Last updated: 2020-01-22

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.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.

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Rmd d886866 brimittleman 2020-01-22 add correlation between pheno DF 
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(gplots)

Attaching package: 'gplots'
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library(tidyverse)
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For this analysis I will look at correlation between the regualtory phenotype effect sizes. I need to make sure the effect sizes go in the same direction.

Read in data:

#expression: - = upreg in human
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID,Gene.name)
ExpRes=read.table("../data/DiffExpression/DEtested_allres.txt", header = F, stringsAsFactors = F, col.names = c("Gene_stable_ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, logFC)%>% rename( "Expresion_logFC"=logFC)


#apa  - = upreg in human
PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene)
apaRes= read.table("../data/DiffIso_Nuclear_DF//TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") %>% separate(cluster, into=c("chr","gene"),sep=":")
apaPASres=read.table("../data/DiffIso_Nuclear_DF//TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Human', 'Chimp','deltaPAU')) %>% filter(intron != "intron") %>% separate(intron, into=c("chr","start", "end","gene"), sep=":") 
apaPASres$start=as.integer(apaPASres$start)
apaPASres$end=as.integer(apaPASres$end)
apaPASres$deltaPAU=as.numeric(apaPASres$deltaPAU)
apaPASres=apaPASres%>% inner_join(PASMeta,by=c("chr", "start", "end", "gene"))
#problem if there are 2 pas then the are opposite but same value - do with all one direction for 
apaPASres_topPos= apaPASres %>% group_by(gene) %>% top_n(1,abs(deltaPAU)) %>% top_n(1,deltaPAU) %>% dplyr::select(gene,logef) %>% rename("Gene.name"=gene, "APA_logef"=logef)
apaPASres_topNeg= apaPASres %>% group_by(gene) %>% top_n(1,abs(deltaPAU)) %>% top_n(-1,deltaPAU) %>% dplyr::select(gene,logef)%>% rename("Gene.name"=gene, "APA_logef"=logef)


#translation:

translation=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",stringsAsFactors = F, header = T) %>% rename("Gene_stable_ID"=ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name,HvC.beta) %>% rename("ribo_beta"=HvC.beta)

#protein  
prot=read.csv("../data/Khan_prot/Khan_TableS4.csv", stringsAsFactors = F, header = T)
#need to look into which is the effect size

Join phenotypes:

Joint_pos=ExpRes %>% inner_join(apaPASres_topPos,by="Gene.name") %>% inner_join(translation, by="Gene.name") %>% dplyr::select(-Gene.name) 
Joint_pos$APA_logef=as.numeric(Joint_pos$APA_logef)

Joint_neg=ExpRes %>% inner_join(apaPASres_topNeg,by="Gene.name") %>% inner_join(translation, by="Gene.name") %>% dplyr::select(-Gene.name) 
Joint_neg$APA_logef=as.numeric(Joint_neg$APA_logef)

Correlations:

PhenoPos_corr= round(cor(Joint_pos),2)
heatmap.2(as.matrix(PhenoPos_corr),trace="none", dendrogram =c("col"), key=T)

PhenoPos_corr
                Expresion_logFC APA_logef ribo_beta
Expresion_logFC            1.00      0.04      0.63
APA_logef                  0.04      1.00      0.02
ribo_beta                  0.63      0.02      1.00
PhenoNeg_corr= round(cor(Joint_neg),2)
heatmap.2(as.matrix(PhenoNeg_corr),trace="none", dendrogram =c("col"), key=T)

PhenoNeg_corr
                Expresion_logFC APA_logef ribo_beta
Expresion_logFC            1.00      0.04      0.63
APA_logef                  0.04      1.00      0.03
ribo_beta                  0.63      0.03      1.00

These don’t look great. I will keep working on it.


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 gplots_3.0.1    workflowr_1.5.0

loaded via a namespace (and not attached):
 [1] gtools_3.8.1       tidyselect_0.2.5   haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       withr_2.1.2       
[13] glue_1.3.0         modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        caTools_1.17.1.1  
[22] evaluate_0.12      knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         KernSmooth_2.23-15
[28] promises_1.0.1     backports_1.1.2    scales_1.0.0      
[31] gdata_2.18.0       jsonlite_1.6       fs_1.3.1          
[34] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[37] grid_3.5.1         rprojroot_1.3-2    cli_1.1.0         
[40] tools_3.5.1        bitops_1.0-6       magrittr_1.5      
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[49] rstudioapi_0.10    assertthat_0.2.0   rmarkdown_1.10    
[52] httr_1.3.1         R6_2.3.0           nlme_3.1-137      
[55] git2r_0.26.1       compiler_3.5.1