Last updated: 2020-02-17

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Knit directory: Thesis_single_RNA/

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C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/final_genotype_data.txt analysis/final_genotype_data.txt
C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/clean_dataset_for_analysis.txt analysis/clean_dataset_for_analysis.txt
C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/MLE.py analysis/MLE.py

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    Modified:   Data_sim.Rmd
    Modified:   analysis/Chapter_2_analysis.Rmd
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final_genotypes<-read.table("C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/final_genotype_data.txt", header = T, sep = "\t")
clean_data<-read.table("C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/clean_dataset_for_analysis.txt", header = T, sep = "\t")
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
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
source_python("C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/MLE.py")
ENSG00000197728_genotype<-final_genotypes[2,]
ENSG00000197728_data<-clean_data[2,]
estimate_MLE_one_gene_with_boot<-function(full_data,boot_n){
  data_ind<-substr(colnames(full_data),0,7)
  data_ind<-data_ind[2:length(data_ind)]
  bootstrap<-list()
  iter=0
for (i in unique(data_ind)){
  if (i=="NA18498"){
    next}
  iter=iter+1
  est=matrix(, boot_n, ncol = 3)
  x=full_data[ , grepl(i, names(full_data))]
  x=t(data.matrix(x))
  for (j in 1:boot_n){
    boot_x = as.matrix(x[sample(nrow(x),nrow(x),replace=TRUE)])
    est[j,]=MaximumLikelihood(boot_x)}
  bootstrap[[iter]]<-est}
return(bootstrap)}
test<-estimate_MLE_one_gene_with_boot(ENSG00000197728_data,boot_n=100)
weights_matrix=matrix(, 53, ncol = 3)
for (i in 1:53){
  current_indiv<-test[[i]]
  weights_matrix[i,1]=1/sqrt(var(current_indiv[,1],na.rm=TRUE))
  weights_matrix[i,2]=1/sqrt(var(current_indiv[,2],na.rm=TRUE))
  weights_matrix[i,3]=1/sqrt(var(current_indiv[,3],na.rm=TRUE))}
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]}
estimates<-data.frame(est,info)
colnames(estimates)<-c("k_on","k_off","k_r","ind","n_cells")
return(estimates)}
MLE_and_regression_for_multiple_genes<-function(dataset, current_genotype){
  estimates<-estimate_MLE_one_gene(dataset)
  current_genotype_for_analysis<-t(current_genotype)
  current_genotype_for_analysis<-data.frame(colnames(current_genotype),current_genotype_for_analysis)
  colnames(current_genotype_for_analysis)<-c("ind","genotype")
  estimates<-left_join(estimates,data.frame(current_genotype_for_analysis), by = "ind")
  estimates$genotype<-as.numeric(as.character(estimates$genotype))
  #model_k_on<-lm(estimates$k_on~estimates$genotype,weights=weights[,1])
  #model_k_off<-lm(estimates$k_off~estimates$genotype,weights=weights[,2])
  #model_k_r<-lm(estimates$k_r~estimates$genotype,weights=weights[,3])
  return(estimates)
}
estimates<-MLE_and_regression_for_multiple_genes(ENSG00000197728_data,ENSG00000197728_genotype)
Warning: Column `ind` joining factors with different levels, coercing to
character vector
model_k_on_w<-lm(estimates$k_on~estimates$genotype,weights=weights_matrix[,1])
model_k_off_w<-lm(estimates$k_off~estimates$genotype,weights=weights_matrix[,2])
model_k_r_w<-lm(estimates$k_r~estimates$genotype,weights=weights_matrix[,3])
summary(model_k_on_w)

Call:
lm(formula = estimates$k_on ~ estimates$genotype, weights = weights_matrix[, 
    1])

Weighted Residuals:
    Min      1Q  Median      3Q     Max 
-1.5298 -0.3529  0.2347  0.7357  2.3186 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         1.21554    0.07705  15.775   <2e-16 ***
estimates$genotype  0.53431    0.21551   2.479   0.0165 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8717 on 51 degrees of freedom
Multiple R-squared:  0.1076,    Adjusted R-squared:  0.09006 
F-statistic: 6.147 on 1 and 51 DF,  p-value: 0.01651
summary(model_k_off_w)

Call:
lm(formula = estimates$k_off ~ estimates$genotype, weights = weights_matrix[, 
    2])

Weighted Residuals:
   Min     1Q Median     3Q    Max 
-3.432 -0.066  0.380  0.805 52.788 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)          0.7159     1.1456   0.625    0.535
estimates$genotype   1.6895     3.7560   0.450    0.655

Residual standard error: 9.543 on 51 degrees of freedom
Multiple R-squared:  0.003952,  Adjusted R-squared:  -0.01558 
F-statistic: 0.2023 on 1 and 51 DF,  p-value: 0.6548
summary(model_k_r_w)

Call:
lm(formula = estimates$k_r ~ estimates$genotype, weights = weights_matrix[, 
    3])

Weighted Residuals:
   Min     1Q Median     3Q    Max 
-6.424 -0.443  0.975  2.765 39.209 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)          21.751      2.066   10.53 2.15e-14 ***
estimates$genotype   -1.678      5.414   -0.31    0.758    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.124 on 51 degrees of freedom
Multiple R-squared:  0.001881,  Adjusted R-squared:  -0.01769 
F-statistic: 0.09609 on 1 and 51 DF,  p-value: 0.7578
model_k_on<-lm(estimates$k_on~estimates$genotype)
model_k_off<-lm(estimates$k_off~estimates$genotype)
model_k_r<-lm(estimates$k_r~estimates$genotype)
summary(model_k_on)

Call:
lm(formula = estimates$k_on ~ estimates$genotype)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2954 -0.4779 -0.2745  0.2301  6.7212 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)          1.4056     0.2389   5.884 3.12e-07 ***
estimates$genotype   2.0121     0.3786   5.315 2.37e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.546 on 51 degrees of freedom
Multiple R-squared:  0.3565,    Adjusted R-squared:  0.3438 
F-statistic: 28.25 on 1 and 51 DF,  p-value: 2.371e-06
summary(model_k_off)

Call:
lm(formula = estimates$k_off ~ estimates$genotype)

Residuals:
    Min      1Q  Median      3Q     Max 
-370.79   15.02   15.71   16.62  628.46 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -14.78      20.54  -0.720    0.475    
estimates$genotype   193.16      32.55   5.935 2.59e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 132.9 on 51 degrees of freedom
Multiple R-squared:  0.4085,    Adjusted R-squared:  0.3969 
F-statistic: 35.22 on 1 and 51 DF,  p-value: 2.593e-07
summary(model_k_r)

Call:
lm(formula = estimates$k_r ~ estimates$genotype)

Residuals:
    Min      1Q  Median      3Q     Max 
-253.97   -0.49    5.03   11.63  464.30 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)           18.51      14.33   1.292    0.202    
estimates$genotype   120.47      22.71   5.305 2.46e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 92.73 on 51 degrees of freedom
Multiple R-squared:  0.3556,    Adjusted R-squared:  0.3429 
F-statistic: 28.14 on 1 and 51 DF,  p-value: 2.458e-06

Beta-Poisson PDf for 53 individuals per genotype

library(reticulate)
library(robustbase)
source_python("C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/MLE.py")
plot((dBP(matrix(seq(0,60,by=1)),estimates[1,1],estimates[1,2],estimates[1,3])),type='l',col='green', main="Beta-Poisson PDF for 53 individuals", ylab="Density", xlab="Number of protein molecules", ylim=c(0,0.2))
for(i in 2:nrow(estimates)){
  if (estimates[i,6]==2){
    color="red"}
  if (estimates[i,6]==1){
    color="blue"}
  if (estimates[i,6]<1){
    color="green"}
  lines((dBP(matrix(seq(0,60,by=1)),estimates[i,1],estimates[i,2],estimates[i,3])),type='l', col=color)
}
legend(45, 0.2, legend=c("Genotype=2", "Genotype=1", "Genotype<1"),
       col=c("red", "blue", "green"),lty=1:1)

CDF for 53 individuals:

library(reticulate)
library(robustbase)
source_python("C:/Users/Moonkin/Documents/GitHub/Thesis_single_RNA/analysis/MLE.py")
plot(cumsum(dBP(matrix(seq(0,60,by=1)),estimates[1,1],estimates[1,2],estimates[1,3])),type='l',col='green', main="Beta-Poisson CDF for 53 individuals", ylab="CDF", xlab="")
for(i in 2:nrow(estimates)){
  if (estimates[i,6]==2){
    color="red"}
  if (estimates[i,6]==1){
    color="blue"}
  if (estimates[i,6]<1){
    color="green"}
  lines(cumsum(dBP(matrix(seq(0,60,by=1)),estimates[i,1],estimates[i,2],estimates[i,3])),type='l', col=color)
}


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] dplyr_0.8.3       robustbase_0.93-3 reticulate_1.13  

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  assertthat_0.2.0 yaml_2.2.0      
[17] rprojroot_1.3-2  digest_0.6.17    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    pillar_1.4.2     DEoptimR_1.0-8   compiler_3.5.0  
[33] backports_1.1.2  jsonlite_1.5     httpuv_1.5.1     pkgconfig_2.0.2