Relationship between RNA, net-seq, h3k27ac

Last updated: 2019-06-19

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Knit directory: apaQTL/analysis/

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    Modified:   code/makePheno.py
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File	Version	Author	Date	Message
Rmd	e0acd44	brimittleman	2019-06-19	fix order
html	34dcae7	brimittleman	2019-06-13	Build site.
Rmd	cc3b639	brimittleman	2019-06-13	fix bug
html	9958fb1	brimittleman	2019-05-16	Build site.
Rmd	ae6ed8b	brimittleman	2019-05-16	scatter plot pas
html	5557709	brimittleman	2019-05-16	Build site.
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'

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library(reshape2)


Attaching package: 'reshape2'

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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)

Total:

Genes to include:

sig_genes=read.table(file="../data/highdiffsiggenes.txt",col.names = "GeneName")

I will have to change the gene names for the 3’ info:

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('geneid', 'GeneName', 'source' ),stringsAsFactors = F) %>% dplyr::select(-source)

peaknumlist=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.5perc.bed", stringsAsFactors = F, header=F, col.names = c("chr", "start","end", "id", "score", "strand"))  %>% separate(id, into=c("peaknum", "geneid"), sep=":") %>% mutate(peakid=paste("peak", peaknum,sep=""))

TotAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>%  dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":")  %>% separate(geneID,into=c("GeneName", "loc"),sep="_") %>% inner_join(sig_genes, by="GeneName")%>%  dplyr::semi_join(peaknumlist, by="peakid") %>%  dplyr::select(-chrom , -start, -end, -strand, -loc)

Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].

Warning: Column `GeneName` joining character vector and factor, coercing
into character vector

TotApaMelt=melt(TotAPA, id.vars =c( "peakid", "GeneName"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, GeneName, Individual, count) %>% inner_join(geneNames,by="GeneName") %>% group_by(Individual,geneid) %>% summarize(TotApa=sum(count)) %>% ungroup() %>% dplyr::rename("ID"=geneid)  %>% mutate(Individual=paste("TotAPA_", Individual, sep=""))

##spread

totApaSpread= spread(TotApaMelt, Individual,TotApa)

Nuclear

NucAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>%  dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":")  %>% separate(geneID,into=c("GeneName", "loc"),sep="_") %>% inner_join(sig_genes, by="GeneName")%>%  dplyr::semi_join(peaknumlist, by="peakid") %>%  dplyr::select(-chrom , -start, -end, -strand, -loc)

Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].

Warning: Column `GeneName` joining character vector and factor, coercing
into character vector

NucApaMelt=melt(NucAPA, id.vars =c( "peakid", "GeneName"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, GeneName, Individual, count) %>% inner_join(geneNames,by="GeneName") %>% group_by(Individual,geneid) %>% summarize(NucApa=sum(count)) %>% ungroup() %>% dplyr::rename("ID"=geneid)  %>% mutate(Individual=paste("NucAPA_", Individual, sep=""))

##spread

nucApaSpread= spread(NucApaMelt, Individual,NucApa)

Correlation:

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

#4su, h3k27, RNA, netseq, nuc, total
allpheno=fourSU %>% dplyr::inner_join(h3k27ac, by="ID") %>% dplyr::inner_join(RNA, by="ID") %>% dplyr::inner_join(netseq, by="ID") %>%  dplyr::inner_join(nucApaSpread, by="ID") %>%  dplyr::inner_join(totApaSpread, 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-red, h3k27-green, RNA-blue, netseq-purple, nuc-orange, total-yellow
colBar=c(rep("Red",65), rep("Green", 59), rep("Blue",69), rep("Purple", 16),rep("Orange", 54),rep("Yellow", 54))
heatmap.2(as.matrix(allphenocorr),trace="none", dendrogram='col',ColSideColors=colBar, col=my_palette)

Version	Author	Date
34dcae7	brimittleman	2019-06-13
5557709	brimittleman	2019-05-16

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(variable) %>% mutate(sumInd=sum(value)) %>% ungroup() %>% mutate(normVal=value/sumInd) %>% group_by(ID) %>% summarize(H3K27AC=mean(normVal))%>% filter(H3K27AC!=0)

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

foursu_mean= melt(fourSU,id.vars = "ID") %>%  group_by(variable) %>% mutate(sumInd=sum(value)) %>% ungroup() %>% mutate(normVal=value/sumInd) %>% group_by(ID) %>% summarize(FourSU=mean(normVal))%>% filter(FourSU!=0)
  

netseq_mean= melt(netseq,id.vars = "ID") %>% group_by(variable) %>% mutate(sumInd=sum(value)) %>% ungroup() %>% mutate(normVal=value/sumInd) %>% group_by(ID) %>% summarize(NetSeq=mean(normVal))%>% filter(NetSeq!=0)

totapa_mean= melt(totApaSpread,id.vars = "ID") %>% group_by(variable) %>% mutate(sumInd=sum(value)) %>% ungroup() %>% mutate(normVal=value/sumInd) %>% group_by(ID) %>% summarize(TotApa=mean(normVal))%>% filter(TotApa!=0)


nucapa_mean= melt(nucApaSpread,id.vars = "ID") %>% group_by(variable) %>% mutate(sumInd=sum(value)) %>% ungroup() %>% mutate(normVal=value/sumInd) %>% group_by(ID) %>% summarize(NucApa=mean(normVal))%>% filter(NucApa!=0)

Join all of these:

Allpheno_mean= h3k27ac_mean %>% inner_join(RNA_mean,by="ID") %>% inner_join(foursu_mean, by="ID") %>%   inner_join(netseq_mean, by="ID") %>% inner_join(totapa_mean, by="ID")  %>%inner_join(nucapa_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)))

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

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

Version	Author	Date
34dcae7	brimittleman	2019-06-13
9958fb1	brimittleman	2019-05-16

ggplotRegression(lm(Allpheno_mean$NucApa~ Allpheno_mean$Rna))

Version	Author	Date
34dcae7	brimittleman	2019-06-13

ggplotRegression(lm(Allpheno_mean$NucApa~ Allpheno_mean$FourSU))

ggplotRegression(lm(Allpheno_mean$TotApa~ Allpheno_mean$Rna))

ggplotRegression(lm(Allpheno_mean$TotApa~ Allpheno_mean$FourSU))

heatmap correlation for these:

Allpheno_mean_mat= as.matrix(Allpheno_mean %>% dplyr::select(-ID))
Allpheno_mean_matcorr= abs(round(cor(Allpheno_mean_mat,method="spearman"),2))


Allpheno_mean_matcorrOrder=as.data.frame(Allpheno_mean_matcorr) %>% select(NetSeq, H3K27AC,NucApa,TotApa,FourSU,Rna)


heatmap.2(as.matrix(Allpheno_mean_matcorrOrder),trace="none",dendrogram = "none", col=my_palette,Colv = F)

Allpheno_mean_matcorrOrder

        NetSeq H3K27AC NucApa TotApa FourSU  Rna
H3K27AC   0.07    1.00   0.14   0.12   0.20 0.15
Rna       0.18    0.15   0.58   0.63   0.88 1.00
FourSU    0.21    0.20   0.62   0.62   1.00 0.88
NetSeq    1.00    0.07   0.17   0.18   0.21 0.18
TotApa    0.18    0.12   0.88   1.00   0.62 0.63
NucApa    0.17    0.14   1.00   0.88   0.62 0.58

remove net and k27ac

Allpheno_mean_small=as.matrix(Allpheno_mean %>% dplyr::select(-ID,-H3K27AC,-NetSeq))
Allpheno_meanSM_matcorr= abs(round(cor(Allpheno_mean_small,method="spearman"),2))

heatmap.2(as.matrix(Allpheno_meanSM_matcorr),trace="none", col=my_palette, ,dendrogram = "none")

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_0.25.2       compiler_3.5.1