Relationship between RNA, net-seq, h3k27ac

Last updated: 2019-05-16

Checks: 6 0

Knit directory: apaQTL/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.3.0). 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(20190411)

The command set.seed(20190411) 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.

Repository version: e32bef6

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:    output/.DS_Store

Untracked files:
    Untracked:  .Rprofile
    Untracked:  ._.DS_Store
    Untracked:  .gitignore
    Untracked:  _workflowr.yml
    Untracked:  analysis/._PASdescriptiveplots.Rmd
    Untracked:  analysis/._cuttoffPercUsage.Rmd
    Untracked:  analysis/cuttoffPercUsage.Rmd
    Untracked:  apaQTL.Rproj
    Untracked:  code/._FC_UTR.sh
    Untracked:  code/._FC_newPeaks_olddata.sh
    Untracked:  code/._LC_samplegroups.py
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilefiltPAS
    Untracked:  code/._aAPAqtl_nominal39ind.sh
    Untracked:  code/._apaQTLCorrectPvalMakeQQ.R
    Untracked:  code/._apaQTL_Nominal.sh
    Untracked:  code/._apaQTL_permuted.sh
    Untracked:  code/._bed2saf.py
    Untracked:  code/._bothFrac_FC.sh
    Untracked:  code/._callPeaksYL.py
    Untracked:  code/._chooseAnno2SAF.py
    Untracked:  code/._chooseSignalSite
    Untracked:  code/._chooseSignalSite.py
    Untracked:  code/._cluster.json
    Untracked:  code/._clusterPAS.json
    Untracked:  code/._clusterfiltPAS.json
    Untracked:  code/._config.yaml
    Untracked:  code/._config2.yaml
    Untracked:  code/._configOLD.yaml
    Untracked:  code/._convertNumeric.py
    Untracked:  code/._dag.pdf
    Untracked:  code/._extractGenotypes.py
    Untracked:  code/._fc2leafphen.py
    Untracked:  code/._filter5perc.R
    Untracked:  code/._filter5percPheno.py
    Untracked:  code/._filterpeaks.py
    Untracked:  code/._finalPASbed2SAF.py
    Untracked:  code/._fix4su304corr.py
    Untracked:  code/._fix4su604corr.py
    Untracked:  code/._fix4sukalisto.py
    Untracked:  code/._fixFChead.py
    Untracked:  code/._fixFChead_bothfrac.py
    Untracked:  code/._fixH3k12ac.py
    Untracked:  code/._fixRNAhead4corr.py
    Untracked:  code/._fixRNAkalisto.py
    Untracked:  code/._fixhead_netseqfc.py
    Untracked:  code/._make5percPeakbed.py
    Untracked:  code/._makeFileID.py
    Untracked:  code/._makePheno.py
    Untracked:  code/._makeSAFbothfrac5perc.py
    Untracked:  code/._makegencondeTSSfile.py
    Untracked:  code/._mergeAllBam.sh
    Untracked:  code/._mergeByFracBam.sh
    Untracked:  code/._mergePeaks.sh
    Untracked:  code/._namePeaks.py
    Untracked:  code/._netseqFC.sh
    Untracked:  code/._peak2PAS.py
    Untracked:  code/._peakFC.sh
    Untracked:  code/._pheno2countonly.R
    Untracked:  code/._quantassign2parsedpeak.py
    Untracked:  code/._removeloc_pheno.py
    Untracked:  code/._run_leafcutterDiffIso.sh
    Untracked:  code/._selectNominalPvalues.py
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._submit-snakemakePAS.sh
    Untracked:  code/._submit-snakemakefiltPAS.sh
    Untracked:  code/._subset_diffisopheno.py
    Untracked:  code/._utrdms2saf.py
    Untracked:  code/.snakemake/
    Untracked:  code/APAqtl_nominal.err
    Untracked:  code/APAqtl_nominal.out
    Untracked:  code/APAqtl_nominal_39.err
    Untracked:  code/APAqtl_nominal_39.out
    Untracked:  code/APAqtl_permuted.err
    Untracked:  code/APAqtl_permuted.out
    Untracked:  code/BothFracDTPlotGeneRegions.err
    Untracked:  code/BothFracDTPlotGeneRegions.out
    Untracked:  code/DistPAS2Sig.py
    Untracked:  code/FC_UTR.err
    Untracked:  code/FC_UTR.out
    Untracked:  code/FC_UTR.sh
    Untracked:  code/FC_newPAS_olddata.err
    Untracked:  code/FC_newPAS_olddata.out
    Untracked:  code/FC_newPeaks_olddata.sh
    Untracked:  code/LC_samplegroups.py
    Untracked:  code/README.md
    Untracked:  code/Rplots.pdf
    Untracked:  code/Upstream100Bases_general.py
    Untracked:  code/aAPAqtl_nominal39ind.sh
    Untracked:  code/apaQTLCorrectPvalMakeQQ_4pc.R
    Untracked:  code/apaQTL_Nominal_4pc.sh
    Untracked:  code/apaQTL_permuted.4pc.sh
    Untracked:  code/bam2bw.err
    Untracked:  code/bam2bw.out
    Untracked:  code/bothFrac_FC.err
    Untracked:  code/bothFrac_FC.out
    Untracked:  code/bothFrac_FC.sh
    Untracked:  code/dag.pdf
    Untracked:  code/dagPAS.pdf
    Untracked:  code/dagfiltPAS.pdf
    Untracked:  code/extractGenotypes.py
    Untracked:  code/fc2leafphen.py
    Untracked:  code/finalPASbed2SAF.py
    Untracked:  code/findbuginpeaks.R
    Untracked:  code/fix4su304corr.py
    Untracked:  code/fix4su604corr.py
    Untracked:  code/fix4sukalisto.py
    Untracked:  code/fixFChead_bothfrac.py
    Untracked:  code/fixFChead_summary.py
    Untracked:  code/fixH3k12ac.py
    Untracked:  code/fixRNAhead4corr.py
    Untracked:  code/fixRNAkalisto.py
    Untracked:  code/fixhead_netseqfc.py
    Untracked:  code/get100upPAS.py
    Untracked:  code/getSeq100up.sh
    Untracked:  code/getseq100up.err
    Untracked:  code/getseq100up.out
    Untracked:  code/log/
    Untracked:  code/makeSAFbothfrac5perc.py
    Untracked:  code/makegencondeTSSfile.py
    Untracked:  code/netseqFC.err
    Untracked:  code/netseqFC.out
    Untracked:  code/netseqFC.sh
    Untracked:  code/removeloc_pheno.py
    Untracked:  code/run_DistPAS2Sig.err
    Untracked:  code/run_DistPAS2Sig.out
    Untracked:  code/run_distPAS2Sig.sh
    Untracked:  code/run_leafcutterDiffIso.sh
    Untracked:  code/run_leafcutter_ds.err
    Untracked:  code/run_leafcutter_ds.out
    Untracked:  code/selectNominalPvalues.py
    Untracked:  code/snakePASlog.out
    Untracked:  code/snakefiltPASlog.out
    Untracked:  code/subset_diffisopheno.py
    Untracked:  code/utrdms2saf.py
    Untracked:  data/CompareOldandNew/
    Untracked:  data/DTmatrix/
    Untracked:  data/DiffIso/
    Untracked:  data/PAS/
    Untracked:  data/QTLGenotypes/
    Untracked:  data/README.md
    Untracked:  data/RNAseq/
    Untracked:  data/Reads2UTR/
    Untracked:  data/SignalSiteFiles/
    Untracked:  data/ThirtyNineIndQtl_nominal/
    Untracked:  data/apaQTLNominal/
    Untracked:  data/apaQTLNominal_4pc/
    Untracked:  data/apaQTLPermuted/
    Untracked:  data/apaQTLPermuted_4pc/
    Untracked:  data/apaQTLs/
    Untracked:  data/assignedPeaks/
    Untracked:  data/bam/
    Untracked:  data/bam_clean/
    Untracked:  data/bam_waspfilt/
    Untracked:  data/bed_10up/
    Untracked:  data/bed_clean/
    Untracked:  data/bed_clean_sort/
    Untracked:  data/bed_waspfilter/
    Untracked:  data/bedsort_waspfilter/
    Untracked:  data/bothFrac_FC/
    Untracked:  data/exampleQTLs/
    Untracked:  data/fastq/
    Untracked:  data/filterPeaks/
    Untracked:  data/fourSU/
    Untracked:  data/h3k27ac/
    Untracked:  data/inclusivePeaks/
    Untracked:  data/inclusivePeaks_FC/
    Untracked:  data/mergedBG/
    Untracked:  data/mergedBW_byfrac/
    Untracked:  data/mergedBam/
    Untracked:  data/mergedbyFracBam/
    Untracked:  data/netseq/
    Untracked:  data/nuc_10up/
    Untracked:  data/nuc_10upclean/
    Untracked:  data/peakCoverage/
    Untracked:  data/peaks_5perc/
    Untracked:  data/phenotype/
    Untracked:  data/phenotype_5perc/
    Untracked:  data/sort/
    Untracked:  data/sort_clean/
    Untracked:  data/sort_waspfilter/
    Untracked:  nohup.out
    Untracked:  output/._.DS_Store
    Untracked:  output/dtPlots/
    Untracked:  output/fastqc/

Unstaged changes:
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/bed2saf.py
    Modified:   code/config.yaml
    Deleted:    code/test.txt

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
Rmd	e32bef6	brimittleman	2019-05-16	add mean corr
html	cb158b3	brimittleman	2019-05-15	Build site.
Rmd	700e9da	brimittleman	2019-05-15	switch rna data
html	35b1f6e	brimittleman	2019-05-15	Build site.
Rmd	90e0c4d	brimittleman	2019-05-15	add reg heatmap

In this analysis I want to look at the correlation between the net seq daata, rna seq, 4su, and h3k27ac to understand the relationship between nascent transcription and steady state RNA. This will be similar to the analysis in Li et al 2016 figure 1c.

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(gplots)


Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

library(gdata)

gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.

gdata: Unable to load perl libaries needed by read.xls()
gdata: to support 'XLSX' (Excel 2007+) files.

gdata: Run the function 'installXLSXsupport()'
gdata: to automatically download and install the perl
gdata: libaries needed to support Excel XLS and XLSX formats.


Attaching package: 'gdata'

The following objects are masked from 'package:dplyr':

    combine, first, last

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

    keep

The following object is masked from 'package:stats':

    nobs

The following object is masked from 'package:utils':

    object.size

The following object is masked from 'package:base':

    startsWith

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

library(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

H3K27ac at TSS

h3k27ac=read.table("../data/h3k27ac/H3K27acShyam_TSS_fixed.txt", header = T,stringsAsFactors = F)%>% dplyr::select(-Chr, -Start, -End,-Strand, -Length) %>% dplyr::rename("ID"= Geneid)

RNA seq

RNA=read.table("../data/fourSU/tr_decay_table_norm.txt", header=T, stringsAsFactors = F)%>%  dplyr::select(gene_id,contains("RNAseq_14000")) %>%  dplyr::rename("ID"=gene_id)

I also have the kalisto TPM that I can try:

RNA_TPM=read.table('../data/RNAseq/kallisto_RNAseq.txt', stringsAsFactors = F,header = T) %>% dplyr::rename("ID"=gene)

4su

fourSU=read.table("../data/fourSU/tr_decay_table_norm.txt", header=T, stringsAsFactors = F)%>%  dplyr::select(gene_id,contains("4su_30")) %>% dplyr::rename("ID"=gene_id)

tpm 4su

foursu_tpm=read.table("../data/fourSU/kallisto_4sU.txt", header = T, stringsAsFactors = F) %>% dplyr::rename("ID"=gene)

Netseq

I want to quantify reads 1kb on either side of the TSS. I will use the gencode v19 annotations to match the files above. I need to convert the gtf file into an saf file with the TSS.

python makegencondeTSSfile.py

Run feature counts with the 16 net seq libraries and this TSS file.

sbatch netseqFC.sh

Fix header

python fixFChead_bothfrac.py ../data/netseq/netseq_TSS.fc ../data/netseq/netseq_TSS.fixed.fc

netseq=read.table("../data/netseq/netseq_TSS.fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Strand, -Start, -Length, -End) %>% dplyr::rename("ID"=Geneid)

Correlation:

I will join all of these based on the genes we have data for in all.

allpheno=foursu_tpm %>% dplyr::inner_join(h3k27ac, by="ID") %>% dplyr::inner_join(RNA, by="ID") %>% dplyr::inner_join(netseq, by="ID")

allpheno_matrix= as.matrix(allpheno %>% dplyr::select(-ID))

my_palette <- colorRampPalette(c("white", "yellow", "orange", "red", "black"))(n = 100)
allphenocorr= abs(round(cor(allpheno_matrix,method="spearman"),2))
#4su, h3k27, RNA, netseq 
colBar=c(rep("Red",20), rep("Green", 59), rep("Blue",69), rep("Purple", 16))
heatmap.2(as.matrix(allphenocorr),trace="none", dendrogram='col',ColSideColors=colBar, col=my_palette)

Version	Author	Date
cb158b3	brimittleman	2019-05-15
35b1f6e	brimittleman	2019-05-15

Pairwise graphs averaging accross indviduals:

First I will take the mean for all individuals for each phenotype:

h3k27ac_mean=melt(h3k27ac,id.vars = "ID") %>% group_by(ID) %>% summarize(H3K27AC=mean(value))%>% filter(H3K27AC!=0)

RNA_mean=melt(RNA,id.vars = "ID") %>% group_by(ID) %>% summarize(Rna=mean(value)) %>% filter(Rna!=0)

foursu_tpm_mean= melt(foursu_tpm,id.vars = "ID") %>% group_by(ID) %>% summarize(FourSU=mean(value))%>% filter(FourSU!=0)

netseq_mean= melt(netseq,id.vars = "ID") %>% group_by(ID) %>% summarize(NetSeq=mean(value)) %>% filter(NetSeq!=0)

Join all of these:

Allpheno_mean= h3k27ac_mean %>% inner_join(RNA_mean,by="ID") %>% inner_join(foursu_tpm_mean, by="ID") %>%   inner_join(netseq_mean, by="ID")

ggplotRegression <- function (fit) {

require(ggplot2)

ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red") +
  labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
                     "Intercept =",signif(fit$coef[[1]],5 ),
                     " Slope =",signif(fit$coef[[2]], 5),
                     " P =",signif(summary(fit)$coef[2,4], 5)))
}

Plots:

ggplotRegression(lm(log10(Allpheno_mean$NetSeq)~ log10(Allpheno_mean$Rna)))

ggplotRegression(lm(log10(Allpheno_mean$FourSU)~ log10(Allpheno_mean$Rna)))

ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$Rna)))

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_1.3.0 reshape2_1.4.3  gdata_2.18.0    gplots_3.0.1   
 [5] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [9] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
[13] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] gtools_3.8.1       tidyselect_0.2.5   haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.3.1       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[19] rvest_0.3.2        caTools_1.17.1.1   evaluate_0.12     
[22] labeling_0.3       knitr_1.20         broom_0.5.1       
[25] Rcpp_1.0.0         KernSmooth_2.23-15 scales_1.0.0      
[28] backports_1.1.2    jsonlite_1.6       fs_1.2.6          
[31] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[34] grid_3.5.1         rprojroot_1.3-2    cli_1.0.1         
[37] tools_3.5.1        bitops_1.0-6       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_0.23.0       compiler_3.5.1