| Type: | Package |
| Title: | Orthogonal Sparse Non-Negative Matrix Tri-Factorization |
| Version: | 0.1.0 |
| Author: | Xiaoyao Yin |
| Maintainer: | Xiaoyao Yin <yinxy1992@sina.com> |
| Description: | A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC. |
| Imports: | dplyr, MASS, stats |
| Depends: | R (≥ 3.4.4) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2019-11-24 12:46:22 UTC; yxy |
| Repository: | CRAN |
| Date/Publication: | 2019-11-28 13:50:02 UTC |
Average Residue
Description
To calculate average residues of the bi-clustering results
Usage
ASR(row_cluster,col_cluster,W)
Arguments
row_cluster |
The cluster results of the rows of W, this value should be a vector whose length is the same as the number of rows in W |
col_cluster |
The cluster results of the columns of W, this value should be a vector whose length is the same as the number of columns in W |
W |
The matrix to be factorized |
Value
The average residues of the bi-clustering results
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
OSNMTF_res <- OSNMTF(W,k=5,l=4)
row_cluster <- OSNMTF_res[[2]][[1]]
column_cluster <- OSNMTF_res[[2]][[2]]
ASR_value <- ASR(row_cluster,column_cluster,W)
Mean Residue
Description
To calculate mean residue of a sub-matrix block of W, indexed by a row cluster and a column cluster
Usage
MSR(Block)
Arguments
Block |
The sub-matrix block of W, indexed by a row cluster and a column cluster |
Value
The mean residue of the block
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
OSNMTF_res <- OSNMTF(W,k=5,l=4)
row_cluster <- OSNMTF_res[[2]][[1]]
column_cluster <- OSNMTF_res[[2]][[2]]
temp_rows <- which(row_cluster==1,TRUE)
temp_cols <- which(column_cluster==1,TRUE)
MSR_value <- MSR(W[temp_rows,temp_cols])
The algorithm OSNMTF
Description
Factorize matrix W into the multiplication of L, C and R, with L and R being orthogonal and C being sparse. Then the row cluster results and column cluster results are obtained from L and R.
Usage
OSNMTF(W,lambda=0.2,theta=10^-4,k,l)
Arguments
W |
The matrix to be factorized |
lambda |
A parameter to set the relative weight of the sparsity constraints |
theta |
A parameter to determine the convergence |
k |
A parameter to specify the row cluster number |
l |
A parameter to specify the column cluster number |
Value
A list containing the clustering result
sub_matrices |
a list containing the matrix L, C, R |
cluster_results |
a list containing the row cluster results and the column cluster results |
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
OSNMTF_res <- OSNMTF(W,k=5,l=4)
Standard Normalization
Description
To normalize the data matrix by column
Usage
Standard_Normalization(x)
Arguments
x |
The data matrix to be normalized |
Value
The normalized matrix
Author(s)
Xiaoyao Yin
Examples
data1 <- matrix(0,100,100)
data2 <- matrix(0,80,100)
for (i in 1:20)
{
data1[i,] <- rnorm(100,10,1)
}
for (i in 21:40)
{
data1[i,] <- rnorm(100,20,1)
}
for (i in 41:60)
{
data1[i,] <- rnorm(100,30,1)
}
for (i in 61:80)
{
data1[i,] <- rnorm(100,40,1)
}
for (i in 81:100)
{
data1[i,] <- rnorm(100,50,1)
}
new_data1 <- Standard_Normalization(data1)
Calculate the similarity matrix
Description
To calculate the similarity matrix with the same method in package M2SMF, for asymmetric case
Usage
affinityMatrix(Diff, K = 20, sigma = 0.5)
Arguments
Diff |
The distance matrix to culculate the similarity |
K |
The number of neighbours to culculate the similarity |
sigma |
A hyper-parameter to culculate the similarity |
Value
The similarity matrix
Author(s)
Xiaoyao Yin
Examples
data1 <- matrix(0,100,100)
data2 <- matrix(0,80,100)
for (i in 1:20)
{
data1[i,] <- rnorm(100,10,1)
}
for (i in 21:40)
{
data1[i,] <- rnorm(100,20,1)
}
for (i in 41:60)
{
data1[i,] <- rnorm(100,30,1)
}
for (i in 61:80)
{
data1[i,] <- rnorm(100,40,1)
}
for (i in 81:100)
{
data1[i,] <- rnorm(100,50,1)
}
for (i in 1:20)
{
data2[i,] <- rnorm(100,5,1)
}
for (i in 21:40)
{
data2[i,] <- rnorm(100,10,1)
}
for (i in 41:60)
{
data2[i,] <- rnorm(100,15,1)
}
for (i in 61:80)
{
data2[i,] <- rnorm(100,20,1)
}
new_data1 <- Standard_Normalization(data1)
new_data2 <- Standard_Normalization(data2)
Diff <- dist2eu(new_data1,new_data2)
simi_matr1 <- affinityMatrix(Diff, K = 20, sigma = 0.5)
Calculate the cost
Description
A function to calculate the cost of the objective function
Usage
cost(W,init_list,lambda=0.2)
Arguments
W |
The matrix to be factorized |
init_list |
A list containing the updated results in this iteration |
lambda |
A parameter to set the relative weight of the sparsity constraint |
Value
A number indicating the total cost of the objective function
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
init_list <- initialization(W,k=5,l=4)
update_L_list <- update_L(W,init_list)
update_B_list <- update_B(W,update_L_list)
update_R_list <- update_R(W,update_B_list)
update_C_list <- update_C(W,update_R_list,lambda=0.2,rho=1.1)
temp_cost <- cost(W,init_list,lambda=0.2)
Euclidean Distance
Description
The distance matrix of the two group of samples
Usage
dist2eu(X,C)
Arguments
X |
The first samples matrix |
C |
The second samples matrix |
Value
The distance matrix
Author(s)
Xiaoyao Yin
Examples
data1 <- matrix(0,100,100)
data2 <- matrix(0,80,100)
for (i in 1:20)
{
data1[i,] <- rnorm(100,10,1)
}
for (i in 21:40)
{
data1[i,] <- rnorm(100,20,1)
}
for (i in 41:60)
{
data1[i,] <- rnorm(100,30,1)
}
for (i in 61:80)
{
data1[i,] <- rnorm(100,40,1)
}
for (i in 81:100)
{
data1[i,] <- rnorm(100,50,1)
}
for (i in 1:20)
{
data2[i,] <- rnorm(100,5,1)
}
for (i in 21:40)
{
data2[i,] <- rnorm(100,10,1)
}
for (i in 41:60)
{
data2[i,] <- rnorm(100,15,1)
}
for (i in 61:80)
{
data2[i,] <- rnorm(100,20,1)
}
new_data1 <- Standard_Normalization(data1)
new_data2 <- Standard_Normalization(data2)
dist1 <- dist2eu(new_data1,new_data2)
initialize the values used in NMTFOSC
Description
initialize the values which will be updated in NMTFOSC
Usage
initialization(W,k,l)
Arguments
W |
The matrix to be factorized |
k |
A parameter to specify the row cluster number |
l |
A parameter to specify the column cluster number |
Value
A list with 6 elements, corresponding to the matrices L,C,R,B,Y and the penalty parameter miu
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
init_list <- initialization(W,k=5,l=4)
Generate simulation data
Description
To generate the simulation data matrix
Usage
simu_data_generation()
Value
The simulated data matrix
Author(s)
Xiaoyao Yin
Examples
simu_data <- simu_data_generation()
Update sub-matrix B
Description
Update sub-matrix B
Usage
update_B(W,update_L_list)
Arguments
W |
The matrix to be factorized |
update_L_list |
A list containing the updated results in this iteration after running the function update_L |
Value
A list the same as update_L_list with the matrix B updated
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
init_list <- initialization(W,k=5,l=4)
update_L_list <- update_L(W,init_list)
update_B_list <- update_B(W,update_L_list)
Update sub-matrix C
Description
Update sub-matrix C
Usage
update_C(W,update_R_list,lambda=0.2,rho=1.1)
Arguments
W |
The matrix to be factorized |
update_R_list |
A list containing the updated results in this iteration after running the function update_R |
lambda |
A parameter to set the relative weight of the sparsity constraints |
rho |
A parameter used in the augmented lagrange multiplier method |
Value
A list the same as update_R_list with the matrix C updated
Author(s)
Xiaoyao Yin
Examples
W <- simu_data_generation()
init_list <- initialization(W,k=5,l=4)
update_L_list <- update_L(W,init_list)
update_B_list <- update_B(W,update_L_list)
update_R_list <- update_R(W,update_B_list)
update_C_list <- update_C(W,update_R_list,lambda=0.2,rho=1.1)
Update sub-matrix L
Description
Update sub-matrix L
Usage
update_L(W,init_list)
Arguments
W |
The matrix to be factorized |
init_list |
A list containing the updated results in this iteration |
Value
A list the same as init_list with the matrix L updated
Examples
W <- simu_data_generation()
init_list <- initialization(W,k=5,l=4)
update_L_list <- update_L(W,init_list)
Update sub-matrix R
Description
Update sub-matrix R
Usage
update_R(W,update_B_list)
Arguments
W |
The matrix to be factorized |
update_B_list |
A list containing the updated results in this iteration after running the function update_B |
Value
A list the same as update_B_list with the matrix R updated
Examples
W <- simu_data_generation()
init_list <- initialization(W,k=5,l=4)
update_L_list <- update_L(W,init_list)
update_B_list <- update_B(W,update_L_list)
update_R_list <- update_R(W,update_B_list)