Relationship between RNA, net-seq, h3k27ac

Last updated: 2019-05-15

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Knit directory: apaQTL/analysis/

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File	Version	Author	Date	Message
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.

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H3K27ac at TSS

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

RNA seq

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

4su

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

tpm 4su

foursu_tpm=read.table("../data/fourSU/kallisto_4sU.txt", header = T, stringsAsFactors = F) %>% 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) %>% select(-Chr, -Strand, -Start, -Length, -End) %>% rename("ID"=Geneid)

Correlation:

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

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

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

allphenocorr= 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='none',ColSideColors=colBar)

Version	Author	Date
35b1f6e	brimittleman	2019-05-15

sessionInfo()

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[49] R6_2.3.0           nlme_3.1-137       git2r_0.23.0      
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Relationship between RNA, net-seq, h3k27ac

Briana Mittleman

5/15/2019

H3K27ac at TSS

RNA seq

4su

Netseq

Correlation: