GraphClust: GraphClust

In GraphPAC: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach.

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)

GraphPAC documentation built on Nov. 8, 2020, 5:36 p.m.