Package 'GraphPAC'

Using Graph Theory to Identify Mutational Clusters of Amino Acids.

Description

The GraphPAC package identifies statistically significant clusters of non-synonomous amino acid mutations and is a sister package to iPAC. GraphPAC reorders the protein into a one dimensional space via a graph theoretrical approach. Specifically, the traveling salesman problem (TSP) is solved heuristically via the TSP package. Once solved, the mutational data is reordered to follow the hamiltonian path and the nmc algorithm is run to find the mutational clusters on the remapped protein. Unlike the MDS remapping approach that is used in iPAC, distant amino acids no longer have an effect on each other's position in one dimensional space allowing for a closer representation of the underlying biology.

Details

Please see the documentation for “get.Positions", “get.AlignedPositions", and "Plot.Protein.Linear" in the iPAC package. There you will find information on getting basic positional data and plotting functions.

Author(s)

Gregory Ryslik Hongyu Zhao

Maintainer: Gregory A. Ryslik <[email protected]>

References

Ye et. al., Statistical method on nonrandom clustering with application to somatic mutations in cancer. BMC Bioinformatics. 2010. doi:10.1186/1471-2105-11-11.

Michael Hahsler and Kurt Hornik (2011). Traveling Salesperson Problem (TSP) R package version 1.0-7. http://CRAN.R-project.org/.

Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.sf.net

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.1.3. http://www.bioconductor.org/.

Bioconductor: Open software development for computational biology and bioinformatics R. Gentleman, V. J. Carey, D. M. Bates, B.Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, and others 2004, Genome Biology, Vol. 5, R80

Examples

## Not run: 
#Load the positional and mutatioanl data
CIF<-"https://files.rcsb.org/view/3GFT.cif"
Fasta<-"https://www.uniprot.org/uniprot/P01116-2.fasta"
KRAS.Positions<-get.Positions(CIF,Fasta, "A")
data(KRAS.Mutations)

#Calculate the required clusters
GraphClust(KRAS.Mutations,KRAS.Positions$Positions,insertion.type = "cheapest_insertion",
		   alpha = 0.05, MultComp = "Bonferroni")

## End(Not run)

---

Find.TSP.Path

Description

Employs a heuristic approach to solve the traveling salesman problem.

Usage

Find.TSP.Path(PositionList, mutation.matrix, insertion.type = "cheapest_insertion",
			  fix.start.pos = "Y")

Arguments

PositionList	A dataframe consisting of six columns: 1) Residue Name, 2) Amino Acid number in the protein, 3) Side Chain, 4) X-coordinate, 5) Y-coordinate and 6) Z-coordinate. Please see get.Positions and get.AlignedPositions in the iPAC package for further information on how to construct this matrix.
mutation.matrix	A matrix of 0's (no mutation) and 1's (mutation) where each column represents an amino acid in the protein and each row represents an individual sample (test subject, cell line, etc). Thus if column i in row j had a 1, that would mean that the ith amino acid for person j had a nonsynonomous mutation.
insertion.type	Specifies the type of insertion method used. Please see the TSP package for more details.
fix.start.pos	The TSP package starts the path at a random amino acid. Such that the results are easily reproducible, the default starts the path on the first amino acid in the protein.

Value

candidate.path	A numeric vector of the sequence found through the protein.
candidate.path.distance	The distance traveled along the candidate path.
dist.matrix	The distance matrix between any two pairwise amino acids.
linear.path.distance	The distance traveled if one were to visit the amino acids in the original sequence (1 -> 2 -> 3 -> ...->N

References

Michael Hahsler and Kurt Hornik (2011). Traveling Salesperson Problem (TSP) R package version 1.0-7. http://CRAN.R-project.org/.

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.1.3. http://www.bioconductor.org/.

Examples

#Load the position and mutational data
CIF<-"https://files.rcsb.org/view/3GFT.cif"
Fasta<-"https://www.uniprot.org/uniprot/P01116-2.fasta"
KRAS.Positions<-get.Positions(CIF,Fasta, "A")
data(KRAS.Mutations)

#Save all the results to path.results
path.results <- Find.TSP.Path(KRAS.Positions$Positions, KRAS.Mutations)

---

GraphClust

Description

Finds mutational clusters after reordering the protein using the traveling salesman approach.

Usage

GraphClust(mutation.data, position.data, insertion.type = "cheapest_insertion", alpha = 0.05,
		   MultComp = "Bonferroni", fix.start.pos = "Y", Include.Culled = "Y",
		   Include.Full = "Y")

Arguments

mutation.data	A matrix of 0's (no mutation) and 1's (mutation) where each column represents an amino acid in the protein and each row represents an individual sample (test subject, cell line, etc). Thus if column i in row j had a 1, that would mean that the ith amino acid for person j had a nonsynonomous mutation.
position.data	A dataframe consisting of six columns: 1) Residue Name, 2) Amino Acid number in the protein, 3) Side Chain, 4) X-coordinate, 5) Y-coordinate and 6) Z-coordinate. Please see get.Positions and get.AlignedPositions in the iPAC package for further information on how to construct this matrix.
insertion.type	Specifies the type of insertion method used. Please see the TSP package for more details.
alpha	The significance level required in order to find a mutational cluster significance. Please see the NMC package for further information.
MultComp	The multiple comparison adjustment required as all pairwise mutations are considered. Options are: “Bonferroni", "BH", or "None".
fix.start.pos	The TSP package starts the path at a random amino acid. Such that the results are easily reproducible, the default starts the path on the first amino acid in the protein.
Include.Culled	If "Y", the standard NMC algorithm will be run on the protein after removing the amino acids for which there is no positional data.
Include.Full	If "Y", the standard NMC algorithm will be run on the full protein sequence.

Details

The protein reordering is done using the TSP package available on CRAN. This hamiltonian path then serves as the new protein ordering.

The position data can be created via the “get.AlignedPositions" or the “get.Positions" functions available via the imported iPAC package.

The mutation matrix must have the default R column headings “V1", “V2",...,“VN", where N is the last amino acid in the protein. No positions should be skipped in the mutaion matrix.

When unmapping back to the original space, the end points of the cluster in the mapped space are used as the endpoints of the cluster in the unmapped space.

Value

Remapped	This shows the clusters found while taking the 3D structure into account and remapping the protein using a traveling salesman approach.
OriginalCulled	This shows the clusters found if you run the NMC algorithm on the canonical linear protein, but with the amino acids for which we don't have 3D positional data removed.
Original	This shows the clusters found if you run the NMC algorithn on the canonical linear protein with all the amino acids.
candidate.path	This shows the path found by the TSP package that heuristically minimizes the total distance through the protein.
path.distance	The length of the candidate path if traveled from start to finish.
linear.path.distance	The length of the sequential path 1,2,3...,N (where N is the total number of amino acids in the protein).
protein.graph	A graph object created by the igraph package that has edges between amino acids on the candidate.path. This can be passed to plotting functions to create visual represnetations.
missing.positions	This shows which amino acids are present in the mutation matrix but for which we do not have positions. These amino acids are cut from the protein when calculating the Remapped and OriginalCulled results.

References

Ye et. al., Statistical method on nonrandom clustering with application to somatic mutations in cancer. BMC Bioinformatics. 2010. doi:10.1186/1471-2105-11-11.

Michael Hahsler and Kurt Hornik (2011). Traveling Salesperson Problem (TSP) R package version 1.0-7. http://CRAN.R-project.org/.

Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.sf.net

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.1.3. http://www.bioconductor.org/.

Examples

## Not run: 
#Load the positional and mutatioanl data
CIF<-"https://files.rcsb.org/view/3GFT.cif"
Fasta<-"https://www.uniprot.org/uniprot/P01116-2.fasta"
KRAS.Positions<-get.Positions(CIF,Fasta, "A")
data(KRAS.Mutations)

#Calculate the required clusters
GraphClust(KRAS.Mutations,KRAS.Positions$Positions,insertion.type = "cheapest_insertion",
	   alpha = 0.05, MultComp = "Bonferroni")

## End(Not run)

---

Plot.Protein

Description

Creates a circular interactive plot of the path through the protein.

Usage

Plot.Protein(graph, path, vertex.size = 5, color.palette = "heat")

Arguments

graph	The graph object returned by GraphClust ($protein.graph).
path	The path returned by GraphClust ($candidate.path).
vertex.size	How large you want each vertex to be.
color.palette	Possible options are: "heat", "gray", "topo", "cm".

Details

This will plot the amino acids in a circular directed graph. The vertices can be dragged around to enhance the visual representation. This is meant to complement the Plot.Protein.Linear function in iPAC which is also applicable in this package.

Note

This function is based on the “tkplot" function in igraph. Please see the documentation for that package for the necessary requirements. Special thanks to Dr. G\'abor Cs\'ardi (creator of the igraph package) for his help.

References

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.1.3. http://www.bioconductor.org/.

Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.sf.net.

Examples

## Not run: 
#Loads the mutational and positional data
CIF<-"https://files.rcsb.org/view/3GFT.cif"
Fasta<-"https://www.uniprot.org/uniprot/P01116-2.fasta"
KRAS.Positions<-get.Positions(CIF,Fasta, "A")
data(KRAS.Mutations)

#gets the cluster results and graph object
my.graph.clusters <- GraphClust(KRAS.Mutations,KRAS.Positions$Positions,
								insertion.type = "cheapest_insertion",alpha = 0.05,
								MultComp = "Bonferroni")

Plot.Protein(my.graph.clusters$protein.graph, my.graph.clusters$candidate.path,
			 vertex.size=5, color.palette="heat")

## End(Not run)

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