initial_map

Last updated: 2019-02-15

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Knit directory: threeprimeseq/analysis/

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    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
html	df39edc	Briana Mittleman	2018-06-01	Build site.
Rmd	e4baa3b	Briana Mittleman	2018-06-01	compare star and subj
html	7126988	Briana Mittleman	2018-05-26	Build site.
Rmd	252c548	Briana Mittleman	2018-05-26	plot prop mapping with subjunc
html	b5cdd59	Briana Mittleman	2018-05-26	Build site.
Rmd	9a49459	Briana Mittleman	2018-05-26	start map qc analysis

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

library(workflowr)

This is workflowr version 1.2.0
Run ?workflowr for help getting started

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


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

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Subjunc

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")

subjplot=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")

Star mapping

I added two lines to the csv file with the star map stats for each line.

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

Summaries for each number:

summary(star_map$star_mapped)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
3326506 5426888 5868012 5834521 6314488 7814874

summary(star_map$star_prop_mapped)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.5648  0.6452  0.6558  0.6719  0.7144  0.7827

Look at this graphically:

star_melt=melt(star_map, id.vars=c("line", "fraction"), measure.vars = c("reads", "star_mapped", "star_prop_mapped"))

star_prop_mapped= star_melt %>% filter(variable=="star_prop_mapped")

starplot=ggplot(star_prop_mapped, aes(y=value, x=line, fill=fraction)) + geom_bar(stat="identity",position="dodge") + labs( title="Proportion of reads mapped with Star") + ylab("Proportion mapped") +  geom_hline(yintercept = mean(star_prop_mapped$value)) + annotate("text",4, mean(star_prop_mapped$value)- .1, vjust = -1, label = "Mean mapping proportion= .672")

Compare the plots:

plot_grid(subjplot,starplot)

Version	Author	Date
df39edc	Briana Mittleman	2018-06-01

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  cowplot_0.9.3   dplyr_0.7.6     reshape2_1.4.3 
[5] tidyr_0.8.1     ggplot2_3.0.0   workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     compiler_3.5.1   pillar_1.3.0     git2r_0.24.0    
 [5] plyr_1.8.4       bindr_0.1.1      tools_3.5.1      digest_0.6.17   
 [9] evaluate_0.13    tibble_1.4.2     gtable_0.2.0     pkgconfig_2.0.2 
[13] rlang_0.2.2      yaml_2.2.0       withr_2.1.2      stringr_1.4.0   
[17] knitr_1.20       fs_1.2.6         rprojroot_1.3-2  grid_3.5.1      
[21] tidyselect_0.2.4 glue_1.3.0       R6_2.3.0         rmarkdown_1.11  
[25] purrr_0.2.5      magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[29] scales_1.0.0     htmltools_0.3.6  assertthat_0.2.0 colorspace_1.3-2
[33] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[37] crayon_1.3.4

initial_map_qc

Briana Mittleman

5/26/2018

Subjunc

Star mapping