Description Usage Arguments Value Examples

View source: R/Ymean_compare.R

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

1 2 3 | ```
Ymean_compare(Y1_mean,Y2_mean,matrixY1, matrixY2,
matrixZ_chain, matrixW1, matrixW2, matrixW_chain,
matrixX, matrixX_chain, result_file_name, plot_name)
``` |

`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 |

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 |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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")
``` |

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