Last updated: 2019-08-07

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Knit directory: apaQTL/analysis/

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    Deleted:    reads_graphs.Rmd

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.

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File Version Author Date Message
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.


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] 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