initial_map_qc

Last updated: 2018-05-26

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I will use this analysis to look at initial mapping QC for the two mappers I am using.

library(workflowr)

Loading required package: rmarkdown

This is workflowr version 1.0.1
Run ?workflowr for help getting started

library(ggplot2)
library(tidyr)
library(reshape2)

Warning: package 'reshape2' was built under R version 3.4.3

Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

I created a csv with the number of reads, mapped reads, and proportion of reads mapped per library.

subj_map= read.csv("../data/reads_mapped_three_prime_seq.csv", header=TRUE, stringsAsFactors = FALSE)
subj_map$line=as.factor(subj_map$line)
subj_map$fraction=as.factor(subj_map$fraction)

Summaries for each number:

summary(subj_map$reads)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 5103350  8030068  8776602  8670328  9341566 10931074 

summary(subj_map$mapped)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
3575191 5688940 6268228 6091626 6394260 7788593 

summary(subj_map$prop_mapped)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.6658  0.6932  0.7034  0.7025  0.7135  0.7343 

Look at this graphically:

subj_melt=melt(subj_map, id.vars=c("line", "fraction"), measure.vars = c("reads", "mapped", "prop_mapped"))

subj_prop_mapped= subj_melt %>% filter(variable=="prop_mapped")

ggplot(subj_prop_mapped, aes(y=value, x=line, fill=fraction)) + geom_bar(stat="identity",position="dodge") + labs( title="Proportion of reads mapped with Subjunc") + ylab("Proportion mapped") +  geom_hline(yintercept = mean(subj_prop_mapped$value)) + annotate("text",4, mean(subj_prop_mapped$value)- .1, vjust = -1, label = "Mean mapping proportion= .702")

sessionInfo()

R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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    dplyr_0.7.4     reshape2_1.4.3  tidyr_0.7.2    
[5] ggplot2_2.2.1   workflowr_1.0.1 rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.15      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1        
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.14     evaluate_0.10.1   tibble_1.4.2     
[13] gtable_0.2.0      pkgconfig_2.0.1   rlang_0.1.6      
[16] yaml_2.1.16       stringr_1.2.0     knitr_1.18       
[19] rprojroot_1.3-2   grid_3.4.2        glue_1.2.0       
[22] R6_2.2.2          purrr_0.2.4       magrittr_1.5     
[25] whisker_0.3-2     backports_1.1.2   scales_0.5.0     
[28] htmltools_0.3.6   assertthat_0.2.0  colorspace_1.3-2 
[31] labeling_0.3      stringi_1.1.6     lazyeval_0.2.1   
[34] munsell_0.4.3     R.oo_1.22.0      

This reproducible R Markdown analysis was created with workflowr 1.0.1