TDCor (Time-Delay Correlation) is an algorithm designed to infer the topology of a gene regulatory network (GRN) from time-series transcriptomic data. The algorithm is described in details in Lavenus et al., Plant Cell, 2015. It was initially developped to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana. The time-series transcriptomic dataset analysed in this study is included in the package.
|License: GNU General Public License Version 2|
The reconstruction of a gene network using the TDCor package involves six steps.
Load the averaged non-log2 time series transcriptomic data into the R workspace.
Define the vector
times containing the times (in hours) at which the samples were collected.
Define the vector containing the gene codes of the genes you want to reconstruct the network with (e.g. see
l_genes), as well as the associated gene names (e.g. see
l_names) and the associated prior (e.g. see
Build or update the TPI database using the
Build or update the DPI database using the
Reconstruct the network using the
TDCOR main function.
See examples below.
Besides the functions of the TDCor algorithm, the package also contains the lateral root transcriptomic dataset (
times vector to use with this dataset (
times), the vector of AGI gene codes used to reconstruct the network shown in the original paper (
l_genes), the vector of the gene names (
l_names) and the prior (
l_prior). The associated TPI and DPI databases (
DPI15) which were used to build the network shown in the original paper are not included. Hence to reconstruct the lateral root network, these first need to be generated. A database of about 1800 Arabidopsis transcription factors is also included (
Three side functions,
draw.profile are also available to the user. These can be used to visualize the transcriptomic data, optimize some of the TDCOR parameters, and analyze the networks.
Lavenus et al. (2015), Inference of the Arabidopsis lateral root gene regulatory network suggests a bifurcation mechanism that defines primordia flanking and central zones. The Plant Cell, in press.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## Not run: # Load the LR transcriptomic dataset data(LR_dataset) # Load the vectors of genes codes, gene names and prior data(l_genes) data(l_names) data(l_prior) # Load the vector of time points for the LR_dataset data(times) # Generate the TPI database (this may take several hours) TPI10=CalculateTPI(dataset=LR_dataset,l_genes=l_genes,l_prior=l_prior, times=times,time_step=1,N=10000,ks_int=c(0.5,3),kd_int=c(0.5,3), delta_int=c(0.5,3),noise=0.1,delay=3) # Generate the DPI database (this may take several hours) DPI15=CalculateDPI(dataset=LR_dataset,l_genes=l_genes,l_prior=l_prior, times=times,time_step=1,N=10000,ks_int=c(0.5,3),kd_int=c(0.5,3), delta_int=c(0.5,3), noise=0.15, delay=3) # Check/update if necessary the databases TPI10=UpdateTPI(TPI10,LR_dataset,l_genes,l_prior) DPI15=UpdateDPI(DPI15,LR_dataset,l_genes,l_prior) # Choose your parameters ptime_step=1 ptol=0.13 pdelayspan=12 pthr_cor=c(0.65,0.8) pdelaymax=c(2.5,3.5) pdelaymin=0 pdelay=3 pthrpTPI=c(0.55,0.8) pthrpDPI=c(0.65,0.8) pthr_overlap=c(0.4,0.6) pthr_ind1=0.65 pthr_ind2=3.5 pn0=1000 pn1=10 pregmax=5 pthr_isr=c(4,6) pTPI=TPI10 pDPI=DPI15 pMinTarNumber=5 pMinProp=0.6 poutfile_name="TDCor_output.txt" # Reconstruct the network tdcor_out= TDCOR(dataset=LR_dataset, l_genes=l_genes,l_names=l_names,n0=pn0,n1=pn1, l_prior=l_prior, thr_ind1=pthr_ind1,thr_ind2=pthr_ind2,regmax=pregmax,thr_cor=pthr_cor, delayspan=pdelayspan,delaymax=pdelaymax,delaymin=pdelaymin,delay=pdelay,thrpTPI=pthrpTPI, thrpDPI=pthrpDPI,TPI=pTPI,DPI=pDPI,thr_isr=pthr_isr,time_step=ptime_step,thr_overlap=pthr_overlap, tol=ptol,MinProp=pMinProp,MinTarNumber=pMinTarNumber,outfile_name=poutfile_name) ## End(Not run)