Mapping Stats

Last updated: 2019-10-01

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/annotationInfo.Rmd

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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
html	fb79675	brimittleman	2019-09-27	Build site.
Rmd	6d96d73	brimittleman	2019-09-27	add alt mapping method

In this analysis I will look at mapping across lines.

library(tidyverse)

── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

metadata=read.table("../data/metadata_HCpanel.txt", stringsAsFactors = F, header = T)

ggplot(metadata, aes(x=Line, y=PerMap, fill=Species,by=Species)) + geom_bar(position = "dodge", stat="identity") + facet_grid(~Fraction) +theme(axis.text.x = element_text(angle = 90)) + labs(y= "Map percent", title="Pre cleaning")

Version	Author	Date
fb79675	brimittleman	2019-09-27

ggplot(metadata, aes(x=Line, y=PerMapClean, fill=Species,by=Species)) + geom_bar(position = "dodge", stat="identity") + facet_grid(~Fraction) +theme(axis.text.x = element_text(angle = 90)) + labs(y= "Map percent", title="Post cleaning")

Version	Author	Date
fb79675	brimittleman	2019-09-27

These are really low. I will try mapping some of the human lines to hg19 to compare to the apaQTL project.

mkdir ../Human/data/bam_hg19
mkdir ../Human/data/sort_hg19
sbatch maphg19.sh

Try with subread. (is it a star problem as well)

mkdir  ../Human/data/bam_hg19_sub
mkdir ../Human/data/sort_hg19_sub
sbatch maphg19_subjunc.sh

I ended up doing this with hg38

hg19Res=read.table("../Human/data/Comphg19_hg38.txt", header = T, stringsAsFactors = F)%>% select(Line, Fraction, PerMap, HG19_mapper, subread_perc) 
hg19Res$Line=as.factor(hg19Res$Line)


hg19Res_m= melt(hg19Res, id.vars = c("Line", "Fraction"))


ggplot(hg19Res_m, aes(x=Line, y=value, fill=variable)) + geom_bar(position = "dodge", stat="identity") +facet_grid(~Fraction)

Version	Author	Date
fb79675	brimittleman	2019-09-27

Seems like the 38 genomes isnt as good in either.

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  forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       workflowr_1.4.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4

Mapping Stats

Briana Mittleman

9/26/2019