Last updated: 2020-02-05

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In this vignette we will load the data, pre-process it and analyse top gene by p_beta parameter.

Filtering

Here we load raw dataset and filter it by cell, individual parameters and order genes by smallest p beta parameter

library(dplyr)
Warning: package 'dplyr' was built under R version 3.5.3

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
raw_data<-read.table("D:/Uchicago/Thesis/Real_data/scqtl-counts/scqtl-counts.txt", header = TRUE, sep = "", dec = ".")
combined_filter<-read.table("D:/Uchicago/Thesis/Real_data/combined_filter.txt", header = FALSE, sep = ",", dec = ".")
combined_filter[,1]<-combined_filter[,1]+2 #adjusting index from python to R
data<-data.frame(raw_data[,1],raw_data[,combined_filter[,1]])
colnames(data)[1]<-"gene"
mean_txt_file<-read.table("D:/Uchicago/Thesis/Real_data/mean/mean.txt", header = TRUE, sep = "", dec = ".",row.names=NULL)
gene_filter<-mean_txt_file[,c("gene","p_beta")]
data<- left_join(data, gene_filter, by = "gene")
Warning: Column `gene` joining factors with different levels, coercing to
character vector
data <- data[order(data$p_beta),]
clean_data <- data[,1:(ncol(data)-1)]

Maximum likelihood estimation for one gene

Below we take the gene with the smallest p beta and estimate the parameters for all individuals that passed quality control

library(reticulate)
Warning: package 'reticulate' was built under R version 3.5.3
library(robustbase)
Warning: package 'robustbase' was built under R version 3.5.1
source_python("C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/MLE.py")
estimate_MLE_one_gene<-function(full_data){
  data_ind<-substr(colnames(full_data),0,7)
  data_ind<-data_ind[2:length(data_ind)]
  est=matrix(, nrow = (length(unique(data_ind))-1), ncol = 3)
  info=matrix(, nrow = (length(unique(data_ind))-1), ncol = 2)
  iter=0
for (i in unique(data_ind)){
  if (i=="NA18498"){
    next}
  iter=iter+1
  x=full_data[ , grepl(i, names(full_data))]
  x=t(data.matrix(x))
  est[iter,]=MaximumLikelihood(x)
  info[iter,1]=i
  info[iter,2]=dim(x)[1]}
return(list(est,info))}
top_gene<-clean_data[1,]
x=estimate_MLE_one_gene(top_gene)
estimates=x[[1]]
info=x[[2]]
par(mfrow=c(1,3))
hist(log(estimates[,1]), main="Distribution of k_on estimates", xlab="", ylab="Number of individuals")
hist(log(estimates[,2]), main="Distribution of k_off estimates", xlab="", ylab="Number of individuals")
hist(log(estimates[,3]), main="Distribution of k_r estimates", xlab="", ylab="Number of individuals")

Distribution for one individuals

Then we take the individual with most cells (NA18501) and take a look at the estimated parameters:

The first plot is the real protein level distribution for NA18501 individual. Second and third plots are beta and poisson distributions with the estimated \(k_{on}\),\(k_{off}\) and \(k_r\) parameters.

Boxplots by genotype

We can get the genotype of the individuals and compare it to the estimated \(k_{on}\), \(k_{off}\) and \(k_{r}\) parameters.

Warning: Column `ind` joining factors with different levels, coercing to
character vector

Regression

Finally we can fit the regression to estimate linear dependance of genotype on estimated parameters.


sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
[1] robustbase_0.93-3 reticulate_1.13   dplyr_0.8.3      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       knitr_1.20       magrittr_1.5     workflowr_1.5.0 
 [5] tidyselect_0.2.5 lattice_0.20-38  R6_2.3.0         rlang_0.4.2     
 [9] stringr_1.3.1    highr_0.7        tools_3.5.0      grid_3.5.0      
[13] git2r_0.26.1     htmltools_0.3.6  yaml_2.2.0       rprojroot_1.3-2 
[17] digest_0.6.17    assertthat_0.2.0 tibble_2.1.3     crayon_1.3.4    
[21] Matrix_1.2-14    purrr_0.2.5      later_0.8.0      fs_1.3.1        
[25] promises_1.0.1   glue_1.3.0       evaluate_0.11    rmarkdown_1.10  
[29] stringi_1.1.7    DEoptimR_1.0-8   pillar_1.4.2     compiler_3.5.0  
[33] backports_1.1.2  jsonlite_1.5     httpuv_1.5.1     pkgconfig_2.0.2