanleafcutter_ds.out
    Untracked:  code/slurm-62824013.out
    Untracked:  code/slurm-62825841.out
    Untracked:  code/slurm-62826116.out
    Untracked:  code/snakePASChimp.err
    Untracked:  code/snakePASChimp.out
    Untracked:  code/snakePAShuman.out
    Untracked:  code/snakemake.batch
    Untracked:  code/snakemakePAS.batch
    Untracked:  code/snakemakePASFiltChimp.err
    Untracked:  code/snakemakePASFiltChimp.out
    Untracked:  code/snakemakePASFiltHuman.err
    Untracked:  code/snakemakePASFiltHuman.out
    Untracked:  code/snakemakePASchimp.batch
    Untracked:  code/snakemakePAShuman.batch
    Untracked:  code/snakemake_chimp.batch
    Untracked:  code/snakemake_human.batch
    Untracked:  code/snakemakefiltPAS.batch
    Untracked:  code/snakemakefiltPAS_chimp.sh
    Untracked:  code/snakemakefiltPAS_human.sh
    Untracked:  code/submit-snakemake-chimp.sh
    Untracked:  code/submit-snakemake-human.sh
    Untracked:  code/submit-snakemakePAS-chimp.sh
    Untracked:  code/submit-snakemakePAS-human.sh
    Untracked:  code/submit-snakemakefiltPAS-chimp.sh
    Untracked:  code/submit-snakemakefiltPAS-human.sh
    Untracked:  code/subset_diffisopheno.py
    Untracked:  code/subset_diffisopheno_Chimp_tvN.py
    Untracked:  code/subset_diffisopheno_Huma_tvN.py
    Untracked:  data/._metadata_HCpanel.txt
    Untracked:  data/._metadata_HCpanel.txt.sb-f4823d1e-qihGek
    Untracked:  data/._metadata_HCpanel.xlsx
    Untracked:  data/CompapaQTLpas/
    Untracked:  data/Peaks_5perc/
    Untracked:  data/Pheno_5perc/
    Untracked:  data/chainFiles/
    Untracked:  data/cleanPeaks_anno/
    Untracked:  data/cleanPeaks_byspecies/
    Untracked:  data/cleanPeaks_lifted/
    Untracked:  data/liftover_files/
    Untracked:  data/metadata_HCpanel.txt
    Untracked:  data/metadata_HCpanel.xlsx
    Untracked:  data/primaryLift/
    Untracked:  data/reverseLift/

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	c3aa529	brimittleman	2019-10-07	Build site.
Rmd	da85ad8	brimittleman	2019-10-07	add code to prepare human and chimp TvN

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

In this anaylsis I will complete the Human total vs nuclear analysis. This analysis is similar to the analysis in the apaQTL project

I need the human 5% both fraction feature counts.

Thee feature counts are in ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc. I need to subset these for those in the annotations. Keep the PAS in this file: ../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed. I will do this with a python script.

mkdir ../Human/data/CleanLiftedPeaks4LC/
python prepareCleanLiftedFC_5perc4LC.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed ../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc

This will only look at PAS on chromosomes 1-22 no extra haplotpyes.

mkdir ../Human/data/DiffIso_Human/

python subset_diffisopheno_Huma_tvN.py 1
python subset_diffisopheno_Huma_tvN.py 2
python subset_diffisopheno_Huma_tvN.py 3
python subset_diffisopheno_Huma_tvN.py 4
python subset_diffisopheno_Huma_tvN.py 5
python subset_diffisopheno_Huma_tvN.py 6
python subset_diffisopheno_Huma_tvN.py 7
python subset_diffisopheno_Huma_tvN.py 8
python subset_diffisopheno_Huma_tvN.py 9
python subset_diffisopheno_Huma_tvN.py 10
python subset_diffisopheno_Huma_tvN.py 11
python subset_diffisopheno_Huma_tvN.py 12
python subset_diffisopheno_Huma_tvN.py 13
python subset_diffisopheno_Huma_tvN.py 14
python subset_diffisopheno_Huma_tvN.py 15
python subset_diffisopheno_Huma_tvN.py 16
python subset_diffisopheno_Huma_tvN.py 18
python subset_diffisopheno_Huma_tvN.py 19
python subset_diffisopheno_Huma_tvN.py 20
python subset_diffisopheno_Huma_tvN.py 21
python subset_diffisopheno_Huma_tvN.py 22

Make sample groups:

python makeSamplyGroupsHuman_TvN.py

I will create a script to run the leafcutter differential isoform pipeline. I need to use the the leaftcutter environement because this is python 2.

(module load Anaconda3/5.3.0 source activate leafcutter)

sbatch runHumanDiffIso.sh

Concatinate results:

awk '{if(NR>1)print}' ../Human/data/DiffIso_Human/TN_diff_isoform_chr*.txt_effect_sizes.txt > ../Human/data/DiffIso_Human/TN_diff_isoform_allChrom.txt_effect_sizes.txt


awk '{if(NR>1)print}' ../Human/data/DiffIso_Human/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../Human/data/DiffIso_Human/TN_diff_isoform_allChrom.txt_significance.txt

Significant clusters:

sig=read.table("../Human/data/DiffIso_Human/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") 

sig$p.adjust=as.numeric(as.character(sig$p.adjust))

qqplot(-log10(runif(nrow(sig))), -log10(sig$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Human: Leafcutter differencial isoform analysis between fractions")
abline(0,1)

tested_genes=nrow(sig)
tested_genes

[1] 7467

sig_genes=sig %>% filter(p.adjust<.05)
number_sig_genes=nrow(sig_genes)
number_sig_genes

[1] 2223

Effect Sizes

effectsize=read.table("../Human/data/DiffIso_Human/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltaPAU')) %>% filter(intron != "intron")

effectsize$deltaPAU=as.numeric(as.character(effectsize$deltaPAU))
effectsize$logef=as.numeric(as.character(effectsize$logef))

plot(sort(effectsize$deltaPAU),main="Human: Leafcutter delta PAU", ylab="Delta PAU", xlab="PAS Index")

effectsize_deltaPAU= effectsize %>% filter(abs(deltaPAU) > .2) 
nrow(effectsize_deltaPAU)

[1] 1239

effectSize_highdiffGenes=effectsize_deltaPAU %>% separate(intron, into=c("chrom", "start", "end", "GeneName"), sep=":") %>% dplyr::select(GeneName) %>% unique()

effectsize_deltaPAU_Genes= effectsize_deltaPAU %>% separate(intron, into=c("chrom", "start", "end","gene"),sep=":") %>% group_by(gene) %>% summarise(nperGene=n()) 

nrow(effectsize_deltaPAU_Genes)

[1] 1006

Pull in the PAS to see where these PAS are located.

PAS=read.table("../data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", stringsAsFactors = F, header = T) %>% filter(loc !="008559")

PAS$start=as.character(PAS$start)
PAS$end=as.character(PAS$end)

Increased usage in nuclear:

effectsize_deltaPAU_nuclear= effectsize_deltaPAU %>% filter(deltaPAU < -0.2) %>% separate(intron,into=c("chr", "start", "end", "gene"), sep=":" )%>% inner_join(PAS, by=c("gene", "chr","start", "end"))

ggplot(effectsize_deltaPAU_nuclear,aes(x=loc,fill=loc)) + geom_histogram(stat="count") + scale_fill_brewer(palette = "Dark2") +labs(title="Human: Location of PAS used more in Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Increased usage in total:

effectsize_deltaPAU_total= effectsize_deltaPAU %>% filter(deltaPAU > 0.2)  %>% separate(intron,into=c("chr", "start", "end", "gene"), sep=":" )%>% inner_join(PAS, by=c("gene", "chr","start", "end"))

ggplot(effectsize_deltaPAU_total,aes(x=loc,fill=loc)) + geom_histogram(stat="count") + scale_fill_brewer(palette = "Dark2") +labs(title="Human: Location of PAS used more in Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

This is in line with expectation. Now I can compare this to the number of discovered PAS by plotting the proportion of PAS used more in each fraction by location.

PAS_loc =PAS%>% group_by(loc) %>% summarise(nloc=n())

effectsize_deltaPAU_nuclear_loc=effectsize_deltaPAU_nuclear %>% group_by(loc) %>% summarise(nlocNuclear=n()) 

effectsize_deltaPAU_total_loc=effectsize_deltaPAU_total %>% group_by(loc) %>% summarise(nloctotal=n()) 

effectsize_deltaPAUProp_tot=effectsize_deltaPAU_total_loc %>% inner_join(PAS_loc, by="loc") %>% mutate(Proportion_tot=nloctotal/nloc)

effectsize_deltaPAUProp_nuc=effectsize_deltaPAU_nuclear_loc %>% inner_join(PAS_loc, by="loc") %>% mutate(Proportion_nuc=nlocNuclear/nloc)


effectsize_deltaPAUProp_both= effectsize_deltaPAUProp_nuc %>% inner_join(effectsize_deltaPAUProp_tot, by=c("loc","nloc")) %>% dplyr::rename(Nuclear=Proportion_nuc, Total=Proportion_tot) %>% select(loc, Nuclear, Total) 
effectsize_deltaPAUProp_both_melt= effectsize_deltaPAUProp_both %>% melt(id.vars="loc", variable.name="Fraction", value.name = "Proportion") 
effectsize_deltaPAUProp_both_melt$Fraction=as.character(effectsize_deltaPAUProp_both_melt$Fraction)

Plot this:

ggplot(effectsize_deltaPAUProp_both_melt, aes(x=loc, y=Proportion, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position="dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Human: Proportion of PAS differential used by location",x="") +scale_x_discrete(labels = c('Coding','5kb downstream','Intronic',"3' UTR", "5' UTR"))

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

Human Total vs Nuclear

Briana Mittleman

10/7/2019