row_optimize {SPEM} | R Documentation |
This function calculates parameters for a single row in the expression data. If a large-size dataset will be calculated, this function is recommended.
## S4 method for signature 'ExpressionSet' row_optimize(TS_eSet,S,beta, sparsity = 0.2, lbH = -3, ubH = 3, lbB = 0, ubB = 10)
TS_eSet |
Time series data in ExpressionSet class assayData: Matrix with n metabolite in row and m time points in column. phenoData: Dataframe includes label "time", which represents the time points. |
S |
Slope of the row you want to calculated. You can either input a vector with length equal to the rows of assayData of TS_eSet, or use s_diff function in this package to calculate it. |
beta |
Initial beta. |
sparsity |
A threshold used to control the sparsity of reconstructed matrix. Values whose absolute value smaller than sparsity will be set to zero. |
lbH |
Lower boundary value of h. |
ubH |
Upper boundary value of h. |
lbB |
Lower boundary value of beta. |
ubB |
Upper boundary value of beta. |
In this SPEM package, we aim to reconstruct gene networks from time-series expression data using the S-system model. The input dataset should be as an ExpressionSet data container, describing, in assayData, expression data for n genes (rows) and m time points (columns), along with a vector of length m, which records the exact values of time points, thus showing the sample intervals in phenoData. SPEM will calculate the parameters alpha, g, beta and h of the S-system function set that best fits the dataset.
In this function, user can calculate one row at a time. This function offers a parallel calculation option for users.
This function return a vector bind with c(alpha, $g_i$, beta, $h_i$, Initial Beta, error).
signature(TS_eSet = "ExpressionSet")
This method is created for the function row_optimize
.
Yang, X-Y, Dent, Jennifer E. and Nardini, C.
#########Load the SOS pathway data ####### data(sos) #########Set Slope and Initial Beta ####### Slope<- s_diff(sos) S<- Slope[1,] #S is the slope of the row you want to calculate. You can either input a vector yourself. beta<- runif(n=1,min=1,max=10) #########Set parameters ####### sparsity<- 0.2 lbH<- -3 ubH<- 3 lbB<- 0 ubB<- 10 #########Calculate results ####### result_r<-row_optimize(sos,S,beta,sparsity,lbH,ubH,lbB,ubB)