, Veronica Vinciotti
[ctb], Ernst Wit [ctb]| Title: | Efficient Network Enrichment Analysis Test |
|---|---|
| Description: | Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, <DOI:10.1186/s12859-016-1203-6>). |
| Authors: | Mirko Signorelli [aut, cre, cph]
, Veronica Vinciotti
[ctb], Ernst Wit [ctb] |
| Maintainer: | Mirko Signorelli <[email protected]> |
| License: | GPL-3 |
| Version: | 1.2.4 |
| Built: | 2024-08-30 02:37:43 UTC |
| Source: | https://github.com/cran/neat |
Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016).
Mirko Signorelli
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
Compute NEAT (Signorelli et al., 2016), a test for network enrichment analysis between/from a first list of sets ('A sets') and/to a second list of sets ('B sets').
neat(alist, blist = NULL, network, nettype, nodes, alpha = NULL, mtc.type = 'fdr', anames = NULL, bnames = NULL)neat(alist, blist = NULL, network, nettype, nodes, alpha = NULL, mtc.type = 'fdr', anames = NULL, bnames = NULL)
alist |
List of A sets. Each element within the list is a vector of genes and represents a gene set |
blist |
List of B sets. Each element within the list is a vector of genes and represents a gene set. If |
network |
One of the following objects: an adjacency matrix of class "matrix" (see 'Example 1') or a sparse adjacency matrix of class "dgCMatrix"; an |
nettype |
Either |
nodes |
Vector containing the (ordered) names of all nodes in the network |
alpha |
Significance level of the test (optional). If specified, a column with the conclusion of the test is added to the output |
mtc.type |
Type of multiple testing correction (NB: added from package version 1.2.0). Use |
anames |
Vector of names for the elements of |
bnames |
Vector of names for the elements of |
A data frame with the following columns:
A |
A set |
B |
B set |
nab |
observed number of links from A to B |
expected_nab |
expected number of links from A to B (in absence of enrichment) |
pvalue |
p-value of the test |
adjusted.p |
p-value adjusted to account for multiple testing |
conclusion |
conclusion of the test (only if |
Mirko Signorelli
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
networkmatrix, plot.neat, print.neat, summary.neat
# Example 1: network given as adjacency matrix: A = matrix(0, nrow=7, ncol=7) A[1,c(2,3)]=1; A[2,c(5,7)]=1;A[3,c(1,4)]=1;A[4,c(2,5,7)]=1;A[6,c(2,5)]=1;A[7,4]=1 labels = letters[1:7] set1 = c('a','e') set2 = c('c','g') set3 = c('d','f') alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3) # test without multiple testing correction test1 = neat(alist = alist, blist = blist, network=A, nettype='directed', nodes=labels, alpha=0.05, mtc.type = 'none') print(test1) # test with FDR multiple testing correction (default) test1 = neat(alist = alist, blist = blist, network=A, nettype='directed', nodes=labels, alpha=0.05, mtc.type = 'fdr') print(test1) # Example 2: network given as igraph object: library(igraph) network = erdos.renyi.game(15, 1/3) set1 = 1:4 set2 = c(2,5,13) set3 = c(3,9,14) set4 = c(8,15,20) alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3, 'set 4' = set4) test2 = neat(alist, blist, network = network, nettype='undirected', nodes=seq(1,15), alpha=NULL) print(test2) # Example 3: network given as list of links: networklist = matrix(nrow=13, ncol=2) networklist[,1]=c('a','a','b','b','c','d','d','d','f','f','f','h','h') networklist[,2]=c('d','e','e','g','d','b','e','g','a','b','e','c','g') labels = letters[1:8] set1 = c('a','b','e') set2 = c('c','g') set3 = c('d','f') set4 = c('a','b','f') alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3, 'set4' = set4) test3 = neat(alist, blist, network = networklist, nettype = 'undirected', nodes=labels, alpha=0.05, mtc.type = 'none') print(test3) alist = list('set 1' = set1, 'set 2' = set2, 'set 3' = set3) test4 = neat(alist, network = networklist, nettype = 'undirected', nodes=labels, alpha=0.05, mtc.type = 'none') print(test4) # Example 4: ESR data ## Not run: data(yeast) esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2) test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) # Replace with "blist = yeast$kegg" to use kegg pathways m = dim(test)[1] test1 = test[1:(m/2),] table(test1$conclusion) plot(test1) o1=test1[test1$conclusion=='Overenrichment',] print(o1, nrows='ALL') #display overenrichments test2 = test[(m/2+1):m,] table(test2$conclusion) plot(test2) o2=test2[test2$conclusion=='Overenrichment',] print(o2, nrows='ALL') #display overenrichments ## End(Not run)# Example 1: network given as adjacency matrix: A = matrix(0, nrow=7, ncol=7) A[1,c(2,3)]=1; A[2,c(5,7)]=1;A[3,c(1,4)]=1;A[4,c(2,5,7)]=1;A[6,c(2,5)]=1;A[7,4]=1 labels = letters[1:7] set1 = c('a','e') set2 = c('c','g') set3 = c('d','f') alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3) # test without multiple testing correction test1 = neat(alist = alist, blist = blist, network=A, nettype='directed', nodes=labels, alpha=0.05, mtc.type = 'none') print(test1) # test with FDR multiple testing correction (default) test1 = neat(alist = alist, blist = blist, network=A, nettype='directed', nodes=labels, alpha=0.05, mtc.type = 'fdr') print(test1) # Example 2: network given as igraph object: library(igraph) network = erdos.renyi.game(15, 1/3) set1 = 1:4 set2 = c(2,5,13) set3 = c(3,9,14) set4 = c(8,15,20) alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3, 'set 4' = set4) test2 = neat(alist, blist, network = network, nettype='undirected', nodes=seq(1,15), alpha=NULL) print(test2) # Example 3: network given as list of links: networklist = matrix(nrow=13, ncol=2) networklist[,1]=c('a','a','b','b','c','d','d','d','f','f','f','h','h') networklist[,2]=c('d','e','e','g','d','b','e','g','a','b','e','c','g') labels = letters[1:8] set1 = c('a','b','e') set2 = c('c','g') set3 = c('d','f') set4 = c('a','b','f') alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3, 'set4' = set4) test3 = neat(alist, blist, network = networklist, nettype = 'undirected', nodes=labels, alpha=0.05, mtc.type = 'none') print(test3) alist = list('set 1' = set1, 'set 2' = set2, 'set 3' = set3) test4 = neat(alist, network = networklist, nettype = 'undirected', nodes=labels, alpha=0.05, mtc.type = 'none') print(test4) # Example 4: ESR data ## Not run: data(yeast) esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2) test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) # Replace with "blist = yeast$kegg" to use kegg pathways m = dim(test)[1] test1 = test[1:(m/2),] table(test1$conclusion) plot(test1) o1=test1[test1$conclusion=='Overenrichment',] print(o1, nrows='ALL') #display overenrichments test2 = test[(m/2+1):m,] table(test2$conclusion) plot(test2) o2=test2[test2$conclusion=='Overenrichment',] print(o2, nrows='ALL') #display overenrichments ## End(Not run)
neat
Internal function, creates a two-column network matrix that can be further processed by neat.
networkmatrix(network, nodes, nettype)networkmatrix(network, nodes, nettype)
network |
One of the following objects: an adjacency matrix (class |
nodes |
Vector containing the (ordered) names of all nodes in the network |
nettype |
Either |
This is an internal function, that is called within neat to convert different types of network objects (see argument 'network' above) into a standard two-column network matrix, that can then be processed by neat.
A two-column matrix, where every row represents and edge. For directed networks, parent nodes must be in the first column, and child nodes in the second.
Mirko Signorelli
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
# First case: adjacency matrix n<-50 adjacency <- matrix(sample(0:1, n^2, replace=TRUE, prob=c(0.9,0.1)), ncol=n) diag(adjacency) <- 0 lab = paste(rep('gene'),1:n) head(networkmatrix(adjacency, lab, 'directed')) # Second case: sparse adjacency matrix library(Matrix) sparse_adjacency<-Matrix(adjacency,sparse=TRUE) head(networkmatrix(sparse_adjacency, lab, 'directed')) # Third case: igraph object library(igraph) igraph_graph = erdos.renyi.game(15, 1/3) lab = paste(rep('gene'),1:15) head(networkmatrix(igraph_graph, lab, 'directed'))# First case: adjacency matrix n<-50 adjacency <- matrix(sample(0:1, n^2, replace=TRUE, prob=c(0.9,0.1)), ncol=n) diag(adjacency) <- 0 lab = paste(rep('gene'),1:n) head(networkmatrix(adjacency, lab, 'directed')) # Second case: sparse adjacency matrix library(Matrix) sparse_adjacency<-Matrix(adjacency,sparse=TRUE) head(networkmatrix(sparse_adjacency, lab, 'directed')) # Third case: igraph object library(igraph) igraph_graph = erdos.renyi.game(15, 1/3) lab = paste(rep('gene'),1:15) head(networkmatrix(igraph_graph, lab, 'directed'))
neat
plot method for class "neat".
## S3 method for class 'neat' plot(x, nbreaks = 10, ...)## S3 method for class 'neat' plot(x, nbreaks = 10, ...)
x |
An object of class " |
nbreaks |
Number of breaks to be used in the histogram (default is 10) |
... |
Further arguments passed to or from other methods |
An histogram showing the distribution of p-values and a p-p plot comparing the distribution of p-values to the uniform distribution.
Mirko Signorelli
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
neat, print.neat, summary.neat
## Not run: data(yeast) esr2 = list('ESR 2' = yeast$esr2) test = neat(alist = esr2, blist = yeast$goslimproc, network = yeast$yeastnet, nettype='undirected', nodes = yeast$ynetgenes, alpha = 0.01) plot(test) ## End(Not run)## Not run: data(yeast) esr2 = list('ESR 2' = yeast$esr2) test = neat(alist = esr2, blist = yeast$goslimproc, network = yeast$yeastnet, nettype='undirected', nodes = yeast$ynetgenes, alpha = 0.01) plot(test) ## End(Not run)
neat
print method for class "neat".
## S3 method for class 'neat' print(x, nrows=10, ...)## S3 method for class 'neat' print(x, nrows=10, ...)
x |
An object of class " |
nrows |
Maximum number of results to print (default is 10). It can be either an integer number or "ALL" |
... |
Further arguments passed to or from other methods |
A dataframe showing the first nrows tests contained in a neat object.
Mirko Signorelli
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
A = matrix(0, nrow=7, ncol=7) A[1,c(2,3)]=1; A[2,c(5,7)]=1;A[3,c(1,4)]=1;A[4,c(2,5,7)]=1;A[6,c(2,5)]=1;A[7,4]=1 labels = letters[1:7] set1 = c('a','e') set2 = c('c','g') set3 = c('d','f') alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3) test = neat(alist, blist, network=A, nettype='directed', nodes=labels, alpha=0.05) print(test)A = matrix(0, nrow=7, ncol=7) A[1,c(2,3)]=1; A[2,c(5,7)]=1;A[3,c(1,4)]=1;A[4,c(2,5,7)]=1;A[6,c(2,5)]=1;A[7,4]=1 labels = letters[1:7] set1 = c('a','e') set2 = c('c','g') set3 = c('d','f') alist = list('set 1' = set1, 'set 2' = set2) blist = list('set 3' = set3) test = neat(alist, blist, network=A, nettype='directed', nodes=labels, alpha=0.05) print(test)
neat
summary method for class "neat".
## S3 method for class 'neat' summary(object, ...)## S3 method for class 'neat' summary(object, ...)
object |
An object of class " |
... |
Further arguments passed to or from other methods |
The summary.neat function returns the following values:
the number of tests computed;
the number of enrichments at 1% and 5% level;
the p-value of the Kolmogorov-Smirnov test to check if the distribution of p-values is uniform.
Mirko Signorelli
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
## Not run: data(yeast) esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2) test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) test1 = test[1:99,] summary(test1) test2 = test[100:198,] summary(test2) ## End(Not run)## Not run: data(yeast) esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2) test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) test1 = test[1:99,] summary(test1) test2 = test[100:198,] summary(test2) ## End(Not run)
yeast is a list that contains:
yeastnet: network matrix representing Yeastnet-v3 (Kim et al., 2013)
ynetgenes: vector with the names of the genes appearing in yeastnet
esr1: vector containing the first of the two gene sets that constitute the "Environmental Stress Response" (ESR) reported by Gasch et al. (2012)
esr2: vector containing the second gene set of the ESR
goslimproc: list containing the gene sets of the GOslim process ontology (Ashburner et al., 2000) for the buddying yeast Saccaromyces Cerevisiae (groups 'biological process' and 'other' are not included)
kegg: list containing the KEGG pathways (Kanehisa and Goto, 2002) for the buddying yeast Saccaromyces Cerevisiae
yeast: list
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000). Gene ontology: tool for the unification of biology. Nat. Genet., 25(1), 25-29.
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., and Brown, P. O. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11(12), 4241-4257.
Kanehisa, M., and Goto, S. (2002). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res., 28(1), 27-30.
Kim, H., Shin, J., Kim, E., Kim, H., Hwang, S., Shim, J. E., and Lee, I. (2013). Yeastnet v3: a public database of data-specific and integrated functional gene networks for saccharomyces cerevisiae. Nucleic Acids Res., 42 (D1), D731-6.
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000). Gene ontology: tool for the unification of biology. Nat. Genet., 25(1), 25-29.
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., and Brown, P. O. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11(12), 4241-4257.
Kanehisa, M., and Goto, S. (2002). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res., 28(1), 27-30.
Kim, H., Shin, J., Kim, E., Kim, H., Hwang, S., Shim, J. E., and Lee, I. (2013). Yeastnet v3: a public database of data-specific and integrated functional gene networks for saccharomyces cerevisiae. Nucleic Acids Res., 42 (D1), D731-6.
Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352. Url: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1203-6.
## Not run: data(yeast) esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2) test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) # Replace with "blist = yeast$kegg" to use kegg pathways m = dim(test)[1] test1 = test[1:(m/2),] o1=test1[test1$conclusion=='Overenrichment',] # list of overenrichments for the first ESR set: print(o1, nrows='ALL') test2 = test[(m/2+1):m,] o2=test2[test2$conclusion=='Overenrichment',] # list of overenrichments for the second ESR set: print(o2, nrows='ALL') # the same can be done using KEGG pathways: keggtest = neat(alist = esr, blist = yeast$kegg, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) ## End(Not run)## Not run: data(yeast) esr = list('ESR 1' = yeast$esr1, 'ESR 2' = yeast$esr2) test = neat(alist = esr, blist = yeast$goslimproc, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) # Replace with "blist = yeast$kegg" to use kegg pathways m = dim(test)[1] test1 = test[1:(m/2),] o1=test1[test1$conclusion=='Overenrichment',] # list of overenrichments for the first ESR set: print(o1, nrows='ALL') test2 = test[(m/2+1):m,] o2=test2[test2$conclusion=='Overenrichment',] # list of overenrichments for the second ESR set: print(o2, nrows='ALL') # the same can be done using KEGG pathways: keggtest = neat(alist = esr, blist = yeast$kegg, network = yeast$yeastnet, nettype = 'undirected', nodes = yeast$ynetgenes, alpha = 0.01) ## End(Not run)