SPEM {SPEM} | R Documentation |
This function calculates parameters of S-system from entire time series matrix.
## S4 method for signature 'ExpressionSet' SPEM(TS_eSet, n = 3, 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: phenoData type. The sample data.frame should include the label "time", which represents the values of time points. |
n |
Positive integer, SPEM will guess initial beta n times. |
sparsity |
A positive number. In order to force the interaction matrix to be sparse, interactions with 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.
alpha, G, beta, H |
Parameters of the reconstructed S-system. |
IniBeta |
Guess of the IniBeta value (Picked randomly by SPEM itself). |
error |
Regression error. |
signature(TS_eSet = "ExpressionSet")
This method is created for function SPEM
.
Yang, X-Y., Dent, Jennifer E. and Nardini, C.
#########Generate Toy Model ####### ######### # If you want to calculate SOS dataset in this package, please read our vignette### #Real dataset takes a long time to calculate. You may want to try function 'row_optimize' to compute it in parallel### toy_expression_data<-matrix(data=abs(rnorm(12)),nrow=3,ncol=4, dimnames=list(paste("G",c(1:3),sep=''), paste("tp",c(0,2,4,6),sep="_"))) toy_timepoints_data<-data.frame(index=c(1:4), label=paste("tp",c(0,2,4,6),sep='_'), time=c(0,2,4,6),row.names=paste("tp",c(0,2,4,6),sep='_')) toy_varMetadata<-data.frame(labelDescription=c("Index number","Label Detail", "Time points values"),row.names=c("index","label","time")) toy_phenoData<-new("AnnotatedDataFrame", data=toy_timepoints_data,varMetadata=toy_varMetadata) toy_ExpressionSet<-new("ExpressionSet", exprs=toy_expression_data,phenoData=toy_phenoData) #########Set parameters ####### n<- 1 sparsity<- 0.2 lbH<- -3 ubH<- 3 lbB<- 0 ubB<- 10 #########Calculate results ####### result<-SPEM(toy_ExpressionSet,n,sparsity,lbH,ubH,lbB,ubB)