This function calculates parameters for a single row in the expression data. If a largesize 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 timeseries expression data using the Ssystem 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 Ssystem 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, XY, Dent, Jennifer E. and Nardini, C.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  #########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)

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