| Title: | Denoising Algorithm based on Relevance network Topology |
|---|---|
| Description: | Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. |
| Authors: | Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng |
| Maintainer: | Charles Shijie Zheng <[email protected]> |
| License: | GPL-2 |
| Version: | 1.55.0 |
| Built: | 2024-10-30 05:20:14 UTC |
| Source: | https://github.com/bioc/DART |
Denoising Algorithm based on Relevance network Topology (DART) is an unsupervised algorithm which evaluates the consistency of model pathway/molecular signatures in independent molecular samples before estimating the activation status of the signature in these independent samples. DART was devised for application to cancer genomics problems. For instance, one may wish to infer the activation status of an in-vitro derived oncogenic perturbation gene expression signature in a primary tumour for which genome-wide expression data is available. Before estimating pathway activity in the tumour, DART will evaluate if the "model" in-vitro signature pattern of up and down regulation is consistent with the expression variation seen across an expression panel of primary tumours. If the consistency score is statistically significant, this justfies using the perturbation signature to infer the activation status of the oncogenic pathway in the independent tumour samples. However, in this case, DART will also prune/denoise the perturbation signature using a relevance network topology strategy. This denoising step implemented in DART has been shown to improve estimates of pathway activity in clinical tumour specimens. Other examples of model pathway signatures could be a pathway model of signal transduction, a curated list of genes predicted to be up or down regulated in response to pathway activation/inhibition, or predicted upregulated targets of a transcription factor from say ChIP-Chip/Seq experiments. Three internal functions implement the steps in DART and are provided as explicit functions to allow user flexibility.
DoDARTCLQ infers perturbation/pathway activity over a smaller and
more compact subnetwork compared to DoDART. Specifically, whereas
DoDART uses the whole pruned correlation network,
DoDARTCLQ infers all maximal cliques within the pruned
correlation network and then estimates activity using only genes in the
subnetwork obtained by the union of these maximal cliques.
DoDART and DoDARTCLQ are the main user functions which will automatically and sequentially run through the following internal functions:
(1) BuildRN: This function builds a relevance correlation network
of the model pathway signature genes in the data set in which the
pathway activity estimate is desired. Note that this step is totally
unsupervised and does not use any sample phenotype information.
(2) EvalConsNet: This function evaluates the consistency of the
inferred relevance network with the prior information of the model pathway signature. The up/down regulatory pattern given by the model signature implies predictions about the directionality of the gene-gene correlations in the independent data set. For instance, if gene "A" is upregulated and gene "B" is downregulated, then assuming that the model signature has any relevance in the independent data set, we would expect genes "A" and "B" to be anti-correlated. Thus, a consistency score can be computed. Only if the consistency score is higher than the score expected by random chance is it recommended that the model signature be used to infer pathway activity.
(3) PruneNet: This function constructs the pruned, i.e consistent, network, in which any edge represents a significant correlation in gene expression whose directionality agrees with that predicted by the prior information. This is the denoising step of the algorithm. The function returns the whole pruned network and its maximally connected component.
(4) PredActScore: Given the adjacency matrix of the maximally
connected consistent (pruned) subnetwork and given the regulatory
weights of the corresponding model pathway signature, this function
estimates a pathway activation score in a given sample. This function
can also be used to infer pathway activity in samples from another
independent data set using the original inferred subnetwork.
Andrew E Teschendorff
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
Teschendorff AE, Li L, Yang Z. (2015) Denoising perturbation signatures reveals an actionable AKT-signaling gene module underlying a poor clinical outcome in endocrine treated ER+ breast cancer. Genome Biology 16:61.
### Example ### load in example data: data(dataDART); ### dataDART$data: mRNA expression data of 67 ER negative breast cancer samples. ### dataDART$pheno: 51 basals and 16 HER2+ (ERBB2+). ### dataDART$sign: perturbation signature of ERBB2 activation. ### Build Relevance Network rn.o <- BuildRN(dataDART$data,dataDART$sign,fdr=0.000001); ### Evaluate Consistency evalNet.o <- EvalConsNet(rn.o); print(evalNet.o$netcons); ### The consistency score, i.e fraction of consistent edges is 0.94 ### P-value is significant, so proceed: ### Prune i.e denoise the network prNet.o <- PruneNet(evalNet.o); ### print dimension of the maximally connected pruned network print(dim(prNet.o$pradjMC)); ### infer signature activation in the original data set pred.o <- PredActScore(prNet.o,dataDART$data); ### check that activation is higher in HER2+ compared to basals boxplot(pred.o$score ~ dataDART$pheno); pv <- wilcox.test(pred.o$score ~ dataDART$pheno)$p.value; text(x=1.5,y=3.8,labels=paste("P=",pv,sep=""));### Example ### load in example data: data(dataDART); ### dataDART$data: mRNA expression data of 67 ER negative breast cancer samples. ### dataDART$pheno: 51 basals and 16 HER2+ (ERBB2+). ### dataDART$sign: perturbation signature of ERBB2 activation. ### Build Relevance Network rn.o <- BuildRN(dataDART$data,dataDART$sign,fdr=0.000001); ### Evaluate Consistency evalNet.o <- EvalConsNet(rn.o); print(evalNet.o$netcons); ### The consistency score, i.e fraction of consistent edges is 0.94 ### P-value is significant, so proceed: ### Prune i.e denoise the network prNet.o <- PruneNet(evalNet.o); ### print dimension of the maximally connected pruned network print(dim(prNet.o$pradjMC)); ### infer signature activation in the original data set pred.o <- PredActScore(prNet.o,dataDART$data); ### check that activation is higher in HER2+ compared to basals boxplot(pred.o$score ~ dataDART$pheno); pv <- wilcox.test(pred.o$score ~ dataDART$pheno)$p.value; text(x=1.5,y=3.8,labels=paste("P=",pv,sep=""));
This function builds the relevance correlation network for the genes in the model pathway signature in the given data set.
BuildRN(data.m, sign.v, fdr)BuildRN(data.m, sign.v, fdr)
data.m |
Data matrix (numeric). Rows label features (genes), columns label samples. It is assumed that the number of features is much larger than the number of samples. Rownames must be a valid gene or probe identifier. |
sign.v |
Model pathway signature vector (numeric). Elements correspond to the regulatory weights, i.e the sign indicates if up or downregulated. Names of sign.v must be a gene name (probe) identifier which must match the gene (probe) identifier used for the rows of |
fdr |
Desired false discovery rate (numeric) which determines the allowed number of false positives in the relevance network. The default value is 0.000001. Since typically model signatures may contain on the order of 100 genes, this amounts to constructing a relevance network on the order of 10000 edges (pairwise correlations). Using a Bonferroni correction, this leads to a P-value threshold of approx. 1e-6 to 1e-7. This parameter is tunable, so choosing a number of different thresholds may be advisable. |
A list with following entries:
adj |
Adjacency matrix of inferred relevance network |
s |
Model signature vector in data |
sd |
Gene signature data matrix |
c |
Correlations between signature genes |
d |
Data matrix |
rep.idx |
Indices of the genes in signature which could be found in data matrix |
Andrew E Teschendorff, Yan Jiao
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.000001) ## See ?DoDART and vignette('DART') for further examples.data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.000001) ## See ?DoDART and vignette('DART') for further examples.
This data object consists of a gene expression data matrix over 67 oestrogen receptor negative breast cancers (Wang, Y. et al. Lancet 365, 671-9 (2005)), of which 51 are basals and 16 are HER2+/ERBB2+. This classification is based on the intrinsic subtype classifier (see Hu Z, et al., BMC Genomics. 2006 Apr 27;7:96), and while not equivalent to IHC or the amplification status at the ERBB2 locus, it broadly matches these other classifications. The model pathway signature is an in-vitro derived perturbation signature reflecting ERBB2 activation (see Creighton CJ, et al., Cancer Res 2006 Apr 1;66(7):3903-11). Both data and signature are annotated with Entrez Gene IDs.
dataDARTdataDART
A list with 4 elements: (i) data: is the gene expression matrix described above, (ii) sign: is the ERBB2 perturbation signature, (iii) pheno: basal/ERBB2 status of samples, (iv) phenoMAINZ: basal/ERBB2 status of a further set of samples.
data(dataDART) names(dataDART)data(dataDART) names(dataDART)
This is the main function implementing DART. Given a data matrix and a model (pathway) signature it will construct the relevance correlation network for the genes in the signature over the data, evaluate the consistency of the correlative patterns with those predicted by the model signature, filter out the noise and finally obtain estimates of pathway activity for each individual sample. Specifically, it will call and run the following functions:
(1) BuildRN: This function builds a relevance correlation network of the model pathway signature in the data set in which the pathway activity estimate is desired. We point that this step is totally unsupervised and does not use and phenotypic information of the samples.
(2) EvalConsNet: This function evaluates the consistency of the inferred network with the prior information of the model pathway signature. The up/down regulatory pattern given by the model signature implies predictions about the directionality of the gene-gene correlations in the independent data set. For instance, if gene "A" is upregulated and gene "B" is downregulated, then assuming that the model signature has any relevance in the independent data set, we would expect genes "A" and "B" to be anti-correlated. Thus, a consistency score can be computed. Only if the consistency score is higher than the score expected by random chance is it recommended that the model signature be used to infer pathway activity.
(3) PruneNet: This function obtains the pruned, i.e consistent, network, in which any edge represents a significant correlation in gene expression whose directionality agrees with that predicted by the prior information. This is the denoising step of the algorithm. The function returns the whole pruned network and its maximally connected component.
(4) PredActScore: Given the adjacency matrix of the maximally connected consistent subnetwork and given the regulatory weights of the corresponding model pathway signature, this function estimates a pathway activation score in each sample. This function can also be used to infer pathway activity in another independent data set using the inferred subnetwork.
Before performing the pruning step, DoDART will check whether
the relevance correlation network is significantly consistent with the
predictions from the model signature. Significance is assessed by
first computing a consistency score (in effect, the fraction of edges in the
relevance network which are consistent with the model prediction) and
subsequently by 1000 random permutations to obtain an empirical null
distribution for the consistency score. Model signatures whose consistency scores have
empirical P-values less than 0.001 are deemed consistent. If the
consistency score is not significant, the function will issue a warning and it is not recommended to use the signature to predict pathway activity.
DoDART(data.m, sign.v, fdr)DoDART(data.m, sign.v, fdr)
data.m |
Data matrix (numeric). Rows label features, columns label samples. It is assumed that number of features is much larger than number of samples. Rownames of |
sign.v |
Model pathway signature vector (numeric). Elements represent the regulatory weights (i.e if up or downregulated). Names of |
fdr |
Desired false discovery rate (numeric) which determines the allowed number of false positives in the relevance network. The default value is 0.000001. Since typically model signatures may contain on the order of 100 genes, this amounts to constructing a relevance network on the order of 10000 edges (pairwise correlations). Using a Bonferroni correction, this leads to a P-value threshold of approx. 1e-6 to 1e-7. This parameter is tunable, so choosing a number of different thresholds may be advisable. |
netcons |
A vector summarising the properties of the correlation network and the consistency with the model pathway signature: nG is the number of genes in the signature, nE is the number of edges of the relevance network generated by function |
adj |
Adjacency matrix of maximally connected consistent relevance network. |
sign |
Model pathway signature vector of genes found in data set and in maximally connected component. |
score |
Predicted activation scores of the model signature in the samples of data set |
degree |
Degrees/connectivities of the genes in the DART network. |
consist |
Module consistency result, output of |
Andrew E Teschendorff, Yan Jiao
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
### Example ### load in example data: data(dataDART); ### dataDART$data: mRNA expression data of 67 ER negative breast cancer samples. ### dataDART$pheno: 51 basals and 16 HER2+ (ERBB2+). ### dataDART$phenoMAINZ: 24 basals and 8 HER2+ (ERBB2+). ### dataDART$sign: perturbation signature of ERBB2 activation. ### Using DoDART dart.o <- DoDART(dataDART$data,dataDART$sign,fdr=0.000001); ### check that activation is higher in HER2+ compared to basals boxplot(dart.o$score ~ dataDART$pheno); pv <- wilcox.test(dart.o$score ~ dataDART$pheno)$p.value; text(x=1.5,y=3.8,labels=paste("P=",pv,sep=""));### Example ### load in example data: data(dataDART); ### dataDART$data: mRNA expression data of 67 ER negative breast cancer samples. ### dataDART$pheno: 51 basals and 16 HER2+ (ERBB2+). ### dataDART$phenoMAINZ: 24 basals and 8 HER2+ (ERBB2+). ### dataDART$sign: perturbation signature of ERBB2 activation. ### Using DoDART dart.o <- DoDART(dataDART$data,dataDART$sign,fdr=0.000001); ### check that activation is higher in HER2+ compared to basals boxplot(dart.o$score ~ dataDART$pheno); pv <- wilcox.test(dart.o$score ~ dataDART$pheno)$p.value; text(x=1.5,y=3.8,labels=paste("P=",pv,sep=""));
This function implements an improved version of DART. DoDARTCLQ
estimates perturbation/pathway activity using a smaller subnetwork
compared to DoDART. Specifically, whereas DoDART uses the whole pruned correlation network, DoDARTCLQ infers the largest cliques within the pruned correlation network and then estimates activity using only genes within a module obtained by merging the largest cliques together. Although the largest cliques may not be unique, we found that they generally exhibited very strong overlaps, justfying their merging, and resulting in approximate clique modules typically in the size of ~10 - 100 genes.
Given a data matrix and a model (pathway) signature it will construct the relevance correlation network for the genes in the signature over the data, evaluate the consistency of the correlative patterns with those predicted by the model signature, filter out the noise and finally obtain estimates of pathway activity for each individual sample. Specifically, it will call and run the following functions:
(1) BuildRN: This function builds a relevance correlation network
of the model pathway signature in the data set in which the pathway
activity estimate is desired. We point that this step is totally
unsupervised and does not use any sample phenotype information.
(2) EvalConsNet: This function evaluates the consistency of the inferred network with the prior information of the model pathway signature. The up/down regulatory pattern given by the model signature implies predictions about the directionality of the gene-gene correlations in the independent data set. For instance, if gene "A" is upregulated and gene "B" is downregulated, then assuming that the model signature has any relevance in the independent data set, we would expect genes "A" and "B" to be anti-correlated. Thus, a consistency score can be computed. Only if the consistency score is higher than the score expected by random chance is it recommended that the model signature be used to infer pathway activity.
(3) PruneNet: This function obtains the pruned, i.e consistent, network, in which any edge represents a significant correlation in gene expression whose directionality agrees with that predicted by the prior information. This is the denoising step of the algorithm. The function returns the whole pruned network and its maximally connected component.
(4) PredActScore: Given the adjacency matrix of the maximally connected consistent subnetwork and given the regulatory weights of the corresponding model pathway signature, this function estimates a pathway activation score in each sample. This function can also be used to infer pathway activity in another independent data set using the inferred subnetwork.
Before performing the pruning step, DoDARTCLQ will check whether
the relevance correlation network is significantly consistent with the
predictions from the model signature. Significance is assessed by
first computing a consistency score (in effect, the fraction of edges in the
relevance network which are consistent with the model prediction) and
subsequently by 1000 random permutations to obtain an empirical null
distribution for the consistency score. Model signatures whose consistency scores have
empirical P-values less than 0.001 are deemed consistent. If the
consistency score is not significant, the function will issue a warning and it is not recommended to use the signature to predict pathway activity.
DoDARTCLQ(data.m, sign.v, fdr)DoDARTCLQ(data.m, sign.v, fdr)
data.m |
Training normalized gene expression data matrix with rows labeling genes and columns labeling samples. Rownames of |
sign.v |
A perturbation expression signature vector, consisting of 1 and -1, indicating whether gene is significant overexpressed or underexpressed, respectively. names of |
fdr |
Desired false discovery rate (numeric) which determines the allowed number of false positives in the relevance network. The default value is 0.000001. Since typically model signatures may contain on the order of 100 genes, this amounts to constructing a relevance network on the order of 10000 edges (pairwise correlations). Using a Bonferroni correction, this leads to a P-value threshold of approx. 1e-6 to 1e-7. This parameter is tunable, so choosing a number of different thresholds may be advisable. |
pred |
A vector of predicted perturbation activity scores for all samples in |
clq |
A list containing information about the approximate clique gene module used to compute perturbation activity. List entries: clq$pradjMC: the adjacency matrix of the approx. clique gene module, clq$signMC: the corresponding perturbation signature for the genes making up the approx. clique module, clq$sizes: the sizes of all largest cliques in the pruned correlation network, clq$n: the number of largest cliques in the pruned correlation network. |
consist |
Module consistency result, output of |
Andrew E Teschendorff
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
Teschendorff AE, Li L, Yang Z. (2015) Denoising perturbation signatures reveals an actionable AKT-signaling gene module underlying a poor clinical outcome in endocrine treated ER+ breast cancer. Genome Biology 16:61.
### Example ### load in example data: data(dataDART); ### dataDART$data: mRNA expression data of 67 ER negative breast cancer samples. ### dataDART$pheno: 51 basals and 16 HER2+ (ERBB2+). ### dataDART$phenoMAINZ: 24 basals and 8 HER2+ (ERBB2+). ### dataDART$sign: perturbation signature of ERBB2 activation. ### Using DoDARTCLQ dart.o <- DoDARTCLQ(dataDART$data,dataDART$sign, fdr=0.000001); ### check that activation is higher in HER2+ compared to basals boxplot(dart.o$pred ~ dataDART$pheno); pv <- wilcox.test(dart.o$pred ~ dataDART$pheno)$p.value; text(x=1.5,y=3.5,labels=paste("P=",pv,sep=""));### Example ### load in example data: data(dataDART); ### dataDART$data: mRNA expression data of 67 ER negative breast cancer samples. ### dataDART$pheno: 51 basals and 16 HER2+ (ERBB2+). ### dataDART$phenoMAINZ: 24 basals and 8 HER2+ (ERBB2+). ### dataDART$sign: perturbation signature of ERBB2 activation. ### Using DoDARTCLQ dart.o <- DoDARTCLQ(dataDART$data,dataDART$sign, fdr=0.000001); ### check that activation is higher in HER2+ compared to basals boxplot(dart.o$pred ~ dataDART$pheno); pv <- wilcox.test(dart.o$pred ~ dataDART$pheno)$p.value; text(x=1.5,y=3.5,labels=paste("P=",pv,sep=""));
This function evaluates statistical consistency of the inferred relevance network with the correlations predicted by the model pathway signature. Only if the consistency score is higher than the score expected by random chance, is it recommended to use the signature to infer pathway activity.
EvalConsNet(buildRN.o)EvalConsNet(buildRN.o)
buildRN.o |
Output |
A list with following entries:
netcons |
A vector summarising the properties of the network and the consistency with the prior information: nG is the number of genes in the signature, nE is the number of edges of the relevance network generated by function BuildRN, fE is the ratio of the number of edges in the relevance network to the maximum possible number, fconsE is the fraction of edges whose sign (i.e sign of correlation) is the same as the directionality predicted by the model signature, Pval(consist) is a p-value reflecting the significance of fconsE, and is estimated as the fraction of randomisations that yielded an average connectivity larger than the observed one. |
netsign |
A matrix of dimension 2 times number of edges in network comparing directionality of prior info and that in the observed data |
adj |
Adjacency matrix of inferred relevance network |
s |
Model signature vector |
c |
Correlation matrix between model signature genes |
Andrew E Teschendorff, Yan Jiao
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05) evalNet.o <- EvalConsNet(rn.o) ## See ?DoDART and vignette('DART') for further examples.data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05) evalNet.o <- EvalConsNet(rn.o) ## See ?DoDART and vignette('DART') for further examples.
This function computes the DART activation score of the model signature for the samples of a data matrix. This data matrix can be the same data matrix in which DART was applied or it could be a totally independent data set.
PredActScore(prNet.o, data.m)PredActScore(prNet.o, data.m)
prNet.o |
Output |
data.m |
A data matrix (numeric) with rows labeling genes/probes and columns labeling samples in which the signature activity scores are desired. |
A list with following elements:
adj |
Adjacency matrix of the DART network for the genes found in the data set given by |
sign |
The corresponding model signature vector for the genes found in the data set. |
score |
A vector of the signature/pathway activity scores for each sample in the data set. |
Andrew E Teschendorff, Yan Jiao
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05) evalNet.o <- EvalConsNet(rn.o) prNet.o <- PruneNet(evalNet.o) pred.o <- PredActScore(prNet.o,dataDART$data) ## See ?DoDART and vignette('DART') for further examples.data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05) evalNet.o <- EvalConsNet(rn.o) prNet.o <- PruneNet(evalNet.o) pred.o <- PredActScore(prNet.o,dataDART$data) ## See ?DoDART and vignette('DART') for further examples.
Prunes relevance network to allow only edges that are consistent with the predictions of the model signature, and extracts the maximally connected component. This is the denoising step in DART.
PruneNet(evalNet.o)PruneNet(evalNet.o)
evalNet.o |
Output |
A list with following entries:
pradj |
The adjacency matrix of the pruned i.e consistent network. |
sign |
The model signature vector of genes in pruned network. |
score |
The fraction of edges surviving the pruning/denoising. |
netconst |
Same output as for EvalConsNet. |
pradjMC |
The adjacency matrix of the maximally connected component of pruned network. |
signMC |
The model signature vector of the genes in the maximally connected component. |
Andrew E Teschendorff, Yan Jiao
Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.
Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.
data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05) evalNet.o <- EvalConsNet(rn.o) prNet.o <- PruneNet(evalNet.o) pred.o <- PredActScore(prNet.o,dataDART$data) ## See ?DoDART and vignette('DART') for further examples.data(dataDART) rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05) evalNet.o <- EvalConsNet(rn.o) prNet.o <- PruneNet(evalNet.o) pred.o <- PredActScore(prNet.o,dataDART$data) ## See ?DoDART and vignette('DART') for further examples.