Description Usage Arguments Details Value References Examples
generateResultsGraph provide the results of an enrichment
in several formats.
generateResultsTable returns a table
that contains the best hits from
with a successful enrichment analysis.
a data frame with the best scoring enzyme families and their
gives a sub-network, plottable through
plotGraph, witht the nodes with
p.score from an enrichment analysis.
addGOToGraph can be applied to such
sub-networks to overlay GO labels and
similarity to a user-defined GO term.
is a wrapper around
to write the results to files.
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generateResultsTable(method = "diffusion", threshold = 0.05, plimit = 15, nlimit = 250, LabelLengthAtPlot = 45, capPscores = 1e-06, object = NULL, data = NULL, ...) generateEnzymesTable(method = "diffusion", threshold = 0.05, nlimit = 250, LabelLengthAtPlot = 45, capPscores = 1e-06, mart.options = list(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), object = NULL, data = NULL, ...) generateResultsGraph(method = "diffusion", threshold = 0.05, plimit = 15, nlimit = 250, thresholdConnectedComponent = 0.05, LabelLengthAtPlot = 22, object = NULL, data = NULL, ...) exportResults(format = "csv", file = "myOutput", method = "diffusion", object = NULL, data = NULL, ...) addGOToGraph(graph = NULL, GOterm = NULL, godata.options = list(OrgDb = "org.Hs.eg.db", ont = "CC"), mart.options = list(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")) plotGraph(graph = NULL, layout = FALSE, graph.layout = NULL, plotLegend = TRUE, plot.fun = "plot.igraph", NamesAsLabels = TRUE, ...)
Numeric value between 0 and 1.
Pathway limit, must be a numeric value between 1 and 50.
Limits the amount of pathways in
Node limit, must be a numeric value between 1 and 1000.
Limits the order of the solution sub-graph when
Numeric value between 10 and 50. Maximum length that a label can reach when plotting the graph. The remaining characters will be truncated using "..."
Numeric value, minimum p-score
admitted for the readable
formatting. Smaller p-scores will be displayed
Optional arguments for the plotting function
List, options for the
Numeric value between 0 and 1. Connected components that are below the threshold are kept, while the ones exceeding it (because they are too small) are discarded.
Character, one of:
Character specifying the output file name
An igraph object,
typically a small one,
coming from an enrichment through
Character, GO entry to draw
semantic similarity in the solution graph.
List, options for the database creator
Logical, should the plot be returned as a layout?
Two-column numeric matrix, if this argument is not null then it is used as graph layout
Logical, should the legend be plotted as well?
Character, can be either
Logical, should KEGG names be displayed as labels instead of KEGG identifiers?
generateEnzymesTable need a
FELLA.DATA object and a
FELLA.USER object with a successful enrichment.
generateResultsTable provides the entries
whose p-score is below the chosen
threshold in a tabular format.
generateEnzymesTable returns a table
that contains (1) the enzymes that are below the user-defined
p-score threshold, along with (2) the genes that belong to
the enzymatic families in the organism defined in the database,
and (3) GO labels of such enzymes, if
NULL and points to the right database.
generateResultsGraph returns an
object with a relevant sub-network
for manual examination.
object with a successful enrichment analysis and the corresponding
FELLA.DATA must be supplied.
Graph nodes are prioritised by
p.score and selected through
the most stringent between (1) p.score
(2) maximum number of nodes
There is an additional filtering feature for tiny connected components,
(smaller is stricter).
The user can choose to turn off this filter by setting
thresholdConnectedComponent = 1.
The idea is to discard connected components so small
that are likely to arise from random selection of nodes.
k be the order of the current sub-network.
A connected component of order
be kept only if the probability that a
random subgraph from the whole KEGG knowledge model
k contains a
connected component of order at least
is smaller than
Such probabilities are estimated during
buildDataFromGraph; the amount of random
trials can be controlled by its
exportResults writes the enrichment results
as the specified filetype.
Options are: a csv table (
an enzyme csv table (
object as an
or any format supported by igraph's
addGOToGraph takes and returns
a graph object with class
adding the following attributes:
GO labels in
semantic similarities in
GOterm != NULL.
The GO database describes genes in terms of three ontologies:
molecular function (MF), biological process (BP) and
cellular component (CC) [Gene Ontology Consortium, 2015].
The user can be interested in finding which enzymatic families
reported with a low
are closest to a particular GO term.
To assess similarity between GO labels, FELLA uses the
semantic similarity defined in [Yu, 2010] and their implementation
in the GOSemSim R package.
The user will obtain, for each enzymatic family, the closest GO
term to his or her GO query and the semantic similarity between them.
Exact matches have a similarity of
plotGraph detects the presence
of the GO similarity option and plots its magnitude.
plots a solution graph from the diffusion and pagerank analysis.
For plotting hypergeom results, please use
Specific colors and shapes for each KEGG category are used:
pathways are maroon, modules are violet, enzymes are orange,
reactions are blue and compounds are green.
If the graph contains the similarity to a GO term, enzymes will
be displayed as triangles whose color depicts the strength of
such measure (yellow: weak, purple: strong).
At the moment,
plotGraph allows plotting
throug the static
plot.igraph and the
generateResultsTable returns a
data.frame that contains the nodes below the
from an enrichment analysis
generateEnzymesTable returns a
data.frame that contains the enzymes below the
along with their genes and GO labels
object: a sub-network from the whole
KEGG knowledge model under the specified thresholds
but as a side effect the specified
file is created.
an igraph object,
which is the input
extra attributes: GO labels in
semantic similarities in
GOterm != NULL
layout = F and
the plotting layout as a data.frame otherwise.
Gene Ontology Consortium. (2015). Gene ontology consortium: going forward. Nucleic acids research, 43(D1), D1049-D1056.
Yu, G., Li, F., Qin, Y., Bo, X., Wu, Y., & Wang, S. (2010). GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics, 26(7), 976-978.
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## First generate a toy enrichment library(igraph) data(FELLA.sample) data(input.sample) ## Enrich input obj <- enrich( compounds = input.sample, data = FELLA.sample) ###################### ## Results table tab.res <- generateResultsTable( method = "hypergeom", threshold = 0.1, object = obj, data = FELLA.sample) head(tab.res) tab.res <- generateResultsTable( method = "diffusion", threshold = 0.1, object = obj, data = FELLA.sample) head(tab.res) ###################### ## Use wrapper to write the table to a file out.file <- tempfile() exportResults( format = "csv", threshold = 0.1, file = out.file, object = obj, data = FELLA.sample) tab.wrap <- read.csv(out.file) head(tab.wrap) ###################### ## Enzymes table tab.ec <- generateEnzymesTable( threshold = 0.1, object = obj, data = FELLA.sample, mart.options = NULL) head(tab.ec) ###################### ## Generate graph g.res <- generateResultsGraph( method = "pagerank", threshold = 0.1, object = obj, data = FELLA.sample) g.res ## Plot graph (without GO terms) plotGraph(g.res) ## Add similarity to the GO CC term "mitochondrion" ## Not run: g.cc <- FELLA:::addGOToGraph( graph = g.res, GOterm = "GO:0005739") ## Plot graph (with GO terms) plotGraph(g.cc) ## Without the CC any(V(g.res)$GO.simil >= 0) ## With the CC v.cc <- unlist(V(g.cc)$GO.simil) sum(v.cc >= 0, na.rm = TRUE) ## Similarity values table(v.cc) ## End(Not run)
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