SPEM (version 1.12.0)

row_optimize: Calculate parameters for one row

Description

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

Usage

"row_optimize"(TS_eSet,S,beta, sparsity = 0.2, lbH = -3, ubH = 3, lbB = 0, ubB = 10)

Arguments

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.

Value

This function return a vector bind with c(alpha, $g_i$, beta, $h_i$, Initial Beta, error).

Methods

signature(TS_eSet = "ExpressionSet")

This method is created for the function row_optimize.

Details

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.

Examples

#########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)