Ymean_compare: Compare the infered Y_mean values with the true values

In iFad: An integrative factor analysis model for drug-pathway association inference

Description

Calculate the RMSE (root-mean-sqaured-error) of the inferred mean of matrixY1 and matrixY2 compared with the original true means of matrixY1 and Y2

Usage

Ymean_compare(Y1_mean,Y2_mean,matrixY1, matrixY2, 
matrixZ_chain, matrixW1, matrixW2, matrixW_chain, 
matrixX, matrixX_chain, result_file_name, plot_name)

Arguments

Y1_mean,Y2_mean	The matrix of the mean value of matrixY before adding the noise term. Calculated by the multiplication of matrixW and matrixX. Dim(Y1_mean)=G1*J,Dim(Y2_mean)=G2*J.
matrixY1,matrixY2	The paired gene expression and drug response matrix measured across the same set of samples (cell lines). Dim(matrixY1)=G1*J. Dim(matrixY2)=G2*J.
matrixZ_chain	The updated matrixZ in each recorded iteration. A list of length 2, for matrixZ1 and matrixZ2 respectively. matrixZ_chain[[1]] and matrixZ2[[2]] are both matrices with dimension A*B, whereas A is the number of recorded iterations and B the length of matrixZ1/Z2. Each row of the matrix corresponds to the vectorized matrixZ1 or matrixZ2 in each iteration.
matrixW1,matrixW2	The factor loading matrix representing the degree of influence of the latent factors on individual genes. Dim(matrixW1)=G1*K; Dim(matrixW2)=G2*K. Real continuous value with mean 0 and SD 1.
matrixW_chain	The updated matrixW in each recorded iteration, with format similar to matrixZ_chain.
matrixX	The factor activity matrix with dimension K*J. matrixX[k,j] is the activity value of the k-th latent factor (e.g., pathway) in the j-th sample (e.g., cell line). Real continuous value with mean 0 and SD 1.
matrixX_chain	The updated matrixX in each recorded iteration. An A*B matrix with each row corresponding to the vectorized matrixX in each recorded iteration.
result_file_name	Name of the ".RData" file for storing the RMSE related result
plot_name	Name of the ".pdf" file for plotting RMSE

Value

The generated .RData file includes the following components:

scaled_Y1_mean,scaled_Y2_mean	The mean of matrixY1 and Y2 after data normalization (prior to the Gibbs sampling, the original matrixY1 and matrixY2 are normalized, so their mean will change), with dimension the same to that of matrixY1 and matrixY2
est_Y1_mean_chain,est_Y2_mean_chain	Estimated mean of matrixY1 and matrixY2 in each iteration of the Gibbs sampling algorithm.Each row corresponds to the vectorized estimated mean of matrixY1 or matrixY2
RMSE_Y_MEAN	Root-mean-squared-error of the estimation. A matrix of two columns. The first column records the RMSE for the mean of matrixY1 in each iteration of the Gibbs sampling process and the second column records that of matrixY2

Examples

library(Rlab)
library(MASS)
library(coda)
library(ROCR)

data(Y1_mean)
data(Y2_mean)
data(matrixY1)
data(matrixY2)
data(matrixZ_chain)
data(matrixW1)
data(matrixW2)
data(matrixW_chain)
data(matrixX)
data(matrixX_chain)
Ymean_compare(Y1_mean,Y2_mean,matrixY1, matrixY2, 
matrixZ_chain, matrixW1, matrixW2, matrixW_chain, 
matrixX, matrixX_chain, result_file_name="RMSE_demo.RData", 
plot_name="RMSE_plot.pdf")

iFad documentation built on May 2, 2019, 6:50 a.m.