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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
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    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
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    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.

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
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.

Version	Author	Date
3b25860	brimittleman	2019-09-04
191d71f	brimittleman	2019-08-07

Version	Author	Date
3b25860	brimittleman	2019-09-04
191d71f	brimittleman	2019-08-07

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

Reads_graphs

Mayher

8/1/2019