Last updated: 2019-11-12

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
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    Modified:   code/DistPAS2Sig.py
    Modified:   code/apaQTLsnake.err
    Deleted:    code/test.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 d8c08ab brimittleman 2019-11-12 add enrichment
html 8a3d1ea brimittleman 2019-11-08 Build site.
Rmd d72c604 brimittleman 2019-11-08 add first intron analysis

Check for enrichment in first intron.

This uses files created in the verifyFirstintronresult analysis.

library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
pas2intron= read.table( "../data/intron_analysis/NuclearIntronPASwithWhichintron.txt", stringsAsFactors = F, header = T)

I need to know how many 1st introns there are, how many second introns there are…

The number of introns in these genes is in the nintron column.

I can group the number of genes with each number of introns.

pas2intron_genes= pas2intron %>% select(gene, nintron) %>% unique() %>% group_by(nintron) %>% summarize(nGenes=n())

head(pas2intron_genes)
# A tibble: 6 x 2
  nintron nGenes
    <int>  <int>
1       3    393
2       4    401
3       5    408
4       6    385
5       7    362
6       8    340

I want to add these numbers but only include below. For example, all of these have 1 intron, only those above 5 have 5 introns.

Do this manually for 1-10 first.

n1intron=sum(pas2intron_genes$nGenes)
n2intron=sum(pas2intron_genes$nGenes)
n3intron=sum(pas2intron_genes$nGenes)
n4intron= pas2intron_genes %>% filter(nintron >=4) 
n5intron= pas2intron_genes %>% filter(nintron >=5) 
n6intron= pas2intron_genes %>% filter(nintron >=6) 
n7intron= pas2intron_genes %>% filter(nintron >=7) 
n8intron= pas2intron_genes %>% filter(nintron >=8) 
n9intron= pas2intron_genes %>% filter(nintron >=9) 
n10intron= pas2intron_genes %>% filter(nintron >=10) 


nGenesum=c(n1intron,n2intron,n3intron, sum(n4intron$nGenes),sum(n5intron$nGenes),sum(n6intron$nGenes),sum(n7intron$nGenes),sum(n8intron$nGenes),sum(n9intron$nGenes),sum(n10intron$nGenes))
pas2intron_intron_grouped=pas2intron %>% group_by(Intronid) %>% summarise(nBin=n(), meanSize=mean(intronLength))  %>% mutate(normNBin=nBin/meanSize) 

pas2intron_intron_grouped_small=pas2intron_intron_grouped %>% filter(Intronid <=10) 

pas2intron_intron_grouped_small_withnintron=as.data.frame(cbind(pas2intron_intron_grouped_small, nGenesum)) %>% mutate(NormbylengthandN=normNBin/nGenesum)

ggplot(pas2intron_intron_grouped_small_withnintron, aes(x=Intronid, y=NormbylengthandN)) +geom_bar(stat="identity") + labs(title="PAS by Intron \n normalized by intron mean length and number of introns tested", y="normalized number in intron category", x="intron category")

Version Author Date
8a3d1ea brimittleman 2019-11-08

Assess enrichment based on introns 1-10 with a uniform distribution:

I will need to sum the number of PAS in these introns and divide them evenily.

nIntron1.10=sum(pas2intron_intron_grouped_small_withnintron$nBin)
pas2intron_intron_grouped_small_withnintron_enrich=pas2intron_intron_grouped_small_withnintron %>% mutate(nUniform=nIntron1.10/10, UniformNormLength=nUniform/meanSize, UnformlengthandN=UniformNormLength/nGenesum)

Enrichment is the actual normalized number/uniform number:

pas2intron_intron_grouped_small_withnintron_enrich[1,6] / pas2intron_intron_grouped_small_withnintron_enrich[1,9] 
[1] 2.688953

Look at just genes with 3 introns.

pas2intron_3introns=pas2intron %>% filter(nintron==3) %>% group_by(Intronid) %>% summarise(nBin=n(), meanSize=mean(intronLength))  %>% mutate(normNBin=nBin/meanSize)

ggplot(pas2intron_3introns,aes(y=normNBin,x=Intronid)) + geom_bar(stat = "identity") + labs(x="Intron in Gene",y="Number introns / average intron length", title="Intron position for genes with 3 introns")


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 workflowr_1.4.0

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       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.4.0     
[17] cli_1.1.0        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.3.1         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] fansi_0.4.0      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     utf8_1.1.4       stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4