Normalize Phenotypes Total

Last updated: 2019-10-10

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Knit directory: Comparative_APA/analysis/

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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	9855436	brimittleman	2019-10-10	fix code for pve
html	b8ccfdb	brimittleman	2019-10-09	Build site.
Rmd	1f06329	brimittleman	2019-10-09	add PCA by total and nuclear

library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

library(tidyverse)

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

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

── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

humanPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt",stringsAsFactors = F, header = T)
chimpPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Chimp_Pheno_5perc.txt",stringsAsFactors = F, header = T)


allPhenoT=humanPheno %>% full_join(chimpPheno,by="chrom") %>% select(-contains("_N"))

mkdir ../data/Pheno_5perc_total

write.table(allPhenoT, "../data/Pheno_5perc_total/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt", col.names = T, row.names = F, quote = F)


gzip ../data/Pheno_5perc_total/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt

#conda deactivate 
conda deactivate 
conda deactivate 
#python 2
source ~/activate_anaconda_python2.sh 
#go to directory ../data/Pheno_5perc_total/
python ../../code/prepare_phenotype_table.py ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz
cat ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz.phen_chr* > ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz.phen_AllChrom

Use these normalized phenotypes for the PCA

metaDataT=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F) %>% filter(Fraction=="Total")

normPheno=read.table("../data/Pheno_5perc_total/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz.phen_AllChrom", col.names = c('Chr', 'start',    'end',  'ID',   '18498_T',      '18499_T',      '18502_T',      '18504_T',  '18510_T',  '18523_T',  '18358_T','3622_T',     '3659_T',   '4973_T',   'pt30_T',       'pt91_T'))

normPheno_matrix=as.matrix(normPheno %>% select(-Chr, -start, -end, -ID))

Run PCA:

pca_Pheno=prcomp(normPheno_matrix, center=T,scale=T)
pca_df=as.data.frame(pca_Pheno$rotation) %>% rownames_to_column(var="ID")

eigs <- pca_Pheno$sdev^2
proportion = eigs/sum(eigs)

plot(proportion)

Version	Author	Date
b8ccfdb	brimittleman	2019-10-09

top5PC=pca_df %>% select(ID, PC1, PC2, PC3, PC4, PC5) %>% inner_join(metaDataT, by="ID")


ggplot(top5PC,aes(x=PC1, y=PC2, col=Species)) + geom_point(size=3) + geom_text(aes(label=Line), position = position_nudge(y = 0.03) )

Version	Author	Date
b8ccfdb	brimittleman	2019-10-09

ggplot(top5PC,aes(x=PC1, y=PC2, col=Concentration, shape=Species)) + geom_point(size=3) + geom_text(aes(label=Line), position = position_nudge(y = 0.03))

top5PC$Cycles=as.factor(top5PC$Cycles)
ggplot(top5PC,aes(x=PC1, y=PC2, col=Cycles, shape=Species)) + geom_point(size=3) + geom_text(aes(label=Library_concentration), position = position_nudge(y = 0.03))

Version	Author	Date
b8ccfdb	brimittleman	2019-10-09

Correlation between experimental factors and pcs:

PC1

Cycles1=summary(lm(top5PC$PC1 ~ top5PC$Cycles))$adj.r.squared
Species1=summary(lm(top5PC$PC1 ~ top5PC$Species))$adj.r.squared
Concentration1=summary(lm(top5PC$PC1 ~ top5PC$Concentration))$adj.r.squared
Library_concentration1=summary(lm(top5PC$PC1 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean1=summary(lm(top5PC$PC1 ~ top5PC$PerMapClean))$adj.r.squared
Ratio1=summary(lm(top5PC$PC1 ~ top5PC$Ratio))$adj.r.squared



PC1Fac=c(Species1, Concentration1, Ratio1,Cycles1,Library_concentration1, PerMapClean1)

PC2

Cycles2=summary(lm(top5PC$PC2 ~ top5PC$Cycles))$adj.r.squared
Species2=summary(lm(top5PC$PC2 ~ top5PC$Species))$adj.r.squared
Concentration2=summary(lm(top5PC$PC2 ~ top5PC$Concentration))$adj.r.squared
Library_concentration2=summary(lm(top5PC$PC2 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean2=summary(lm(top5PC$PC2 ~ top5PC$PerMapClean))$adj.r.squared
Ratio2=summary(lm(top5PC$PC2 ~ top5PC$Ratio))$adj.r.squared



PC2Fac=c(Species2, Concentration2, Ratio2,Cycles2,Library_concentration2, PerMapClean2)

PC3

Cycles3=summary(lm(top5PC$PC3 ~ top5PC$Cycles))$adj.r.squared
Species3=summary(lm(top5PC$PC3 ~ top5PC$Species))$adj.r.squared
Concentration3=summary(lm(top5PC$PC3 ~ top5PC$Concentration))$adj.r.squared
Library_concentration3=summary(lm(top5PC$PC3 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean3=summary(lm(top5PC$PC3 ~ top5PC$PerMapClean))$adj.r.squared
Ratio3=summary(lm(top5PC$PC3 ~ top5PC$Ratio))$adj.r.squared



PC3Fac=c(Species3, Concentration3, Ratio3,Cycles3,Library_concentration3, PerMapClean3)

PC4

Cycles4=summary(lm(top5PC$PC4 ~ top5PC$Cycles))$adj.r.squared
Species4=summary(lm(top5PC$PC4 ~ top5PC$Species))$adj.r.squared
Concentration4=summary(lm(top5PC$PC4 ~ top5PC$Concentration))$adj.r.squared
Library_concentration4=summary(lm(top5PC$PC4 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean4=summary(lm(top5PC$PC4 ~ top5PC$PerMapClean))$adj.r.squared
Ratio4=summary(lm(top5PC$PC4 ~ top5PC$Ratio))$adj.r.squared



PC4Fac=c(Species4, Concentration4, Ratio4,Cycles4,Library_concentration4, PerMapClean4)

PC5

Cycles5=summary(lm(top5PC$PC5 ~ top5PC$Cycles))$adj.r.squared
Species5=summary(lm(top5PC$PC5 ~ top5PC$Species))$adj.r.squared
Concentration5=summary(lm(top5PC$PC5 ~ top5PC$Concentration))$adj.r.squared
Library_concentration5=summary(lm(top5PC$PC5 ~ top5PC$Library_concentration))$adj.r.squared
PerMapClean5=summary(lm(top5PC$PC5 ~ top5PC$PerMapClean))$adj.r.squared
Ratio5=summary(lm(top5PC$PC5 ~ top5PC$Ratio))$adj.r.squared



PC5Fac=c(Species5, Concentration5, Ratio5,Cycles5,Library_concentration5, PerMapClean5)

Make DF and plot:

Exp=c('Species', 'Concentration', 'Ratio', 'Cycles','Library_concentration', 'PerMapClean')

pcandEx=as.data.frame(cbind(Experiment=Exp,pc1=PC1Fac, pc2=PC2Fac,  pc3=PC3Fac,  pc4=PC4Fac,  pc5=PC5Fac))

pcandExM=melt(pcandEx, id.var="Experiment",variable.name = "PC", value.name = "PVE")

Warning: attributes are not identical across measure variables; they will
be dropped

pcandExM$PVE=as.numeric(pcandExM$PVE)

ggplot(pcandExM, aes(x=PC, fill=PVE, y=Experiment))+ geom_tile() + labs(title="Proportion of variation explained in PCs by Experimental Faction \n Total", y="")+ scale_fill_distiller(palette = "Blues", direction=1)

ggplot(top5PC,aes(x=Species,y=Concentration)) + geom_boxplot(alpha=.5)+ geom_jitter(aes(col=Cycles)) + stat_compare_means(method="t.test") + labs(title="Total Fraction")

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] tidyverse_1.2.1 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  

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        pillar_1.3.1      
[10] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[13] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       workflowr_1.4.0   
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         broom_0.5.1       
[25] Rcpp_1.0.2         scales_1.0.0       backports_1.1.2   
[28] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[31] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[34] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[37] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[40] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[43] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[46] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[49] git2r_0.25.2       compiler_3.5.1

Normalize Phenotypes Total

Briana Mittleman

10/9/2019