Last updated: 2019-09-11
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/NuclearSpecIncludeNotTested.Rmd
Modified: analysis/Readdistagainstfeatures.Rmd
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Modified: code/apaQTLCorrectPvalMakeQQ.R
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Modified: code/bam2bw.sh
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Modified: code/config.yaml
Modified: code/environment.yaml
Modified: code/makePheno.py
Modified: code/mergeAllBam.sh
Modified: code/mergeByFracBam.sh
Modified: code/mergePeaks.sh
Modified: code/peakFC.sh
Modified: code/snakemake.batch
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Modified: code/submit-snakemake.sh
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Modified: code/submit-snakemakefiltPAS.sh
Deleted: code/test.txt
Modified: data/MetaDataSequencing.txt
Deleted: reads_graphs.Rmd
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File | Version | Author | Date | Message |
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Rmd | 74a6602 | brimittleman | 2019-09-11 | add number or reads |
html | 3b25860 | brimittleman | 2019-09-04 | Build site. |
html | 191d71f | brimittleman | 2019-08-07 | Build site. |
Rmd | 01d0be5 | brimittleman | 2019-08-07 | add reads_graph analysis |
I first install and download all necessary libraries and packages
Then, I am reading in my file, and then only keeping the columns that I need: mapped, mapped no mispriming, reads, and the Sample Line ID.
df <- read.delim("../data/MetaDataSequencing.txt")
keeps <- c("line","fraction", "reads", "mapped", "Mapped_noMP")
frac_dif <- df[keeps]
Next, I am dividing the data set into it’s two categories: nuclear and total
nuclear <- subset(frac_dif, fraction == "nuclear", c(fraction, line, reads, mapped, Mapped_noMP))
total <- subset(frac_dif, fraction == "total", c(fraction, line, reads, mapped, Mapped_noMP))
Here, I create my data matrices. Each are proportions of reads. There is mapped, mapped with mispriming, mapped without mispriming, and unmapped. I do this for both nuclear and total.
#nuclear proportions
nuc_mapped_prop <- data.matrix(nuclear$mapped/nuclear$reads)
nuc_mapped_noMP_prop <- data.matrix(nuclear$Mapped_noMP/nuclear$reads)
nuc_mapped_MP <- nuc_mapped_prop - nuc_mapped_noMP_prop
nuc_none <- 1 - nuc_mapped_prop
nuc_lines <- data.matrix(nuclear$line)
#total proportions
total_mapped_prop <- data.matrix(total$mapped/total$reads)
total_mapped_noMP_prop <- data.matrix(total$Mapped_noMP/total$reads)
total_mapped_MP <-total_mapped_prop - total_mapped_noMP_prop
total_none <- 1 - total_mapped_prop
total_lines <- data.matrix(total$line)
Then, I combine these proportions into a large data frame called “combination”, which I can then easily create my plots.The gather() function tidies my data so that it is in the format that ggplot uses to create the bar plots. I then make the type column a factor with 3 levels. These levels allow me to order the stacks in the way I want them to.
nuc_combination <- data.frame(nuc_lines, nuc_mapped_noMP_prop, nuc_mapped_MP, nuc_none)
nuc_combination <- gather(nuc_combination, nuc_mapped_noMP_prop, nuc_mapped_MP, nuc_none, key = "type", value = "count")
nuc_combination$type <- factor(nuc_combination$type, levels=c("nuc_none", "nuc_mapped_MP", "nuc_mapped_noMP_prop"))
total_combination <- data.frame(total_lines, total_mapped_noMP_prop, total_mapped_MP, total_none)
total_combination <- gather(total_combination, total_mapped_noMP_prop, total_mapped_MP, total_none, key = "type", value = "count")
total_combination$type <- factor(total_combination$type, levels=c("total_none", "total_mapped_MP", "total_mapped_noMP_prop"))
Finally, I create my graphs, one for nuclear and the other for total. geom_col allows me to make bar graphs based off of heights in the data, instead of frequency, which geom_bar does. I stacked the bar plots using three proportions - mapped with mispriming, mapped without mispriming, and unmapped.
Note that the echo = FALSE
parameter was added to the code chunk to prevent printing of the R code that generated the plot.
Plot the number of reads:
nuclear_melt=melt(nuclear,id.vars = c("fraction", "line"), variable.name = "read", value.name = "Number")
ggplot(nuclear_melt, aes(x=line, y=Number, fill=read))+ geom_bar(stat="identity")+ scale_fill_brewer(palette="Dark2", name = "type of read:", labels = c("unmapped", "mapped + MP", "mapped + noMP"))+ theme(axis.text.x = element_text( hjust = 0,vjust = 1, size = 6, angle = 90)) + labs(x="lines", y="Number of Reads", title="Nuclear Fraction")
total_melt=melt(total,id.vars = c("fraction", "line"), variable.name = "read", value.name = "Number")
ggplot(total_melt, aes(x=line, y=Number, fill=read))+ geom_bar(stat="identity")+ scale_fill_brewer(palette="Dark2", name = "type of read:", labels = c("unmapped", "mapped + MP", "mapped + noMP"))+ theme(axis.text.x = element_text( hjust = 0,vjust = 1, size = 6, angle = 90)) + labs(x="lines", y="Number of Reads", title="Total Fraction")
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] reshape2_1.4.3 ggplot2_3.1.1 tidyr_0.8.3 dplyr_0.8.0.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 knitr_1.20 whisker_0.3-2
[4] magrittr_1.5 workflowr_1.4.0 munsell_0.5.0
[7] tidyselect_0.2.5 colorspace_1.3-2 R6_2.3.0
[10] rlang_0.4.0 plyr_1.8.4 stringr_1.3.1
[13] highr_0.7 tools_3.5.1 grid_3.5.1
[16] gtable_0.2.0 withr_2.1.2 git2r_0.25.2
[19] htmltools_0.3.6 lazyeval_0.2.1 yaml_2.2.0
[22] rprojroot_1.3-2 digest_0.6.18 assertthat_0.2.0
[25] tibble_2.1.1 crayon_1.3.4 RColorBrewer_1.1-2
[28] purrr_0.3.2 fs_1.3.1 glue_1.3.0
[31] evaluate_0.12 rmarkdown_1.10 labeling_0.3
[34] stringi_1.2.4 compiler_3.5.1 pillar_1.3.1
[37] scales_1.0.0 backports_1.1.2 pkgconfig_2.0.2