Last updated: 2019-06-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/motifDisruption.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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 6fea690 brimittleman 2019-06-13 fix big bug
html f1c3fb0 brimittleman 2019-05-09 Build site.
Rmd 1c60a3a brimittleman 2019-05-09 add metadata
html 144c00b brimittleman 2019-05-08 Build site.
Rmd 5e39f1c brimittleman 2019-05-08 choose pcs and start qtl rerun
html f5af9c6 brimittleman 2019-05-08 Build site.
Rmd 1ba7d2b brimittleman 2019-05-08 add pca 
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()
library(reshape2)

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

    smiths

Concatinate qqnorm res:

less APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr*.gz > APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm.allChrom
less APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr*.gz > APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm.allChrom
totalqqnorm=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm.allChrom", col.names = c('Chr',  'start',    'end',  'ID',   'NA18486',  'NA18498',  'NA18499',  'NA18501',  'NA18502',  'NA18504', 'NA18505',   'NA18508',  'NA18510',  'NA18511',  'NA18516',  'NA18517',  'NA18519',  'NA18520',  'NA18522',  'NA18852', 'NA18853',   'NA18855',  'NA18856',  'NA18858',  'NA18861',  'NA18862',  'NA18870',  'NA18907',  'NA18909',  'NA18912', 'NA18913',   'NA18916',  'NA19092',  'NA19093',  'NA19101',  'NA19119',  'NA19128',  'NA19130',  'NA19131',  'NA19137','NA19138',    'NA19140',  'NA19141',  'NA19144',  'NA19152',  'NA19153',  'NA19160',  'NA19171',  'NA19193',  'NA19200','NA19207',    'NA19209',  'NA19210',  'NA19223',  'NA19225',  'NA19238',  'NA19239',  'NA19257') )

totalqqnorm_matrix=as.matrix(totalqqnorm %>% select(-Chr, -start, -end, -ID))

RUn PCA:

pca_tot_peak=prcomp(totalqqnorm_matrix, center=T,scale=T)
pca_tot_df=as.data.frame(pca_tot_peak$rotation) %>% rownames_to_column(var="lib") %>% select(1:11)

pca_tot_df_fix=bind_cols(line=pca_tot_df[,dim(pca_tot_df)[[2]]],pca_tot_df[,3:dim(pca_tot_df)[[2]]-1])

Variance explained:

eigs_tot <- pca_tot_peak$sdev^2
proportion_tot = eigs_tot/sum(eigs_tot)

plot(proportion_tot)

nuclearqqnorm=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm.allChrom", col.names = c('Chr',  'start',    'end',  'ID',   'NA18486',  'NA18498',  'NA18499',  'NA18501',  'NA18502',  'NA18504', 'NA18505',   'NA18508',  'NA18510',  'NA18511',  'NA18516',  'NA18517',  'NA18519',  'NA18520',  'NA18522',  'NA18852', 'NA18853',   'NA18855',  'NA18856',  'NA18858',  'NA18861',  'NA18862',  'NA18870',  'NA18907',  'NA18909',  'NA18912', 'NA18913',   'NA18916',  'NA19092',  'NA19093',  'NA19101',  'NA19119',  'NA19128',  'NA19130',  'NA19131',  'NA19137','NA19138',    'NA19140',  'NA19141',  'NA19144',  'NA19152',  'NA19153',  'NA19160',  'NA19171',  'NA19193',  'NA19200','NA19207',    'NA19209',  'NA19210',  'NA19223',  'NA19225',  'NA19238',  'NA19239',  'NA19257'))

nuclearqqnorm_matrix=as.matrix(nuclearqqnorm %>% select(-Chr, -start, -end, -ID))
pca_nuc_peak=prcomp(nuclearqqnorm_matrix, center=T,scale=T)
pca_nuc_df=as.data.frame(pca_nuc_peak$rotation) %>% rownames_to_column(var="lib") %>% select(1:11)

pca_nuc_df_fix=bind_cols(line=pca_nuc_df[,dim(pca_nuc_df)[[2]]],pca_nuc_df[,3:dim(pca_nuc_df)[[2]]-1])

Variance explained:

eigs_nuc <- pca_nuc_peak$sdev^2
proportion_nuc = eigs_nuc/sum(eigs_nuc)

plot(proportion_nuc)

Plot together:

both_prop=as.data.frame(cbind(PCs=seq(1,54,1),Total=proportion_tot,Nuclear=proportion_nuc))

both_prop_melt=melt(both_prop, id.var=c("PCs"), variable.name="Fraction",value.name = "VariationExplained" )
ggplot(both_prop_melt, aes(x=PCs, y=VariationExplained,group=Fraction, color=Fraction)) + geom_line() + geom_vline(xintercept = 6, col="red") + annotate("text", label="6 PCs", x=10, y=.1) + labs(title="Proportion of variance explained \nin PCA on normalized APA usage")

Version Author Date
144c00b brimittleman 2019-05-08 
both_prop_melt_filt=both_prop_melt %>% filter(PCs<10)

ggplot(both_prop_melt_filt, aes(x=PCs, y=VariationExplained,group=Fraction, color=Fraction)) + geom_line() + geom_vline(xintercept = 4, col="red") + annotate("text", label="4 PCs", x=5, y=.1) + labs(title="Proportion of variance explained \nin PCA on normalized APA usage")

WHich factors correlate with PCs:

metadata=read.table("../data/MetaDataSequencing.txt", stringsAsFactors = F, header = T)
metadata_tot=metadata %>% filter(fraction=="total") %>% select(batch,Sex, alive_avg, undiluted_avg, library_conc,ratio260_280)
metadata_nuc=metadata %>% filter(fraction=="nuclear") %>% select(batch,Sex, alive_avg, undiluted_avg, library_conc,ratio260_280)

Function from Ben

covariate_pc_pve_heatmap <- function(pc_df, covariate_df, title) {
  # Load in data
  pcs <- pc_df
  #pcs=pca_tot_df
  covs <- covariate_df
  #covs=metadata_tot

# Remove unimportant columns
  pcs <- as.matrix(pcs[,2:dim(pcs)[[2]]])
  covs <- data.frame(as.matrix(covs[,1:dim(covs)[[2]]]))

  # Initialize PVE heatmap
  pve_map <- matrix(0, dim(covs)[2], dim(pcs)[2])
  colnames(pve_map) <- colnames(pcs)
  rownames(pve_map) <- colnames(covs)

  # Loop through each PC, COV Pair and take correlation
  num_pcs <- dim(pcs)[2]
  num_covs <- dim(covs)[2]
  for (num_pc in 1:num_pcs) {
    for (num_cov in 1:num_covs) {
      pc_vec <- pcs[,num_pc]
      cov_vec <- covs[,num_cov]
      lin_model <- lm(pc_vec ~ cov_vec)
      pve_map[num_cov, num_pc] <- summary(lin_model)$adj.r.squared
      if (pve_map[num_cov, num_pc] <0){pve_map[num_cov, num_pc]=0}
    }
  }
  pve_map
  ord <- hclust( dist(scale(pve_map), method = "euclidean"), method = "ward.D" )$order

  melted_mat <- melt(pve_map)
  colnames(melted_mat) <- c("Covariate", "PC","PVE")

  #  Use factors to represent covariate and pc name
  melted_mat$Covariate <- factor(melted_mat$Covariate, levels = rownames(pve_map)[ord])
  melted_mat$PC <- factor(melted_mat$PC)
  if (dim(pcs)[2] == 10) {
    levels(melted_mat$PC) <- c(levels(melted_mat$PC)[1],levels(melted_mat$PC)[3:10],levels(melted_mat$PC)[2])
  }
  if (dim(pcs)[2] == 21) {
    levels(melted_mat$PC) <- c(levels(melted_mat$PC)[1],levels(melted_mat$PC)[12],levels(melted_mat$PC)[15:21],levels(melted_mat$PC)[2:11], levels(melted_mat$PC)[13:14])
  }

  #  PLOT!
  heatmap <- ggplot(data=melted_mat, aes(x=Covariate, y=PC)) + geom_tile(aes(fill=PVE)) + scale_fill_gradient2(midpoint=-.05, guide="colorbar")
  heatmap <- heatmap + theme(text = element_text(size=14), panel.background = element_blank(), axis.text.x = element_text(angle = 90, vjust=.5))
  heatmap <- heatmap + labs(y="latent factor", title=title)

  # Save File
  return(heatmap)
}
covariate_pc_pve_heatmap(pca_tot_df,metadata_tot, title="Total PCs")

summary(lm(pca_tot_df$PC6~metadata_tot$library_conc))

Call:
lm(formula = pca_tot_df$PC6 ~ metadata_tot$library_conc)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.45681 -0.07200  0.04265  0.08592  0.20909 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)
(Intercept)               -0.024988   0.026649  -0.938    0.353
metadata_tot$library_conc  0.003065   0.003704   0.828    0.412

Residual standard error: 0.1375 on 52 degrees of freedom
Multiple R-squared:  0.013, Adjusted R-squared:  -0.005983 
F-statistic: 0.6848 on 1 and 52 DF,  p-value: 0.4117
#covariate_pc_pve_heatmap(pca_nuc_df,metadata_nuc, title="Nuclear PCs")

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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       workflowr_1.3.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.3.1      cli_1.0.1        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.2.6        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         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     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4