Last updated: 2019-02-15
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Knit directory: threeprimeseq/analysis/
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html | b9cce4b | Briana Mittleman | 2018-12-05 | Build site. |
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Rmd | 66570c5 | Briana Mittleman | 2018-09-25 | PAS per gene |
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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)
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)
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
ggsave("../output/plots/MapBoxplots.png",bothmapplots)
Saving 7 x 5 in image
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
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)
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
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