39indQC

Last updated: 2018-12-05

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Ignored files:
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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   code/Snakefile

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Expand here to see past versions:

File	Version	Author	Date	Message
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 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0

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

library(workflowr)

This is workflowr version 1.1.1
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)

Expand here to see past versions of unnamed-chunk-2-1.png:

Version	Author	Date
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

Expand here to see past versions of unnamed-chunk-3-1.png:

Version	Author	Date
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

Expand here to see past versions of unnamed-chunk-4-1.png:

Version	Author	Date
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)

Expand here to see past versions of unnamed-chunk-5-1.png:

Version	Author	Date
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

Expand here to see past versions of unnamed-chunk-6-1.png:

Version	Author	Date
bbd632b	Briana Mittleman	2018-09-25

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

Saving 7 x 5 in image

Session information

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_1.1.1 forcats_0.3.0   stringr_1.3.1   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        R.oo_1.22.0        pillar_1.3.0      
[10] glue_1.3.0         withr_2.1.2        R.utils_2.7.0     
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] bindr_0.1.1        plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] R.methodsS3_1.7.1  evaluate_0.11      labeling_0.3      
[25] knitr_1.20         broom_0.5.0        Rcpp_0.12.19      
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.5      
[31] hms_0.4.2          digest_0.6.17      stringi_1.2.4     
[34] grid_3.5.1         rprojroot_1.3-2    cli_1.0.1         
[37] tools_3.5.1        magrittr_1.5       lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.8    
[49] R6_2.3.0           nlme_3.1-137       git2r_0.23.0      
[52] compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1

39indQC

Briana Mittleman

2018-09-24

Map Stats

Genes with multiple peaks

Session information