Last updated: 2020-03-19

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd b6ccdbc brimittleman 2020-03-19 add new quat 
library(tidyverse)
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library(ggpubr)
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    extract

I am worried about using featureCounts. It looks like there are reads that are not counted. In this analysis, I will look at correlation between counts as well as the specific locations I have been worried about.

First, I will do this in human nuclear with the final PAS.

This is the fixed strand. The

Meta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T)
ChimpMeta=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_ChimpCoordChimpUsage.sort.bed", col.names = c("chr","start","end", "PAS", "score", "strand"),stringsAsFactors = F) %>% select(PAS)
HumanCountsFeatureCounts=read.table("../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc", header = T) %>%  separate(Geneid, into=c("disc", "PAS", "chr2", "start2", "end2", 'strand', 'geneid'), sep=":")  %>% select(-contains("_T")) %>% filter(PAS %in% Meta$PAS) 

ChimpCountsFeatureCounts=read.table("../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc", header = T) %>%  separate(Geneid, into=c("disc", "PAS", "chr2", "start2", "end2", 'strand', 'geneid'), sep=":")  %>% select(-contains("_T")) %>% right_join(ChimpMeta,by="PAS")

First use the bedtools method:

mkdir ../data/testQuant

sbatch humanMultiCov.sh

sbatch chimpMultiCov.sh
HumanDFMulticov=read.table("../data/testQuant/Human_DF_PAS.txt")

Compare:

# cor(a,b)

HumanCountsFeatureCounts_mat= as.matrix(HumanCountsFeatureCounts %>% select(contains("_N")))

HumanDFMulticov_mat=as.matrix(HumanDFMulticov[,7:12])
#PAS correlation 
Human_DFcor= cor(HumanCountsFeatureCounts_mat,HumanDFMulticov_mat)

I care about the diagonal for the matrix.

Human_DFcordiag= diag(Human_DFcor)

Human_DFcordiag
[1] 0.9839297 0.9551134 0.9668683 0.9742895 0.9781832 0.9821528

There are the correlations for individual.

ChimpDFMulticov=read.table("../data/testQuant/Chimp_DF_PAS.txt")

ChimpCountsFeatureCounts_mat= as.matrix(ChimpCountsFeatureCounts %>% select(contains("_N")))

ChimpDFMulticov_mat=as.matrix(ChimpDFMulticov[,7:12])
#PAS correlation 
Chimp_DFcor= cor(ChimpCountsFeatureCounts_mat,ChimpDFMulticov_mat)

Chimp_DFcordiag= diag(Chimp_DFcor)

Chimp_DFcordiag
[1] 0.9900335 0.9727290 0.9819526 0.9805695 0.9751067 0.9772205

Compare species

DF_Multi=as.data.frame(cbind(Chimp=Chimp_DFcordiag,Human=Human_DFcordiag)) %>% gather("Species", "Correlation")


ggplot(DF_Multi,aes(x=Species, y=Correlation, fill=Species)) + geom_boxplot() + geom_jitter() + stat_compare_means() + labs(title="Double filtered Final PAS correlation \nbetween feature counts and multiCov")

Chimp example:

human188040

Meta %>% filter(PAS=="human188040")
          PAS  disc  gene loc  chr     start       end Chimp Human
1 human188040 Human PLCL1 cds chr2 198084933 198085133 0.095 0.025
  strandFix
1         +
ChimpDFMulticov %>% filter(V4=="human188040")
     V1       V2       V3          V4    V5 V6 V7 V8 V9 V10 V11 V12
1 chr2B 84660923 84661123 human188040 0.095  +  0  0  5   1   0   0
ChimpCountsFeatureCounts %>% filter(PAS=="human188040")
   disc         PAS chr2    start2      end2 strand    geneid   Chr
1 Human human188040 chr2 198084933 198085133      - PLCL1_cds chr2B
     Start      End Strand Length X18358_N X3622_N X3659_N X4973_N pt30_N
1 84660923 84661123      -    201        0       0       5       1      0
  pt91_N
1      0
HumanDFMulticov %>% filter(V4=="human188040")
    V1        V2        V3          V4    V5 V6 V7 V8 V9 V10 V11 V12
1 chr2 198084933 198085133 human188040 0.025  +  0  2  6   3   0   2
HumanCountsFeatureCounts %>% filter(PAS=="human188040")
   disc         PAS chr2    start2      end2 strand    geneid  Chr
1 Human human188040 chr2 198084933 198085133      - PLCL1_cds chr2
      Start       End Strand Length X18498_N X18499_N X18502_N X18504_N
1 198084933 198085133      -    201        0        2        6        3
  X18510_N X18523_N
1        0        2

Same counts here. This is why it was not found in chimp. It seems it wouldnt pass the non zero filter.

Test also on the original calls. To see if it can handle this one.

I need to change bed to include both positive and negative. I can do this with python.

python bed2Bedbothstrand.py ../Human/data/inclusivePeaks/human_APApeaks.ALLChrom.bed ../data/testQuant/Human_ALLpeaks.Bothstrand.bed

python bed2Bedbothstrand.py ../Chimp/data/inclusivePeaks/chimp_APApeaks.ALLChrom.bed ../data/testQuant/Chimp_ALLpeaks.Bothstrand.bed

sbatch humanMultiCov_inclusive.sh
sbatch chimpMultiCovInclusive.sh


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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     workflowr_1.6.0  cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.26.1     compiler_3.5.1