Data set from Luis Lab

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Last updated: 2021-09-10

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Introduction

“The idea is that gene expression at a given gene might be associated wit the marks in parts of the genome near-by that gene. But we don’t know in advance exactly where to look for that signal - just nearby the gene. And the signal might be at different places for different marks or the same place.”

“So by running factor analysis I hope to automatically identify factors representing the things that covary… eg co-variation of gene expression with certain marks”

Try to look at some data examples.

CMC2

CMC2 is a protein coding gene, with 10 exons.

library(readr)
CMC2_ATACseq = read_csv("data/luis/CMC2_ATACseq.csv")

Warning: Missing column names filled in: 'X1' [1]

── Column specification ────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  locus = col_character()
)
ℹ Use `spec()` for the full column specifications.

CMC2_RNAseq = read_csv("data/luis/CMC2_RNAseq.csv")

Warning: Missing column names filled in: 'X1' [1]

── Column specification ────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  locus = col_character()
)
ℹ Use `spec()` for the full column specifications.

CMC2_H3K4me1 = read_csv("data/luis/CMC2_H3K4me1.csv")

Warning: Missing column names filled in: 'X1' [1]

── Column specification ────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  locus = col_character()
)
ℹ Use `spec()` for the full column specifications.

CMC2_ATACseq = do.call(cbind.data.frame, CMC2_ATACseq)
CMC2_RNAseq = do.call(cbind.data.frame, CMC2_RNAseq)
CMC2_H3K4me1 = do.call(cbind.data.frame, CMC2_H3K4me1)

dim(CMC2_ATACseq)

[1] 426090     72

dim(CMC2_RNAseq)

[1] 426090     80

dim(CMC2_H3K4me1)

[1] 426090     56

What do data look like?

plot(CMC2_ATACseq$AF08_Flu,type='l',xlab = 'locus',ylab='',main = 'CMC2 ATAC AF08_Flu')

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5450d94	Dongyue Xie	2021-09-10

plot(CMC2_RNAseq$AF08_Flu,type='l',xlab = 'locus',ylab='',main = 'CMC2 RNA AF08_Flu')

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plot(CMC2_H3K4me1$AF08_Flu_H3K4me1,type='l',xlab = 'locus',ylab='',main = 'CMC2 H3K4me1 AF08_Flu')

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How sparse the data are?

sparse_prop = function(Y){
  sum(Y==0)/prod(dim(Y))
}
sparse_prop(CMC2_ATACseq[,-c(1,2)])

[1] 0.9674908

sparse_prop(CMC2_RNAseq[,-c(1,2)])

[1] 0.9738769

sparse_prop(CMC2_H3K4me1[,-c(1,2)])

[1] 0.9594024

Get overlapping individuals

indis_ATAC = (colnames(CMC2_ATACseq))[-c(1,2)]
indis_RNA = (colnames(CMC2_RNAseq))[-c(1,2)]
indis_H3K4me1 = (colnames(CMC2_H3K4me1))[-c(1,2)]
for(i in 1:length(indis_H3K4me1)){
  name_i = strsplit(indis_H3K4me1[i],split = '_')[[1]]
  indis_H3K4me1[i] = paste(name_i[1],name_i[2],sep = '_')
}
indis = intersect(intersect(indis_ATAC,indis_RNA),indis_H3K4me1)
indis

 [1] "AF08_Flu" "AF08_NI"  "AF12_Flu" "AF12_NI"  "AF16_Flu" "AF16_NI" 
 [7] "AF18_Flu" "AF18_NI"  "AF20_Flu" "AF20_NI"  "AF22_Flu" "AF22_NI" 
[13] "AF24_Flu" "AF24_NI"  "AF26_Flu" "AF26_NI"  "AF28_Flu" "AF28_NI" 
[19] "AF30_Flu" "AF30_NI"  "AF34_Flu" "AF34_NI"  "AF36_Flu" "AF36_NI" 
[25] "AF38_Flu" "AF38_NI"  "EU03_Flu" "EU03_NI"  "EU05_Flu" "EU05_NI" 
[31] "EU07_Flu" "EU07_NI"  "EU09_Flu" "EU09_NI"  "EU13_Flu" "EU13_NI" 
[37] "EU15_Flu" "EU15_NI"  "EU19_Flu" "EU19_NI"  "EU21_Flu" "EU21_NI" 
[43] "EU25_Flu" "EU25_NI"  "EU27_Flu" "EU27_NI"  "EU33_Flu" "EU33_NI" 
[49] "EU39_Flu" "EU39_NI"  "EU41_Flu" "EU41_NI"  "EU47_Flu" "EU47_NI" 

length(indis)

[1] 54

Only use NI individuals.

indis_NI = c()
for(i in 1:length(indis)){
  id = strsplit(indis[i],split='_')[[1]][2]
  if(id=='NI'){
    indis_NI = c(indis_NI,indis[i])
  }
}

Try to run flashier on [RNA seq, H3K4me1, ATAC]

Y_RNA = t(CMC2_RNAseq[,match(indis_NI,indis_RNA)+2])
Y_H3K4 = t(CMC2_H3K4me1[,match(indis_NI,indis_H3K4me1)+2])
Y_ATAC = t(CMC2_ATACseq[,match(indis_NI,indis_ATAC)+2])

Increase 200kb window to 2000kb to reduce the size of matrix

Y_RNAr = do.call(cbind, by(t(Y_RNA), (seq(ncol(Y_RNA)) - 1) %/% 10, FUN = colSums))
Y_H3K4r = do.call(cbind, by(t(Y_H3K4), (seq(ncol(Y_H3K4)) - 1) %/% 10, FUN = colSums))
Y_ATACr = do.call(cbind, by(t(Y_ATAC), (seq(ncol(Y_ATAC)) - 1) %/% 10, FUN = colSums))

library(flashier)
Y = cbind(Y_RNAr,Y_H3K4r,Y_ATACr)
dim(Y)
Y = log(Y+1e-4)
# fit_flash = flashier::flash(Y,var.type = 1)
# saveRDS(fit_flash,'output/flash_on_cmc2_reduced_2000kb.rds')

#fit_flash_by_row = flashier::flash(Y,var.type = 1,greedy.Kmax=5)

ploter.flash.factor = function(fit_flash,k=1,p){
  
  #plot(fit_flash$loadings.pm[[2]][,k],type='l',xlab='locus',ylab='',main=paste('factor ',k))
  par(mfrow=c(3,1))
  plot(fit_flash$loadings.pm[[2]][1:(p/3),k],type='l',
       xlab='',ylab='',main=paste('factor ',k, ', RNA'),col=2)
  plot(fit_flash$loadings.pm[[2]][((p/3+1):(p/3*2)),k],type='l',
       xlab='',ylab='',main='H3K4me1',col=3)
  plot(fit_flash$loadings.pm[[2]][((p/3*2+1):(p)),k],type='l',
       xlab='locus',ylab='',main='ATAC',col=4)
  
}

ploter.flash.loading = function(fit_flash,K){
  par(mfrow=c(ceiling(K/2),2))
  for(k in 1:K){
    plot(fit_flash$loadings.pm[[1]][,k],type='h',xlab='individuals',ylab='',main=paste('loading ',k))
  }
  
}

fit_flash = readRDS('output/flash_on_cmc2_reduced_2000kb.rds')
plot(fit_flash$pve,type='p',pch=20,xlab = 'k',ylab='pve')

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826e06f	Dongyue Xie	2021-09-10
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ploter.flash.loading(fit_flash,10)

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for(k in 1:10){
  ploter.flash.factor(fit_flash,k,127827)
}

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Try to fit the wave ebmf

source('code/wave_ebmf.R')

Loading required package: MASS

WaveThresh: R wavelet software, release 4.6.8, installed

Copyright Guy Nason and others 1993-2016

Note: nlevels has been renamed to nlevelsWT

Loading flashr

Registered S3 method overwritten by 'flashr':
  method      from    
  print.flash flashier

#rm.idx = c(1:4921,(42609-4920+1):42609)
#Y = cbind(Y_RNAr[,-rm.idx],Y_H3K4r[,-rm.idx],Y_ATACr[,-rm.idx])
# fit_wave = wave_ebmf(Y,Kmax=10,s=fit_flash$residuals.sd)
# saveRDS(fit_wave,'output/wave_flash_on_cmc2_reduced_2000kb.rds')
fit_wave = readRDS('output/wave_flash_on_cmc2_reduced_2000kb.rds')
par(mfrow=c(1,1))
plot(fit_wave$pve,type='p',pch=20,xlab = 'k',ylab='pve')

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Version	Author	Date
ed30472	Dongyue Xie	2021-09-10
826e06f	Dongyue Xie	2021-09-10

ploter.wave.flash.factor = function(fit_wave,k=1,p){
  
  #plot(fit_flash$loadings.pm[[2]][,k],type='l',xlab='locus',ylab='',main=paste('factor ',k))
  par(mfrow=c(3,1))
  plot(fit_wave$ldf$f[1:(p/3),k],type='l',
       xlab='',ylab='',main=paste('factor ',k, ', RNA'),col=2)
  plot(fit_wave$ldf$f[((p/3+1):(p/3*2)),k],type='l',
       xlab='',ylab='',main='H3K4me1',col=3)
  plot(fit_wave$ldf$f[((p/3*2+1):(p)),k],type='l',
       xlab='locus',ylab='',main='ATAC',col=4)
  
}
ploter.wave.flash.loading = function(fit_wave,K){
  par(mfrow=c(ceiling(K/2),2))
  for(k in 1:K){
    plot(fit_wave$ldf$l[,k],type='h',xlab='individuals',ylab='',main=paste('loading ',k))
  }
  
}

ploter.wave.flash.loading(fit_wave,10)

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ed30472	Dongyue Xie	2021-09-10

for(k in 1:10){
  ploter.wave.flash.factor(fit_wave,k,127827)
}

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FAM220A

FAM220A is a protein coding gene with 2 exons.

FAM220A_ATACseq = read_csv("data/luis/FAM220A_ATACseq.csv")
FAM220A_RNAseq = read_csv("data/luis/FAM220A_RNAseq.csv")
FAM220A_H3K4me1 = read_csv("data/luis/FAM220A_H3K4me1.csv")

FAM220A_ATACseq = do.call(cbind.data.frame, FAM220A_ATACseq)
FAM220A_RNAseq = do.call(cbind.data.frame, FAM220A_RNAseq)
FAM220A_H3K4me1 = do.call(cbind.data.frame, FAM220A_H3K4me1)

dim(FAM220A_ATACseq)
dim(FAM220A_RNAseq)
dim(FAM220A_H3K4me1)

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] flashr_0.6-7     testthat_3.0.0   wavethresh_4.6.8 MASS_7.3-53     
[5] readr_1.4.0      workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] pkgload_1.1.0     splines_4.0.3     assertthat_0.2.1  horseshoe_0.2.0  
 [5] mixsqp_0.3-43     deconvolveR_1.2-1 yaml_2.2.1        remotes_2.2.0    
 [9] sessioninfo_1.1.1 ebnm_0.1-50       pillar_1.4.6      backports_1.1.10 
[13] lattice_0.20-41   glue_1.4.2        digest_0.6.27     promises_1.1.1   
[17] colorspace_1.4-1  plyr_1.8.6        htmltools_0.5.1.1 httpuv_1.5.4     
[21] Matrix_1.2-18     pkgconfig_2.0.3   devtools_2.3.2    invgamma_1.1     
[25] purrr_0.3.4       scales_1.1.1      processx_3.5.1    whisker_0.4      
[29] later_1.1.0.1     git2r_0.27.1      tibble_3.0.4      generics_0.1.0   
[33] ggplot2_3.3.2     usethis_1.6.3     ellipsis_0.3.1    withr_2.3.0      
[37] ashr_2.2-47       cli_2.4.0         magrittr_2.0.1    crayon_1.3.4     
[41] memoise_1.1.0     evaluate_0.14     ps_1.4.0          fs_1.5.0         
[45] truncnorm_1.0-8   pkgbuild_1.1.0    tools_4.0.3       prettyunits_1.1.1
[49] softImpute_1.4    hms_0.5.3         REBayes_2.2       lifecycle_1.0.0  
[53] stringr_1.4.0     munsell_0.5.0     trust_0.1-8       flashier_0.2.9   
[57] irlba_2.3.3       callr_3.6.0       compiler_4.0.3    rlang_0.4.10     
[61] grid_4.0.3        rstudioapi_0.11   rmarkdown_2.5     gtable_0.3.0     
[65] DBI_1.1.0         reshape2_1.4.4    R6_2.4.1          knitr_1.30       
[69] dplyr_1.0.5       rprojroot_1.3-2   desc_1.2.0        stringi_1.5.3    
[73] parallel_4.0.3    SQUAREM_2020.5    Rcpp_1.0.5        vctrs_0.3.7      
[77] tidyselect_1.1.0  xfun_0.18