Last updated: 2019-11-25

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Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/diffSplicing.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.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 7e02ff7 brimittleman 2019-11-25 update hg19 map stats
html 3848668 brimittleman 2019-11-20 Build site.
Rmd c5953c3 brimittleman 2019-11-20 add results
html 40db68c brimittleman 2019-11-20 Build site.
Rmd 7c206f8 brimittleman 2019-11-20 add extra QC anaylsis 
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()

I have high levels of multi mapping in these samples. I want to test if this is the case in hg19 as well. I can try with 1 or 2 samples.

mkdir ../Human/data/RNAseq/bam_hg19
sbatch mapRNAseqhg19.sh

18504 hg19: multi map= 15.24 (83% map) hg38: multi map= 21.39% (77.52% map)

18510- hg19: multimap = 14.46% (83.97% map) hg38: multimap= 20.49% (78.37%% map)

redo this with the new multimap criteria:

sbatch mapRNAseqhg19_newPipeline.sh

New pipeline:

18504 hg19: too many loci= 0.37%, multiple loci- 14.93% (83.19%) hg38: too many loci= 0.65%, multiple loci- 20.75% (77.50%)

18510- hg19: too many loci= 0.34%, multiple loci- 14.17% (83.93%) hg38: too many loci= 0.60%, multiple loci- 19.89% (78.35%)

With the hg19 samples make the multimapping graphs.

sbatch hg19MapStats.sh

python numMultimap.py /project2/gilad/briana/Comparative_APA/data/MapStats/Human_hg19_RNAseq_mapstats.txt /project2/gilad/briana/Comparative_APA/data/MapStats/Humanhg19_groupedMultstat.txt

HumanHG19= read.table("../data/MapStats/Humanhg19_groupedMultstat.txt", header = F, col.names = c("HI", "Hg19Count"), stringsAsFactors = F) %>% separate(HI, into=c("N", "i", "num"),sep=":") 
HumanHG19$num=as.integer(as.character(HumanHG19$num))
ggplot(HumanHG19, aes(x=num, y=Hg19Count)) + geom_bar(stat="identity") + labs(x="Number of loacations read maps to", y="Reads", title="Multimapping statistics for hg19 RNA seq")

Add the hg38 human chimp.

HumanMM= read.table("../data/MapStats/Human_groupedMultstat.txt", header = F, col.names = c("HI", "Human"), stringsAsFactors = F)
ChimpMM=read.table("../data/MapStats/Chimp_groupedMultstat.txt",header = F, col.names = c("HI", "Chimp"),stringsAsFactors = F)
HumanHG19_2= read.table("../data/MapStats/Humanhg19_groupedMultstat.txt", header = F, col.names = c("HI", "Hg19"), stringsAsFactors = F)


allMM=HumanMM %>% inner_join(ChimpMM ,by="HI") %>% inner_join(HumanHG19_2, by="HI")%>% gather("species", "Count", Human:Hg19) %>% separate(HI, into=c("N", "i", "num"),sep=":")  %>% group_by(species) %>% mutate(fullCount=sum(Count), prop=Count/fullCount)

allMM$num=as.integer(as.character(allMM$num))
ggplot(allMM, aes(x=num, y=prop, fill=species,by=species)) + geom_bar(position = "dodge" ,stat="identity") + labs(x="Number of loacations read maps to", y="Reads", title="Multimapping statistics for RNA seq") + scale_fill_brewer(palette = "Dark2")


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

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] workflowr_1.5.0    cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1