39indQC

Last updated: 2019-02-15

Checks: 6 0

Knit directory: threeprimeseq/analysis/

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
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    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

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	b9cce4b	Briana Mittleman	2018-12-05	Build site.
Rmd	e230640	Briana Mittleman	2018-12-05	add code to save relevant figures
html	bbd632b	Briana Mittleman	2018-09-25	Build site.
Rmd	66570c5	Briana Mittleman	2018-09-25	PAS per gene
html	d3bb287	Briana Mittleman	2018-09-24	Build site.
Rmd	f7934ce	Briana Mittleman	2018-09-24	wflow_publish(c(“index.Rmd”, “39indQC.Rmd”))

I will use this to look at the map stats and peak stats for the full set of 39 ind.

library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0

Warning: package 'stringr' was built under R version 3.5.2

── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(workflowr)

This is workflowr version 1.2.0
Run ?workflowr for help getting started

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

library(tximport)

Map Stats

mapstats= read.csv("../data/comb_map_stats_39ind.csv", header = T, stringsAsFactors = F)
mapstats$line=as.factor(mapstats$line)
mapstats$fraction=as.factor(mapstats$fraction)
map_melt=melt(mapstats, id.vars=c("line", "fraction"), measure.vars = c("comb_reads", "comb_mapped", "comb_prop_mapped"))

prop_mapped= map_melt %>% filter(variable=="comb_prop_mapped")
mapped_reads= map_melt %>% filter(variable=="comb_mapped")


mapplot_prop=ggplot(prop_mapped, aes(y=value, x=line, fill=fraction)) + geom_bar(stat="identity",position="dodge") + labs( title="Proportion of reads mapped") + ylab("Proportion mapped")


mapplot_mapped=ggplot(mapped_reads, aes(y=value, x=line, fill=fraction)) + geom_bar(stat="identity",position="dodge") + labs( title="Number of Mapped reads") + ylab("Mapped")

plot_grid(mapplot_prop, mapplot_mapped)

Version	Author	Date
b9cce4b	Briana Mittleman	2018-12-05
d3bb287	Briana Mittleman	2018-09-24

Plot boxplots for total vs nuclear.

box_mapprop=ggplot(prop_mapped, aes(y=value, x=fraction, fill=fraction)) + geom_boxplot(width=.3) + geom_jitter(position = position_jitter(.3)) + labs( title="Map Proportion") + ylab("Mapped Proportion") + scale_fill_manual(values=c("deepskyblue3","darkviolet"))

box_map=ggplot(mapped_reads, aes(y=value, x=fraction, fill=fraction)) + geom_boxplot(width=.3) + geom_jitter(position = position_jitter(.3)) + labs( title="Number of Mapped reads") + ylab("Mapped") + scale_fill_manual(values=c("deepskyblue3","darkviolet"))


bothmapplots=plot_grid(box_map, box_mapprop)
bothmapplots

Version	Author	Date
b9cce4b	Briana Mittleman	2018-12-05
d3bb287	Briana Mittleman	2018-09-24

ggsave("../output/plots/MapBoxplots.png",bothmapplots)

Saving 7 x 5 in image

Genes with multiple peaks

This is similar to the analysis I ran in dataprocfigures.Rmd. I start by overlappping the refseq genes with my peaks. With the script refseq_countdistinct.sh.

namesPeak=c("Chr", "Start", "End", "Name", "Score", "Strand", "numPeaks")
Opeakpergene=read.table("../data/peakPerRefSeqGene/filtered_APApeaks_perRefseqGene_oppStrand.txt", stringsAsFactors = F, header = F, col.names = namesPeak) %>% mutate(onePeak=ifelse(numPeaks==1, 1, 0 )) %>%  mutate(multPeaks=ifelse(numPeaks > 1, 1, 0 ))

Ogenes1peak=sum(Opeakpergene$onePeak)/nrow(Opeakpergene) 
OgenesMultpeak=sum(Opeakpergene$multPeaks)/nrow(Opeakpergene)
Ogenes0peak= 1- Ogenes1peak - OgenesMultpeak


OperPeak= c(round(Ogenes0peak,digits = 3), round(Ogenes1peak,digits = 3),round(OgenesMultpeak, digits = 3))
Category=c("Zero", "One", "Multiple")
OperPeakdf=as.data.frame(cbind(Category,OperPeak))

OperPeakdf$OperPeak=as.numeric(as.character(OperPeakdf$OperPeak))

Olab1=paste("Genes =", Ogenes0peak*nrow(Opeakpergene), sep=" ")
Olab2=paste("Genes =", sum(Opeakpergene$onePeak), sep=" ")
Olab3=paste("Genes =", sum(Opeakpergene$multPeaks), sep=" ")

Ogenepeakplot=ggplot(OperPeakdf, aes(x="", y=OperPeak, by=Category, fill=Category)) + geom_bar(stat="identity")+ labs(title="Characterize Refseq genes by number of PAS- Oppstrand", y="Proportion of Protein Coding gene", x="")+ scale_fill_brewer(palette="Paired") + coord_cartesian(ylim=c(0,1)) + annotate("text", x="", y= .2, label=Olab1) + annotate("text", x="", y= .4, label=Olab2) + annotate("text", x="", y= .9, label=Olab3)
Ogenepeakplot

Version	Author	Date
b9cce4b	Briana Mittleman	2018-12-05
bbd632b	Briana Mittleman	2018-09-25

I will now repull in the RNA seq data for one of my lines to look at the expression levels of the genes with at least 1 called peak.

tx2gene=read.table("../data/RNAkalisto/ncbiRefSeq.txn2gene.txt" ,header= F, sep="\t", stringsAsFactors = F)

txi.kallisto.tsv <- tximport("../data/RNAkalisto/abundance.tsv", type = "kallisto", tx2gene = tx2gene)

Note: importing `abundance.h5` is typically faster than `abundance.tsv`

reading in files with read_tsv

1 
removing duplicated transcript rows from tx2gene
transcripts missing from tx2gene: 99
summarizing abundance
summarizing counts
summarizing length

txi.kallisto.tsv$abundance= as.data.frame(txi.kallisto.tsv$abundance) %>% rownames_to_column(var="Name")
colnames(txi.kallisto.tsv$abundance)= c("Name", "TPM")
#genes with >0 TPM and at least 1 peak
refPeakandRNA_withO_TPM=Opeakpergene %>% inner_join(txi.kallisto.tsv$abundance, by="Name") %>% filter(TPM>0, numPeaks>0)

#genes with >0  TPM and 0 peak
refPeakandRNA_noPeakw_withO_TPM=Opeakpergene %>% inner_join(txi.kallisto.tsv$abundance, by="Name") %>% filter(TPM >0, numPeaks==0) 

#plot
plot(sort(log10(refPeakandRNA_withO_TPM$TPM), decreasing = T), main="Distribution of RNA expression 18486", ylab="log10 TPM", xlab="Refseq Gene")
points(sort(log10(refPeakandRNA_noPeakw_withO_TPM$TPM), decreasing = T), col="Red")
legend("topright", legend=c("Genes wth Peak", "Genes without Peak"), col=c("black", "red"),pch=19)

Version	Author	Date
b9cce4b	Briana Mittleman	2018-12-05
bbd632b	Briana Mittleman	2018-09-25

Plot this as distributions.

comp_RNAtpm=ggplot(refPeakandRNA_withO_TPM, aes(x=log10(TPM))) + geom_histogram(binwidth=.5, alpha=.5) +geom_histogram(data = refPeakandRNA_noPeakw_withO_TPM, aes(x=log10(TPM)), fill="Red", alpha=.5, binwidth=.5) + labs(title="Comparing RNA expression for genes with a PAS vs no PAS") + annotate("text", x=-8, y=3000, col="Red", label="Genes without PAS") + annotate("text", x=-8.1, y=2700, col="Black", label="Genes with PAS") + geom_rect(linetype=1, xmin=-10, xmax=-6, ymin=2500, ymax=3200, color="Black", alpha=0)
comp_RNAtpm

Version	Author	Date
b9cce4b	Briana Mittleman	2018-12-05
bbd632b	Briana Mittleman	2018-09-25

ggsave("../output/plots/QC_plots/TPMcoverage4GenesbyPAS.png",comp_RNAtpm)

Saving 7 x 5 in image

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  tximport_1.8.0  cowplot_0.9.3   reshape2_1.4.3 
 [5] workflowr_1.2.0 forcats_0.3.0   stringr_1.4.0   dplyr_0.7.6    
 [9] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
[13] ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4   haven_1.1.2        lattice_0.20-35   
 [4] colorspace_1.3-2   htmltools_0.3.6    yaml_2.2.0        
 [7] rlang_0.2.2        pillar_1.3.0       glue_1.3.0        
[10] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[13] readxl_1.1.0       bindr_0.1.1        plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[19] rvest_0.3.2        evaluate_0.13      labeling_0.3      
[22] knitr_1.20         broom_0.5.0        Rcpp_0.12.19      
[25] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[28] fs_1.2.6           hms_0.4.2          digest_0.6.17     
[31] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[34] cli_1.0.1          tools_3.5.1        magrittr_1.5      
[37] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[40] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[43] assertthat_0.2.0   rmarkdown_1.11     httr_1.3.1        
[46] rstudioapi_0.9.0   R6_2.3.0           nlme_3.1-137      
[49] git2r_0.24.0       compiler_3.5.1

39indQC

Briana Mittleman

2018-09-24

Map Stats

Genes with multiple peaks