RSeed-class: Class "RSeed"
In RSeed: Borenstein Analysis
Description
Objects from the Class
Slots
Methods
Note
Author(s)
References
See Also
Examples
Description
Datastructure to perform Borenstein algorithm and save results.
Objects from the Class
Objects can be created by calls of the form new("RSeed", model, connectedComponentCutOff, currencyMetabolites).
This is the startup, where the basic parameters are set. They can also be set later.
Slots
note: this class does only have one slot: an environment. to access data, the usere has to use the methods supplied.
env:Object of class "environment"
Methods
- buildGraph
signature(rs = "RSeed"):
Builds a graph from the stoichiometric matrix. In this process is checked weather the graph is connected. Unconnected parts are removed from the graph, if they are smaller than 'connectedComponentCutOff'. If the parts are greater, there will be an error.
- combined_nodes
signature(rs = "RSeed"):
Names of the nodes, which combine metabolites.
- combined_nodes<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- confidenceLevel
signature(rs = "RSeed"):
Return a confidence level for every seed compound as defined in the original paper. The threshold is 0.2.
- connectedComponentCutOff
signature(rs = "RSeed"):
Return the cut off, until which size it is allowed to remove unconnected components from the graph. Default value is 10.
- connectedComponentCutOff<-
signature(rs = "RSeed"):
Set this Value.
- currencyMetabolites
signature(rs = "RSeed"):
metabolite IDs, which should be treated as currency metabolites and therefor be removed from the network. If this value is NULL, nothing is removed.
- currencyMetabolites<-
signature(rs = "RSeed"):
Sets this slot. The replace Value has to be a character vector. The return value from 'findCurrencyMetabolites' is suitable.
- getSourceMetabolites
signature(rs = "RSeed"):
Returns a list, with the metabolite IDs of the as essential identified metabolites. Each list entry represents a source compound.
- graph_network
signature(rs = "RSeed"):
Graph representation of the network.
- graph_network<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- graph_scc
signature(rs = "RSeed"):
Graph representation of the network with the nodes, which belong in one scc, aggregated to one node.
- graph_scc<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- initialize
signature(.Object = "RSeed"):
General initialize method. Automatically called from 'new'.
- list_sc
signature(rs = "RSeed"):
Return node names of source compounds.
- list_sc<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- makeExperiment
signature(rs = "RSeed"):
Performs the whole analysis. The result is stored in the object and in addition to this a reference is returned, too.
- model_changes
signature(rs = "RSeed"):
Returns the names of removed metabolites and reactions. These metabolites were either removed because they had no connection to the biggest part in the graph, or because they were given as source metabolites. Reactions are removed if all participating metabolites were removed.
- model_changes<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- model_edited
signature(rs = "RSeed"):
The 'modelorg' after removing metabolites and reactions.
- model_edited<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- model_original
signature(rs = "RSeed"):
The 'modelorg' from the beginning.
- model_original<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- plot
signature(x = "RSeed", y = "missing"):
Plots the graph_scc. Rgraphviz is needed.
- scc_sizes
signature(rs = "RSeed"):
Sizes of the SCCs.
- scc_sizes<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- scc
signature(rs = "RSeed"):
Returns a named list, which represents the aggregated nodes for every SCC (strongly connected component).
- scc<-
signature(rs = "RSeed"):
Setter method for this slot. This should not used by the user, until u really know, what ur doing!
- sourceCompounds
signature(rs = "RSeed"):
Aggregates metabolites from the same SCC into a common node.
- show
signature(object = "RSeed"):
Prints a short summary about the Object. (Called from generic 'print')
Note
Most slots of this object are automatically set by the algorithm and contain calculated results. So there is low sence in setting them by ur own.
Nodenames are chosen as follows: 'n' + the number of the metabolite. So n4 would mean, that met_id(model)[4] is the represented metabolite.
Author(s)
Claus Jonathan Fritzemeier
<clausjonathan.fritzemeier@uni-duesseldorf.de>
References
Master Thesis
by Claus Jonathan Fritzemeier
at Heinrich-Heine-Universitaet Duesseldorf,
Department of Bioinformatics
in 2012
Borenstein et. al. 2008
See Also
findCurrencyMetabolites,
modelorg
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
data(Ec_core)
ec <- new("RSeed", Ec_core)
# run the experiment
# calls of
# buildGraph(ec)
# sourceCompounds(ec)
# would do the same in two steps.
makeExperiment(ec)
# look which nodes aggregate
scc(ec)
# look at source metabolites
# (every list entry is a source compound)
getSourceMetabolites(ec)
# plot the graph
plot(ec)
Example output
Loading required package: sybil
Loading required package: Matrix
Loading required package: lattice
Loading required package: RBGL
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
building graph
preprocessing: checking if graph is connected
graph is connected
==Original Model:
model name: Ecoli_core_model
number of compartments 2
C_c
C_e
number of reactions: 95
number of metabolites: 72
number of unique genes: 137
objective function: +1 Biomass_Ecoli_core_w_GAM
==Connected Component Cut Off:
[1] 10
==currencyMetabolites:
NULL
==Edited Model:
NULL
==Model Changes:
rmMet rmReact
0 0
==Graph of the Network:
A graphNEL graph with directed edges
Number of Nodes = 72
Number of Edges = 455
==List of the Strong Connected Components:
[1] 7
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 1.00 1.00 10.29 1.50 65.00
==Graph of the Strong Connected Components:
A graphAM graph with directed edges
Number of Nodes = 7
Number of Edges = 6
==List of the Source Compounds:
[1] 6
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 1.000 1.167 1.000 2.000
$c1
[1] "n1" "n2" "n3" "n4" "n5" "n6" "n7" "n8" "n9" "n10" "n11" "n12"
[13] "n13" "n14" "n15" "n16" "n17" "n18" "n19" "n20" "n21" "n22" "n23" "n24"
[25] "n25" "n26" "n27" "n28" "n29" "n31" "n33" "n34" "n36" "n38" "n39" "n40"
[37] "n41" "n42" "n43" "n44" "n45" "n46" "n47" "n48" "n50" "n51" "n52" "n53"
[49] "n54" "n55" "n58" "n59" "n60" "n61" "n62" "n63" "n64" "n65" "n66" "n67"
[61] "n68" "n69" "n70" "n71" "n72"
$n30
[1] "n30"
$n32
[1] "n32"
$n35
[1] "n35"
$n37
[1] "n37"
$n49
[1] "n49"
$c7
[1] "n56" "n57"
$n30
[1] "fru[e]"
$n32
[1] "fum[e]"
$n35
[1] "glc_D[e]"
$n37
[1] "gln_L[e]"
$n49
[1] "mal_L[e]"
$c7
[1] "o2[c]" "o2[e]"
Loading required namespace: Rgraphviz
RSeed documentation built on May 2, 2019, 2:38 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Objects from the Class Slots Methods Note Author(s) References See Also Examples
Description
Datastructure to perform Borenstein algorithm and save results.
Objects from the Class
Objects can be created by calls of the form new("RSeed", model, connectedComponentCutOff, currencyMetabolites).
This is the startup, where the basic parameters are set. They can also be set later.
Slots
note: this class does only have one slot: an environment. to access data, the usere has to use the methods supplied.
env:Object of class
"environment"
Methods
- buildGraph
signature(rs = "RSeed"): Builds a graph from the stoichiometric matrix. In this process is checked weather the graph is connected. Unconnected parts are removed from the graph, if they are smaller than 'connectedComponentCutOff'. If the parts are greater, there will be an error.- combined_nodes
signature(rs = "RSeed"): Names of the nodes, which combine metabolites.- combined_nodes<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- confidenceLevel
signature(rs = "RSeed"): Return a confidence level for every seed compound as defined in the original paper. The threshold is 0.2.- connectedComponentCutOff
signature(rs = "RSeed"): Return the cut off, until which size it is allowed to remove unconnected components from the graph. Default value is 10.- connectedComponentCutOff<-
signature(rs = "RSeed"): Set this Value.- currencyMetabolites
signature(rs = "RSeed"): metabolite IDs, which should be treated as currency metabolites and therefor be removed from the network. If this value is NULL, nothing is removed.- currencyMetabolites<-
signature(rs = "RSeed"): Sets this slot. The replace Value has to be a character vector. The return value from 'findCurrencyMetabolites' is suitable.- getSourceMetabolites
signature(rs = "RSeed"): Returns a list, with the metabolite IDs of the as essential identified metabolites. Each list entry represents a source compound.- graph_network
signature(rs = "RSeed"): Graph representation of the network.- graph_network<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- graph_scc
signature(rs = "RSeed"): Graph representation of the network with the nodes, which belong in one scc, aggregated to one node.- graph_scc<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- initialize
signature(.Object = "RSeed"): General initialize method. Automatically called from 'new'.- list_sc
signature(rs = "RSeed"): Return node names of source compounds.- list_sc<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- makeExperiment
signature(rs = "RSeed"): Performs the whole analysis. The result is stored in the object and in addition to this a reference is returned, too.- model_changes
signature(rs = "RSeed"): Returns the names of removed metabolites and reactions. These metabolites were either removed because they had no connection to the biggest part in the graph, or because they were given as source metabolites. Reactions are removed if all participating metabolites were removed.- model_changes<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- model_edited
signature(rs = "RSeed"): The 'modelorg' after removing metabolites and reactions.- model_edited<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- model_original
signature(rs = "RSeed"): The 'modelorg' from the beginning.- model_original<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- plot
signature(x = "RSeed", y = "missing"): Plots the graph_scc. Rgraphviz is needed.- scc_sizes
signature(rs = "RSeed"): Sizes of the SCCs.- scc_sizes<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- scc
signature(rs = "RSeed"): Returns a named list, which represents the aggregated nodes for every SCC (strongly connected component).- scc<-
signature(rs = "RSeed"): Setter method for this slot. This should not used by the user, until u really know, what ur doing!- sourceCompounds
signature(rs = "RSeed"): Aggregates metabolites from the same SCC into a common node.- show
signature(object = "RSeed"): Prints a short summary about the Object. (Called from generic 'print')
Note
Most slots of this object are automatically set by the algorithm and contain calculated results. So there is low sence in setting them by ur own.
Nodenames are chosen as follows: 'n' + the number of the metabolite. So n4 would mean, that met_id(model)[4] is the represented metabolite.
Author(s)
Claus Jonathan Fritzemeier
<clausjonathan.fritzemeier@uni-duesseldorf.de>
References
Master Thesis
by Claus Jonathan Fritzemeier
at Heinrich-Heine-Universitaet Duesseldorf,
Department of Bioinformatics
in 2012
Borenstein et. al. 2008
See Also
findCurrencyMetabolites,
modelorg
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data(Ec_core)
ec <- new("RSeed", Ec_core)
# run the experiment
# calls of
# buildGraph(ec)
# sourceCompounds(ec)
# would do the same in two steps.
makeExperiment(ec)
# look which nodes aggregate
scc(ec)
# look at source metabolites
# (every list entry is a source compound)
getSourceMetabolites(ec)
# plot the graph
plot(ec)
|
Example output
Loading required package: sybil
Loading required package: Matrix
Loading required package: lattice
Loading required package: RBGL
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
building graph
preprocessing: checking if graph is connected
graph is connected
==Original Model:
model name: Ecoli_core_model
number of compartments 2
C_c
C_e
number of reactions: 95
number of metabolites: 72
number of unique genes: 137
objective function: +1 Biomass_Ecoli_core_w_GAM
==Connected Component Cut Off:
[1] 10
==currencyMetabolites:
NULL
==Edited Model:
NULL
==Model Changes:
rmMet rmReact
0 0
==Graph of the Network:
A graphNEL graph with directed edges
Number of Nodes = 72
Number of Edges = 455
==List of the Strong Connected Components:
[1] 7
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 1.00 1.00 10.29 1.50 65.00
==Graph of the Strong Connected Components:
A graphAM graph with directed edges
Number of Nodes = 7
Number of Edges = 6
==List of the Source Compounds:
[1] 6
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 1.000 1.167 1.000 2.000
$c1
[1] "n1" "n2" "n3" "n4" "n5" "n6" "n7" "n8" "n9" "n10" "n11" "n12"
[13] "n13" "n14" "n15" "n16" "n17" "n18" "n19" "n20" "n21" "n22" "n23" "n24"
[25] "n25" "n26" "n27" "n28" "n29" "n31" "n33" "n34" "n36" "n38" "n39" "n40"
[37] "n41" "n42" "n43" "n44" "n45" "n46" "n47" "n48" "n50" "n51" "n52" "n53"
[49] "n54" "n55" "n58" "n59" "n60" "n61" "n62" "n63" "n64" "n65" "n66" "n67"
[61] "n68" "n69" "n70" "n71" "n72"
$n30
[1] "n30"
$n32
[1] "n32"
$n35
[1] "n35"
$n37
[1] "n37"
$n49
[1] "n49"
$c7
[1] "n56" "n57"
$n30
[1] "fru[e]"
$n32
[1] "fum[e]"
$n35
[1] "glc_D[e]"
$n37
[1] "gln_L[e]"
$n49
[1] "mal_L[e]"
$c7
[1] "o2[c]" "o2[e]"
Loading required namespace: Rgraphviz
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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