Last updated: 2019-06-19
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/molQTL.Rmd
Modified: analysis/nascentRNA.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/signalsiteanalysis.Rmd
Modified: code/BothFracDTPlotGeneRegions.sh
Modified: code/Snakefile
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Modified: code/apaQTL_Nominal.sh
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Modified: code/bed2saf.py
Modified: code/cluster.json
Modified: code/clusterfiltPAS.json
Modified: code/config.yaml
Modified: code/environment.yaml
Modified: code/makePheno.py
Deleted: code/test.txt
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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'
The following objects are masked from 'package:dplyr':
combine, first, last
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object.size
The following object is masked from 'package:base':
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library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
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library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
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=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)
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)
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)
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)
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)
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)
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$Rna)))
ggplotRegression(lm(log10(Allpheno_mean$FourSU)~ log10(Allpheno_mean$Rna)))
ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$Rna)))
ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$NetSeq)))
ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$FourSU)))
ggplotRegression(lm(log10(Allpheno_mean$FourSU)~ log10(Allpheno_mean$NetSeq)))
ggplotRegression(lm(log10(Allpheno_mean$Rna)~ log10(Allpheno_mean$NetSeq)))
ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$NetSeq)))
ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$TotApa)))
ggplotRegression(lm(log10(Allpheno_mean$H3K27AC)~ log10(Allpheno_mean$NucApa)))
ggplotRegression(lm(Allpheno_mean$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