Last updated: 2019-09-04

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    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
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/signalsiteanalysis.Rmd/figure1bMain-1.pdf
    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 41d1961 brimittleman 2019-09-04 wflow_publish(c(“analysis/signalsiteanalysis.Rmd”, “analysis/corrbetweenind.Rmd”,
html db6b12d brimittleman 2019-08-05 Build site.
Rmd c39919d brimittleman 2019-08-05 update pc plots
html 957c8f5 brimittleman 2019-06-13 Build site.
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',  'NA18519',  'NA18520',  'NA18522',  'NA18852', 'NA18853',   'NA18855',  'NA18856',  'NA18858',  'NA18861',  'NA18862',  'NA18870',  'NA18907',  'NA18909',  'NA18912', 'NA18913',   'NA18916',  'NA19092',  'NA19093',  'NA19101',  'NA19119',  '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)

Version Author Date
957c8f5 brimittleman 2019-06-13
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',  'NA18519',  'NA18520',  'NA18522',  'NA18852', 'NA18853',   'NA18855',  'NA18856',  'NA18858',  'NA18861',  'NA18862',  'NA18870',  'NA18907',  'NA18909',  'NA18912', 'NA18913',   'NA18916',  'NA19092',  'NA19093',  'NA19101',  'NA19119',  '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)

Version Author Date
957c8f5 brimittleman 2019-06-13

Plot together:

both_prop=as.data.frame(cbind(PCs=seq(1,54,1),Total=proportion_tot,Nuclear=proportion_nuc))
Warning in cbind(PCs = seq(1, 54, 1), Total = proportion_tot, Nuclear
= proportion_nuc): number of rows of result is not a multiple of vector
length (arg 2)
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
957c8f5 brimittleman 2019-06-13
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")

Version Author Date
957c8f5 brimittleman 2019-06-13

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,ratio260_280)
metadata_nuc=metadata %>% filter(fraction=="nuclear") %>% select(batch,Sex, alive_avg, undiluted_avg,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")

Version Author Date
db6b12d brimittleman 2019-08-05
957c8f5 brimittleman 2019-06-13
covariate_pc_pve_heatmap(pca_nuc_df,metadata_nuc, title="Nuclear PCs")

Version Author Date
db6b12d brimittleman 2019-08-05

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 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        workflowr_1.4.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 labeling_0.3     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4