row_optimize: Calculate parameters for one row
In SPEM: S-system parameter estimation method

Description Usage Arguments Details Value Methods Author(s) Examples

This function calculates parameters for a single row in the expression data. If a large-size dataset will be calculated, this function is recommended.

1 2	## 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)