From BIOlogical Sequences to MultiDimensional Scaling

Description

The bios2mds package is developed in the Bioinformatics team at Integrated Neurovascular Biology Laboratory, UMR CNRS 6214 / INSERM 771, University of Angers - FRANCE.

This package is dedicated to the analysis of biological sequences by metric MultiDimensional Scaling (MDS) with projection of supplementary data. It contains functions for reading multiple sequence alignment (MSA) files, calculating distance matrices from MSA files, performing MDS analysis and visualizing results. The MDS analysis and visualization tools can be applied to any kind of data.

The main functionalities of bios2mds are summarized below:

(1) BUILDING DISTANCE MATRICES FROM MULTIPLE SEQUENCE ALIGNMENTS :

Several functions allow users to read multiple sequence alignment files and to compute matrices of distances between these sequences:

• import.fasta: reads a multiple sequence alignment in FASTA format.

• import.msf: reads a multiple sequence alignment in MSF format.

• mat.dif: computes a matrix of pairwise distances between sequences based on sequence difference.

• mat.dis: computes a matrix of pairwise distances between sequences based on sequence dissimilarity.

(2) MULTIDIMENSIONAL SCALING :

A function performs metric MDS analysis of a distance matrix between active elements with the option of projecting supplementary elements onto the active space.

• mmds: performs metric multidimensional scaling.

• mmds.project: performs projection of supplementary elements onto the active space.

(3) GRAPHICAL TOOLS :

Several functions are proposed to visualize results of metric MDS analysis:

• scree.plot: draws the scree plot of eigenvalues.

• mmds.2D.plot: draws a scatter plot of the MDS coordinates.

• mmds.2D.multi: draws a scatter plot of the MDS coordinates with projection of multiple groups of supplementary element.

• mmds.3D.plot: Displays a 3D plot of the MDS coordinates.

• mmds.plot: wrapper function that draws the scree plot and three scatter plots of MDS coordinates.

• col.group: colors scatter plots with user provided groupings and colors.

• write.mmds.pdb: writes MDS coordinates in a PDB formatted file for 3D visualisation.

(4) CLUSTER ANALYSIS :

Several functions allow users to perform data clustering and to assess the clustering robustness:

• kmeans.run: performs multiple runs of K-means clustering and analyzes clusters

• sil.score: calculates the silhouette score from multiple K-means runs to determine the optimal number of clusters.

• extract.cluster: extraction of clusters alignments

(5) DATASETS :

Two raw datasets are proposed to test the functionalities of bios2mds. They correspond to the multiple sequence alignments of GPCRs from H. sapiens and D. melanogaster in .msf and .fa formats (msa/human_gpcr.* and msa/drome_gpcr.*). They are based on the non-redundant sets of non-olfactory class A GPCRs, prepared and analyzed in Deville et al. (2009) and updated with the July 2009 release of Uniprot http://www.uniprot.org. Each MSA file is related to a .csv file that assigns a group and a color to each sequence of the alignment (csv/human_gpcr_group.csv and csv/drome_gpcr_group.csv).

Pre-analyzed data from these two MSA files are in gpcr. Moreover, gpcr contain projection of GPCRs from N. vectensis and from C. intestinalis onto the active space of human GPCRs calculated by MDS analysis.

For an index of functions, use library(help = bios2mds).

Details

Author(s)

Julien Pele <julien.pele@yahoo.fr> with Jean-Michel Becu <jean-michel.becu@etu.univ-rouen.fr>, Herve Abdi <herve@utdallas.edu> and Marie Chabbert <marie.chabbert@univ-angers.fr>.

Maintainer: Marie Chabbert <marie.chabbert@univ-angers.fr>

References

citation('bios2mds')

Examples

# The MSA files provided with the package correspond to the sequence 
# alignment of non-olfactory class A G-protein-coupled receptors from
# H. sapiens and D. melanogaster prepared by Deville et al. (2009).  
# loading GPCR data
wd <- tempdir()
file <- file.path(wd,"R.pdb")
data(gpcr)

# building distance matrices between the aligned GPCR sequences from
# H. sapiens and D. melanogaster
human <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
drome <- import.fasta(system.file("msa/drome_gpcr.fa", package = "bios2mds"))

#active <- mat.dif(human, human)
# or
active <- gpcr$dif$sapiens.sapiens

#sup <- mat.dif(drome, human)
# or
sup <- gpcr$dif$melanogaster.sapiens

# performing MDS analysis of the GPCR sequences from H. sapiens 
mmds_active <- mmds(active, group.file=system.file(
"csv/human_gpcr_group.csv",package = "bios2mds"))

# performing MDS analysis of the GPCR sequences from H. sapiens 
# with projection of GPCRs from D. melanogaster 
# as supplementary elements onto the space of human GPCRs
mmds_sup <- mmds.project(mmds_active, sup,system.file(
"csv/drome_gpcr_group.csv",package = "bios2mds"))

# displaying MDS coordinates 
layout(matrix(1:6, 2, 3))

scree.plot(mmds_active$eigen.perc, lab = TRUE, title = "Scree plot of metric MDS")

mmds.2D <- mmds.2D.plot(mmds_active, title = "Sequence space of human GPCRs")

mmds.2D.plot(mmds_active,mmds_sup, title = "Projection of GPCRs from D. melanogaster
 onto the space space of human GPCRs ", active.alpha = 0.3)
   
# writing PDB files for 3D visualization of MDS coordinates
write.mmds.pdb(mmds_active,file.pdb=file)

Links elements in a mmds object to specific groups and colors

Description

Links elements in a mmds object or mmds.project object to user-provided groups and colors.

Usage

col.group(x,file)

Arguments

Details

col.group assigns each element of the mmds object to user-provided groupings and colors for coloring and labeling mmds scatter plots.

col.group requires a formatted file. See "csv/human_gpcr_group.csv" for an example. Each line corresponds to one element of the mmds object and must contain three parameters separated by ",".
The first parameter is the element name, as given in the multiple sequence alignment file.
The second parameter is the group name. Groupings must be provided by the user.
The third parameter is the group color in full letters (example : "black","green"). Two or more groups can have the same color, but elements within the same group must have the same color. The group is colored by the first color encountered.

Value

Adds data to a mmds object in order to color and label mmds scatter plots with user-provided groupings and colors.

Author(s)

Jean-Michel Becu

See Also

See colors function (default R package).
See getcol in made4 package for special color palette developed to maximize the contrast between colors.

Examples

# performing metric MDS on human GPCRs with projection of 
# GPCRs from D. melanogaster as supplementary data:
data(gpcr)
active <- gpcr$dif$sapiens.sapiens
mmds_active <- mmds(active)
mmds_active<-col.group(mmds_active,system.file("csv/human_gpcr_group.csv"
,package = "bios2mds"))

Difference score

Description

Measures the difference score between two aligned amino acid or nucleotide sequences.

Usage

dif(seq1, seq2, gap = FALSE, aa.strict = FALSE)

Arguments

Details

The difference score between two aligned sequences is given by the proportion of sites that differs and is equivalent to 1 - {PID} (percent identity). dif is given by the number of aligned positions (sites) whose symbols differ, divided by the number of aligned positions. dif is equivalent to the p distance defined by Nei and Zhang (2006). In dif, positions with at least one gap can be excluded (gap = FALSE). When gaps are taken as a supplementary symbol (gap = TRUE), sites with gaps in both sequences are excluded.

From Nei and Zhang (2006), the p distance, which is the proportion of sites that differ between two sequences, is estimated by:

{p} = \frac{n_d}{n},

where n is the number of sites and n_d is the number of sites with different symbols.

The difference score ranges from 0, for identical sequences, to 1, for completely different sequences.

Value

A single numeric value representing the difference score.

Author(s)

Julien Pele

References

May AC (2004) Percent sequence identity: the need to be explicit. Structure 12:737-738.

Nei M and Zhang J (2006) Evolutionary Distance: Estimation. Encyclopedia of Life Sciences.

Nei M and Kumar S (2000) Molecular Evolution and Phylogenetics. Oxford University Press, New York.

Examples

# calculating the difference score between the sequences 
# of CLTR1_HUMAN and CLTR2_HUMAN:
aln <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
dif <- dif(aln$CLTR1_HUMAN, aln$CLTR2_HUMAN)
dif

Dissimilarity score

Description

Computes the dissimilarity score between two aligned amino acid sequences, based on substitution matrices.

Usage

dis(seq1, seq2, sub.mat.id = "PAM250", gap = NULL)

Arguments

Details

Grishin and Grishin (2002) developped a method to calculate the similarity score with amino acid substitution matrices.

Let s be an amino acid substitution matrix with elements s(a, b), let A be an alignment of n sequences, A_{ik} is a symbol (amino acid or gap: '-') in the site k of the sequence i. For each pair of sequences i and j from A, the following equations(1), (2), (3) and (4) are calculated as follows:

(1)

S_{ij}, called score per site, is obtained as:

S_{ij} = \sum_{k \in K_{ij}}s(A_{ik}, A_{jk})/l(K_{ij})

where K_{ik} is the set of sites k such that A_{ik} != '-' and A_{jk} != '-' and l(K_{ij}) is the number of elements in K_{ij}.

(2)

T_{ij}, called average upper limit of the score per site, is obtained as:

T_{ij} = 0.5 \sum_{k \in K_{ij}}(s(A_{ik}, A_{ik}) + s(A_{jk}, A_{jk}))/l(K_{ij})

(3)

S^{rand}_{ij}, called score per site expected from random sequences, is obtained as:

S^{rand}_{ij} = \sum^{20}_{a=1}\sum^{20}_{b=1}f^i_j(a)f^j_i(b)s(a, b)

where f^i_j(a) is the frequency of amino acid 'a' in i^{th} protein sequence of A over all sites in K_{ij}.

(4)

V_{ij}, called normalized score (Feng and Doolittle, 1997), is obtained as:

V_{ij} = \frac{S_{ij} - S^{rand}_{ij}}{T_{ij} - S^{rand}_{ij}}

The normalized score V_{ij} ranges from 0 (for random sequences) to 1 (for identical sequences). However, for very divergent sequences, V_{ij} can become negative due to statistical errors. In this case, dis attributes 0 to negative scores.

The dissimilarity score D_{ij} between sequences i and j is obtained from the similarity score as:

D_{ij} = V_{ij} - 1

Value

A single numeric value representing the dissimilarity score.

Author(s)

Julien Pele

References

Grishin VN and Grishin NV (2002) Euclidian space and grouping of biological objects. Bioinformatics 18:1523-1534.

Feng DF and Doolittle RF (1997) Converting amino acid alignment scores into measures of evolutionary time: a simulation study of various relationships. J Mol Evol 44:361-370.

Examples

# calculating dis between the sequences of CLTR1_HUMAN and CLTR2_HUMAN:
aln <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
dis <- dis(aln$CLTR1_HUMAN, aln$CLTR2_HUMAN)
dis

Writes a MSA file in FASTA format

Description

Writes a multiple sequence alignment (MSA) file in FASTA format.

Usage

export.fasta(x, outfile = "alignment.fa", ncol = 60, open = "w")

Arguments

Details

Initially, FASTA (for FAST-ALL) was the input format of the FASTA program, used for protein comparison and searching in databases. Presently, FASTA format is a standard format for biological sequences.

The FASTA formatted file of a single sequence displays:

• a single-line description beginning with a greater-than (>) symbol. The following word is the identifier.

• followed by any number of lines, representing biological sequence.

For multiple alignments, the FASTA formatted sequences are concatenated to create a multiple FASTA format.

Value

Produces a FASTA file for an 'align' object or a FASTA file for each cluster in list.

Note

For further information about FASTA format, see: http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml

Author(s)

Jean-Michel Becu

See Also

write.fasta function from seqinr package.

Examples

# reading of the multiple sequence alignment of human GPCRS in FASTA format:
wd <- tempdir()
#wd <- getwd()
file1 <- file.path(wd,"alignment.fa")
aln <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
export.fasta(aln,file1)

Extraction of clusters alignments

Description

Extracts the multiple sequence alignement of each cluster after K-means clustering.

Usage

extract.cluster(x, align)

Arguments

Details

Extraction of the MSA of each cluster.

Value

A named list of 'align' objects.

Author(s)

Jean-Michel Becu

Examples

# Clustering human GPCRs in 4 groups with 100 runs of K-means 
# and extraction of the alignment of each cluster
aln <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
data(gpcr)
kmeans <- kmeans.run(gpcr$mmds$sapiens.active$coord, nb.clus = 4, nb.run = 100)
clusAlign <- extract.cluster(kmeans,aln)

Pre-analyzed G-Protein-Coupled Receptor (GPCR) data set

Description

This data set was obtained by the bios2mds analysis of the two multiple sequence alignment files provided in /msa. The MSA files were prepared as previously described by Deville et al. (2009) and updated with the July 2009 release of Uniprot (http://www.uniprot.org). They correspond to non-redundant sets of non-olfactory class A G-protein-coupled receptors (GPCRs) from H. sapiens and D. melanogaster (283 and 59 sequences, respectively). The data sets from H. sapiens and D. melanogaster constitute the active and supplementary data sets, respectively.

Usage

data(gpcr)

Format

gpcr is a named list of three elements:

dif

a named list containing two distance matrices calculated from the mat.dif function (distances based on difference scores) with default parameters:

sapiens.sapiens

a 283 by 283 matrix of difference scores between the 283 aligned sequences of human GPCRs.

melanogaster.sapiens

a 59 by 283 matrix of difference scores between the aligned sequences of GPCRs from H. sapiens and D. melanogaster (283 and 59 sequences, respectively)..

dis

a named list containing sixteen distance matrices calculated from the mat.dis function (distances based on dissimilarity scores) for the eight substitution matrices in sub.mat (other parameters by default):

sapiens.sapiens$*

are 283 by 283 matrices of dissimilarity scores between the 283 aligned sequences of human GPCRs.

sapiens.sapiens$PAM40

is calculated with PAM40.

sapiens.sapiens$PAM80

is calculated with PAM80.

sapiens.sapiens$PAM120

is calculated with PAM120.

sapiens.sapiens$PAM250

is calculated with PAM250.

sapiens.sapiens$BLOSUM30

is calculated with BLOSUM30.

sapiens.sapiens$BLOSUM45

is calculated with BLOSUM45.

sapiens.sapiens$BLOSUM62

is calculated with BLOSUM62.

sapiens.sapiens$BLOSUM80

is calculated with BLOSUM80.

sapiens.sapiens$GONNET

is calculated with GONNET.

sapiens.sapiens$JTT

is calculated with JTT.

sapiens.sapiens$JTT_TM

is calculated with JTT_TM.

sapiens.sapiens$PHAT

is calculated with PHAT.

melanogaster.sapiens$*

are 59 by 283 matrices of dissimilarity scores between the 283 aligned sequences from H. sapiens and D. melanogaster

melanogaster.sapiens$PAM40

is calculated with PAM40.

melanogaster.sapiens$PAM80

is calculated with PAM80.

melanogaster.sapiens$PAM120

is calculated with PAM120.

melanogaster.sapiens$PAM250

is calculated with PAM250.

melanogaster.sapiens$BLOSUM30

is calculated with BLOSUM30.

melanogaster.sapiens$BLOSUM45

is calculated with BLOSUM45.

melanogaster.sapiens$BLOSUM62

is calculated with BLOSUM62.

melanogaster.sapiens$BLOSUM80

is calculated with BLOSUM80.

melanogaster.sapiens$GONNET

is calculated with GONNET.

melanogaster.sapiens$JTT

is calculated with JTT.

melanogaster.sapiens$JTT_TM

is calculated with JTT_TM.

melanogaster.sapiens$PHAT

is calculated with PHAT.

mmds

a typical example of metric MDS analysis with the mmds function of bios2mds using GPCRs from H. sapiens as active data and GPCRs from D. melanogaster, N. vectensis and C. intestinalis for supplementary data.

sapiens.active

metric MDS analysis of active data from H. sapiens.

melanogaster.project

projection of supplementary data from D. melanogaster onto the human active space.

vectensis.project

projection of supplementary data from N. vectensis onto active the human space.

intestinalis.project

projection of supplementary data from C. intestinalis onto the human active space.

Source

Deville J, Rey J and Chabbert M (2009) An indel in transmembrane helix 2 helps to trace the molecular evolution of class A G-protein-coupled receptors. J Mol Evol 68: 475- 489.

Examples

# loading gpcr
data(gpcr)

# displaying the matrix of differences scores between GPCRs sequences
# from H. sapiens
gpcr$dif$sapiens.sapiens

# displaying the matrix of dissimilarity scores between GPCRs sequences
# from H. sapiens
gpcr$dis$sapiens.sapiens$PAM250

# displaying the matrix of dissimilarity scores between GPCRs sequences
# from H. sapiens and D. Melanogaster calculated with the BLOSUM45 matrix
gpcr$dis$melanogaster.sapiens$BLOSUM45

# displaying mmds analysis of the MSA of GPCRs from H. sapiens
# and D. Melanogaster
gpcr$mmds$sapiens.active
gpcr$mmds$melanogaster.project

Reads a file in FASTA format

Description

Reads a Multiple Sequence Alignment (MSA) file in FASTA format (.fasta or .fa extension).

Usage

import.fasta(file, aa.to.upper = TRUE, gap.to.dash = TRUE)

Arguments

Details

Initially, FASTA (for FAST-ALL) was the input format of the FASTA program, used for protein comparison and searching in databases. Presently, FASTA format is a standard format for biological sequences.

The FASTA formatted file of a single sequence displays:

• a single-line description beginning with a greater-than (>) symbol. The following word is the identifier.

• followed by any number of lines, representing biological sequence.

For multiple alignments, the FASTA formatted sequences are concatenated to create a multiple FASTA format.

Value

A object of class 'align', which is a named list whose elements correspond to sequences, in the form of character vectors.

Note

For further information about FASTA format, see: http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml

Author(s)

Julien Pele

References

Pearson WR and Lipman DJ (1988) Improved tools for biological sequence comparison. Proc Natl Acad Sci U S A 27:2444-2448.

See Also

read.fasta function from bio3d package.
read.fasta function from seqinr package.
read.FASTA function from aaMI package (archived).

Examples

# reading of the multiple sequence alignment of human GPCRS in FASTA format:
aln <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))

Reads a multiple sequence alignment file in MSF format

Description

Reads a Multiple Sequence Alignment (MSA) file in MSF format (.msf extension).

Usage

import.msf(file, aa.to.upper = TRUE, gap.to.dash = TRUE)

Arguments

Details

Initially, Multiple Sequence Format (MSF) was the multiple sequence alignment format of the Wisconsin Package (WP) or GCG (Genetic Computer Group). This package is a suite of over 130 sequence analysis programs for database searching, secondary structure prediction or sequence alignment. Presently, numerous multiple sequence alignment editors (Jalview and GeneDoc for example) can read and write MSF files.

MSF file displays several specificities:

• a header containing sequence identifiers and characteristics (length, check and weight).

• a separator symbolized by 2 slashes (//).

• sequences of identifiers, displayed by consecutive blocks.

Value

A object of class 'align', which is a named list whose elements correspond to sequences, in the form of character vectors.

Note

import.msf checks the presence of duplicated identifiers in header. Sequences whose identifiers are missing in header are ignored.

Author(s)

Julien Pele

See Also

read.alignment function from seqinr package.
read.GDoc function from aaMI package (archived).

Examples

# reading of the multiple sequence alignment of human GPCRs in MSF format:
aln <- import.msf(system.file("msa/human_gpcr.msf", package = "bios2mds"))

Multiple runs of K-means analysis

Description

Performs multiple runs of K-means clustering and analyzes data.

Usage

kmeans.run(mat, nb.clus = 2, nb.run = 1000, iter.max = 10000,
method = "euclidean")

Arguments

Details

The aim of K-means clustering is the partition of elements into a user-provided number of clusters. Several runs of K-means analysis on the same data may return different cluster assignments because the K-means procedure attributes random initial centroids for each run. The robustness of an assignment depends on its reproducibility.

The function matchClasses from the e1071 package is used to compare the cluster assignments of the different runs and returns a score of agreement between them. The most frequent clustering solution is selected and used as a reference to assess the reproducibility of the analysis.

kmeans.run returns two lists. In either list, the clusters refer to those observed in the most frequent solution. The first list provides, for each element, the relative ratio of its assignment to each cluster in the different runs. The second list provides, for each cluster, the list of the assigned elements along with the relative assignment to this cluster in the different runs.

Value

A object of class 'kmean', which is a named list of two elements

Note

During the K-means procedure, an empty cluster can be obtained if no objects are allocated to the cluster. In kmeans.run, runs with empty clusters are discarded.

kmeans.run requires Kmeans and matchClasses functions from amap and e1071 packages, respectively.

Author(s)

Julien Pele

Examples

# Clustering human GPCRs in 4 groups with 100 runs of K-means
data(gpcr)
coord <- gpcr$mmds$sapiens.active$coord
kmeans.run1 <- kmeans.run(coord, nb.clus = 4, nb.run = 100)
kmeans.run1$clusters
kmeans.run1$elements

Matrices of difference scores between sequences

Description

Computes a matrix providing the distances based on the difference scores between sequences from two multiple sequence alignments.

Usage

mat.dif(align1, align2, gap = FALSE, aa.strict = FALSE, sqrt = FALSE)

Arguments

Details

If align1 and align2 are identical, mat.dif computes the symetrical matrix of distances between each sequence of the alignment.

Before using mat.dif, users must check the alignment of sequences within align1 and align2 and between align1 and align2.

Value

A named numeric matrix providing the difference-based distances between each pair of sequences from align1 and align2. The number of rows and columns is identical to the number of sequences in align1 and align2, respectively.

Author(s)

Julien Pele and Jean-Michel Becu

See Also

identity function from bio3d package.

Examples

# calculating the matrix of distances based on the difference scores
# between GPCRs sample from H. sapiens and D. melanogaster:
aln_human <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
aln_drome <- import.fasta(system.file("msa/drome_gpcr.fa", package = "bios2mds"))
mat.dif1 <- mat.dif(aln_human[1:5], aln_drome[1:5])
mat.dif1

Matrices of dissimilarity scores between amino acid sequences

Description

Computes a matrix providing the distances based on dissimilarity scores between sequences from two multiple sequence alignments.

Usage

mat.dis(align1, align2, sub.mat.id = "PAM250", sqrt=FALSE)

Arguments

Details

The dissimilarity score between a sequence i from align1 and a sequence j from align2 is calculated with an amino acid substitution matrix from sub.mat.

If align1 and align2 are identical, mat.dis computes the symetrical matrix of distances between each sequence of the alignment.

Before using mat.dis, users must check the alignment of sequences within align1 and align2 and between align1 and align2.

Value

A named numeric matrix providing the dissimilarity-based distances between each pair of sequences from align1 and align2, based on the substitution matrix sub.mat.id. The number of rows and columns is identical to the number of sequences in align1 and align2, respectively.

Author(s)

Julien Pele and Jean-Michel Becu

Examples

# calculating dissimilarity distances between GPCR sequences sample from 
#H. sapiens and D. melanogaster, based on the PAM250 matrix:
aln_human <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
aln_drome <- import.fasta(system.file("msa/drome_gpcr.fa", package = "bios2mds"))
mat.dis1 <- mat.dis(aln_human[1:5], aln_drome[1:5])
mat.dis1

# calculating dissimilarity distances between GPCRs sequences sample from 
#H. sapiens and D. melanogaster, based on the BLOSUM45 matrix:
aln_human <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
aln_drome <- import.fasta(system.file("msa/drome_gpcr.fa", package = "bios2mds"))
mat.dis1 <- mat.dis(aln_human[1:5], aln_drome[1:5], sub.mat.id = "BLOSUM45")
mat.dis1

Metric multidimensional scaling

Description

Performs metric MultiDimensional Scaling (MDS) analysis of active elements.

Usage

mmds(active, pc = 3, group.file = NULL)

Arguments

Details

Metric multidimensional scaling is a statistical analysis technique aimed at analyzing a matrix of distances between 'active' elements. MDS maps the elements onto a low dimensional space (usually 2D or 3D), In this space, elements are represented by points whose respective distances best approximate the initial distance.

active must have some characteristics:

• active represents the matrix of pairwise distances between active elements. The active matrix must be symmetric (square and equals to its transpose). The distances on the main diagonal must be equal to 0. The distances do not have to be Euclidean. They can just express a difference or dissimilarity score, with a 0 value between same elements and a positive value between different elements.

The method for the computation of metric MDS is described by Abdi (2007). Briefly, if N is the number of active sequences and D is the N by N matrix of the squared distances computed from the active matrix, the mmds function performs the following steps:

(1)

Transforms D into a cross-product matrix S:

\bold{S} = -0.5 [\bold{I} - \bold{m}^{T}\bold{1}] \times \bold{D} \times [\bold{I} - \bold{m}^{T}\bold{1}]^{T}

where I is the N by N identity matrix, m is the N by 1 matrix mass, where each mass equal to \frac{1}{N} and 1 is an N by N matrix of ones.

(2)

Transforms S into a factor matrix F:

\bold{F} = \bold{M}^{\frac{-1}{2}}\bold{U\Lambda}^{\frac{1}{2}}

where M is the diag{m}, U is the eigenvector matrix and \bold{\Lambda} is the diagonal matrix of the eigenvalues, such as S = \bold{{U{\Lambda}U}^T}, where \bold{^T} denotes the transposition operation.

The eigenvectors of S, also called principal components (whose number is smaller or equal to N), form the active space. F gives the coordinates of the active elements in this space.

Value

A object of class 'mmds', which is a named list of five elements:

Note

If active do not contain names:
A tag "A" followed by an incremented number names the rows and the columns of active.

Author(s)

Julien Pele and Jean-Michel Becu

References

Abdi H (2007) Metric multidimensional scaling. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 598-605.

For further information on multidimentional scaling:

Takane Y, Jung S and Oshima-Takane Y (2009) Multidimensional scaling, in Handbook of quantitative methods in psychology, eds Millsap R, Maydeu-Olivares A (Sage Publications, London) pp. 219-242.

Borg I and Groenen PJF (2005) Modern multidimensional scaling. New York : Springer.

Gower JC (1967) A comparison of some methods of cluster analysis. Biometrics 23:623-637.

ToRgerson WS (1958) Theory and methods of scaling. New York : Wiley.

See Also

cmdscale function from stats package.
dudi.pco, suprow and supcol functions from ade4 package.
PCA function from FactoMineR package.

Examples

# performing metric MDS of human GPCRs with projection of
# GPCRs from D. melanogaster as supplementary elements:
data(gpcr)
active <- gpcr$dif$sapiens.sapiens
mmds1 <- mmds(active = active)
mmds1$active.coord

Plots the mmds coordinates onto a 2D space

Description

Displays a scatter plot of the active elements and the barycenter of supplementary elements, or of groups of supplementary elements, after a metric MDS analysis.

Usage

mmds.2D.multi(x,project, title = NULL, axis = c(1, 2), xlim = NULL,
ylim = NULL, outfile.type = NULL,bary="p",
outfile.name = "mmds",new.plot = TRUE, active.col = x$col[,3],
active.alpha = 0.6, active.pch = 20, sup.pch = NULL , active.cex = 2,
sup.cex = 2, active.legend.cex = 2, sup.legend.cex = 2,
active.legend.lwd = 1, sup.legend.lwd = 2, active.lwd = 1, sup.lwd = 3,
legend = TRUE, active.legend.pos = "bottomleft",
sup.legend.pos = "bottomright", group.name = NULL,
ensemble.legend.name = "", group.col = NULL, outfile.width = NULL, outfile.height = NULL,
box.lwd = 1, cex.axis = 1, sup.legend.text = 1,
active.legend.text = 1, legend.axis = TRUE, grid = TRUE, axes = TRUE)

Arguments

Details

If mmds.2D.plot is used after the col.group function, the elements are colored by the color scheme provided in the .csv file (see col.group for details). If the col.group function has not been used, the default colors are black and magenta for active and supplementary elements.

mmds.2D.plot helps identify patterns in data and compare active and supplementary elements.

active.alpha argument is helpful for visualization of supplementary elements because it allows the symbols of supplementary elements to be in the foreground as compared to active elements.

Value

Produces a scatter plot on the active graphical device.

Note

mmds.2D.multi requires alpha function from scales package.

Author(s)

Jean-Michel Becu

See Also

plot.PCA function from FactoMineR package.
png, pdf, postscript functions (default R package).

Examples

# scatter plot of human GPCRs onto the first two axes obtained
# from MDS analysis with projection of GPCRs from N.vectensis 
# and C.intestinalis as supplementary elements:
data(gpcr)

mmds_human <- gpcr$mmds$sapiens.active
project_vectensis <-gpcr$mmds$vectensis.project
project_intestinalis <-gpcr$mmds$intestinalis.project

mmds.2D.multi(mmds_human,project=list(project_vectensis,project_intestinalis),
bary='l',cex.axis=0.01,active.cex = 1, sup.cex = 1,active.lwd=1.5,sup.lwd=3,
ensemble.legend.name=c('nemve','inte','human'),legend=FALSE,title='multi_human')

# with selected group

mmds.2D.multi(mmds_human,project=list(project_vectensis,project_intestinalis),
bary='l',cex.axis=0.01,active.cex = 1, sup.cex = 1,active.lwd=1.5,sup.lwd=3,
ensemble.legend.name=c('nemve','inte','human'),legend=FALSE,title='multi_human',
group.name=c('SO','PEP','OPN','ADENO'),group.col=c("red","forestgreen","orange","maroon"))

Plots the mmds coordinates onto a 2D space

Description

Displays a scatter plot of the active elements and, if present, of supplementary elements, after a metric MDS analysis.

Usage

mmds.2D.plot(x,project = NULL, title = NULL, axis = c(1, 2), xlim = NULL,
ylim = NULL, outfile.type = NULL, outfile.name = "mmds",new.plot = TRUE,
active.col = x$col[,3], active.alpha = 1, sup.col = project$col[,3], 
active.pch = 20, sup.pch = 3, active.lab = FALSE, sup.lab = FALSE, 
active.cex = 2, sup.cex = 2, active.legend.cex = 2, sup.legend.cex = 2,
active.legend.lwd = 1, sup.legend.lwd = 2, active.lwd = 1, sup.lwd = 4,
legend = TRUE, active.legend.pos = "bottomleft",
sup.legend.pos = "bottomright", active.legend.name = x$group[,1],
sup.legend.name = project$group[,1], active.legend.col = x$group[,2],
sup.legend.col = project$group[,2], outfile.width = NULL, outfile.height = NULL,
box.lwd = 1, cex.axis = 1, cex.lab = 1, sup.legend.text = 1,
active.legend.text = 1, legend.axis = TRUE, grid = TRUE, axes = TRUE)

Arguments

Details

If mmds.2D.plot is used after the col.group function, the elements are colored by the color scheme provided in the .csv file (see col.group for details). If the col.group function has not been used, the default colors are black and magenta for active and supplementary elements.

mmds.2D.plot helps identify patterns in data and compare active and supplementary elements.

active.alpha argument is helpful for visualization of supplementary elements because it allows the symbols of supplementary elements to be in the foreground as compared to active elements.

Value

Produces a scatter plot on the active graphical device.

Note

mmds.2D.plot requires alpha function from scales package.

Author(s)

Julien Pele and Jean-Michel Becu

See Also

plot.PCA function from FactoMineR package.
png, pdf, postscript functions (default R package).

Examples

# scatter plot of human GPCRs onto the first two axes obtained from MDS analysis
# with projection of GPCRs from D. melanogaster as supplementary elements:
data(gpcr)
active <- gpcr$dif$sapiens.sapiens
mmds_active <- mmds(active,group.file=system.file(
"csv/human_gpcr_group.csv",package = "bios2mds"))
mmds.2D.plot(mmds_active, active.alpha = 0.5, active.lab = TRUE)

# with group information
sup <- gpcr$dif$melanogaster.sapiens
mmds_sup <- mmds.project(mmds_active, sup,group.file=system.file(
"csv/drome_gpcr_group.csv",package = "bios2mds"))
mmds.2D.plot(mmds_active,mmds_sup)

Display the mmds coordinates onto a 3D space

Description

Displays a 3D plot of the active elements and, if present, of supplementary elements, after a metric MDS analysis.

Usage

mmds.3D.plot(x, project = NULL, title = NULL, axis = c(1:3), 
active.type = "s", sup.type = "p", active.size = 2, radius = 0.005,
sup.size = 10, active.col = x$col[,3], sup.col = project$col[,3], 
box = TRUE, axes = TRUE, new.plot = TRUE, label = TRUE,
xlim = NULL, ylim = NULL, zlim = NULL, box.lwd = 2,
box.antialias = TRUE, ...)

Arguments

Details

If mmds.3D.plot is used after the col.group function, the elements are colored by the color scheme provided in the .csv file (see col.group for details). If the col.group function has not been used, the default colors are black and magenta for active and supplementary elements.

mmds.3D.plot helps identify patterns in data and compare active and supplementary elements.

Value

Produces a 3D plot on graphical device.

Note

mmds.3D.plot requires plot3D function from rgl package. See rgl documentation to supplementary function like snapshot3D to save image file of 3D device.

Author(s)

Jean-Michel Becu

Examples

# 3D plot of human GPCRs onto the first three axes obained from MDS analysis
# with projection of GPCRs from D. melanogaster as supplementary elements:
data(gpcr)
mmds.3D.plot(gpcr$mmds$sapiens.active,active.type="p",label=FALSE,lit=FALSE,
point_antialias=TRUE,box.lwd=3,sup.size=4.3,active.size=4.3)
bbox3d(shininess=0.5)

Plots a summary of the mmds results

Description

Displays one scree plot and three scatter plots of mmds results.

Usage

mmds.plot(x, project = NULL, new.plot = TRUE, pdf.file = NULL)

Arguments

Details

mmds.plot is a wrapper calling of both scree.plot and mmds.2D.plot. It produces a 2x2 plot with one scree plot of the relative eigenvalues, in the upper left, and three scatter plots. The three scatter plots are generated as follows:

• scatter plot of the elements on the first and second components in the upper right.

• scatter plot of the elements on the first and third components in the lower left.

• scatter plot of the elements on the second and third components in the lower right.

If object x contains supplementary elements, they are also projected onto the three scatter plots. The active and supplementary elements are represented by dots and crosses, respectively. The color.group function may be used before calling mmds.plot to color elements by user-provided groups.

Value

Produces a summary plot of the MDS analysis on the same active graphical device.

Note

The scatter plots can display supplementary objects if their coordinates are present in x input.

Author(s)

Julien Pele and Jean-Michel Becu

See Also

plot.pca function in bio3d package.

Examples

# summary plot of the MDS analysis of human GPCRs with projection of GPCRs
# from D. melanogaster as supplementary elements:
data(gpcr)
mmds.plot(gpcr$mmds$sapiens.active,gpcr$mmds$melanogaster.project)

Metric multidimensional Scaling Projection

Description

Performs metric MultiDimensional Scaling (MDS) analysis of active elements and projects supplementary elements onto the active space defined by active elements.

Usage

mmds.project(mmds, sup, pc = 3, group.file = NULL)

Arguments

Details

Metric multidimensional scaling is a statistical analysis technique aimed at analyzing a matrix of distances between 'active' elements. MDS maps the elements onto a low dimensional space (usually 2D or 3D). In this space, elements are represented by points whose respective distances best approximate the initial distance. In addition, after the metric MDS analysis of active elements, the mmds.project function allows projecting supplementary elements onto the active space in the context of the R environment. The active space is defined only by the MDS analysis of the active elements. The position of supplementary elements onto the active space depends only on their distances to active elements.

sup must have some characteristics:

• sup represents the matrix of pairwise distances between supplementary (rows) and active (columns) elements and does not have to be symmetric. The number of supplementary elements may be lower or higher than the number of active objects. The names of supplementary elements must be placed in the left column of sup.

The method for the computation of metric MDS projection of supplementary data is described by Abdi (2007). Briefly, if N is the number of active sequences and D is the N by N matrix of the squared distances computed from the active matrix, the mmds function performs the following steps:

(1)

Transforms D into a cross-product matrix S:

\bold{S} = -0.5 [\bold{I} - \bold{m}^{T}\bold{1}] \times \bold{D} \times [\bold{I} - \bold{m}^{T}\bold{1}]^{T}

where I is the N by N identity matrix, m is the N by 1 matrix mass, where each mass equal to \frac{1}{N} and 1 is an N by N matrix of ones.

(2)

Transforms S into a factor matrix F:

\bold{F} = \bold{M}^{\frac{-1}{2}}\bold{U\Lambda}^{\frac{1}{2}}

where M is the diag{m}, U is the eigenvector matrix and \bold{\Lambda} is the diagonal matrix of the eigenvalues, such as S = \bold{{U{\Lambda}U}^T}, where \bold{^T} denotes the transposition operation.

The eigenvectors of S, also called principal components (whose number is smaller or equal to N), form the active space. F gives the coordinates of the active elements in this space.

The supplementary elements are projected onto the active space as described below. If N_{sup} is the number of supplementary sequences and if D_{sup} is the N_{sup} by N matrix of the squared distances, the mmds.project function performs the following steps :

(3)

Transforms \bold{D_{sup}} into a cross-product matrix \bold{S_{sup}}:

\bold{S_{sup}} = -0.5[\bold{I} - \bold{m}^{T}\bold{1}] \times [\bold{{D_{sup}}^{T}} - \bold{D}\bold{m}\bold{1_{sup}^{T}}],

where \bold{1_{sup}} is an \bold{N_{sup}} by N matrix of ones.

(4)

Transforms \bold{S_{sup}} into a factor matrix \bold{F_{sup}}:

\bold{F_{sup}} = \bold{{S_{sup}}^T}\bold{F}\bold{\Lambda}^{-1}.

\bold{F_{sup}} gives the coordinates of the supplementary elements in the active space.

Value

A object of class 'project', which is a named list of three elements:

Note

If sup do not contain names:
A tag "S" followed by an incremented number names the rows of sup.
The columns of sup are named as the rows of mmds$D.

Author(s)

Julien Pele and Jean-Michel Becu

References

Abdi H (2007) Metric multidimensional scaling. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 598-605.

For further reading on projection of supplementary elements:

Gower JC (1968) adding a point to vector diagrams in multivariate analysis. Biometrika 55:582-585.

Trosset MW and Pribe CE (2008) The out-of-sample problem for classical multidimensional scaling. Computational statistics & Data analysis 52:4635-4642.

Pele J, Abdi H, Moreau M, Thybert D and Chabbert M (2011) Multidimensional scaling reveals the main evolutionary pathways of class A G-protein-coupled receptors. PloS ONE 6:e19094.

See Also

cmdscale function from stats package.
dudi.pco, suprow and supcol functions from ade4 package.
PCA function from FactoMineR package.

Examples

# performing metric MDS of human GPCRs with projection of
# GPCRs from D. melanogaster as supplementary elements:
data(gpcr)
active <- gpcr$dif$sapiens.sapiens
sup <- gpcr$dif$melanogaster.sapiens
mmds_active<-mmds(active)
mmds_sup <- mmds.project(mmds_active,sup)

Random Alignment

Description

Builds a multiple sequence alignment (MSA) of random sequences.

Usage

random.msa(nb.seq = 100, id = "SEQ", nb.pos = 100, gap = FALSE,
aa.strict = FALSE, align = NULL, align.replace = TRUE)

Arguments

Details

random.msa may be used to compare a reference MSA to a random MSA. The random MSA must have the same characteristics as the reference MSA (same number of sequences of same length).

A mmds procedure can be applied to the random MSA to assess the amount of variance due to random mutations in the reference MSA.

Value

A named list whose objects correspond to random sequences.

Note

The subset function is used for random selection of the amino acids. If a truly random procedure is needed, see random package.

Author(s)

Julien Pele

References

For an application of random MSA see :

Pele J, Abdi H, Moreau M, Thybert D and Chabbert M (2011) Multidimensional scaling reveals the main evolutionary pathways of class A G-protein-coupled receptors. PLoS ONE 6: e19094. doi:10.1371.

See Also

permutation and synsequence functions from seqinr package.

Examples

# generating a random sequence alignment with the same characterics
# as human GPCRs:
aln <- import.fasta(system.file("msa/human_gpcr.fa", package = "bios2mds"))
nb.seq <- length(aln)
nb.pos <- length(aln[[1]])
aln.random <- random.msa(nb.seq = nb.seq, nb.pos = nb.pos)

Plots the eingenvalues of an MDS analysis

Description

Displays a bar plot of the eigenvalues obtained by MDS

Usage

scree.plot(x, lab = FALSE, title = NULL, xlim = NULL,
    ylim = NULL, new.plot = TRUE, pdf.file = NULL)

Arguments

Details

A scree plot is a method for determining the optimal number of components useful to describe the data in the context of metric MultiDimensional Scaling (MDS). The scree plot is an histogram showing the eigenvalues of each component. The relative eigenvalues express the ratio of each eigenvalue to the sum of the eigenvalues. The relative eigenvalue of a component gives the proportion of the data variance explained by this component.

The aim is to evaluate the number of components required to capture most information contained in the data. In a scree plot, the relative eigenvalues decrease when the component number increases. The 'elbow' of the plot determines the optimal number of components to describe the data (usually the components before the 'elbow').

Value

Produces a bar plot on the active graphical device.

Note

The scree plot is not an exclusive method to determine the optimal number of components. A shepard plot, which is a scatterplot of the scaled MDS distances against the original distance data, can be another solution. See shepard function from MASS package.

Author(s)

Julien Pele

See Also

plot.pca.scree function from bio3d package.
goodness.metaMDS function from vegan package.

Examples

# displaying the scree plot of the MDS analysis of human GPCRs
data(gpcr)
active <- gpcr$dif$sapiens.sapiens
mmds1 <- mmds(active, pc = 5)
scree.plot(mmds1$eigen.perc, lab = TRUE, title = "Scree plot of metric MDS")

Silhouette score

Description

Computes silhouette scores for multiple runs of K-means clustering.

Usage

sil.score(mat, nb.clus = c(2:13), nb.run = 100, iter.max = 1000,
method = "euclidean")

Arguments

Details

Silhouettes are a general graphical aid for interpretation and validation of cluster analysis. This technique is available through the silhouette function (cluster package). In order to calculate silhouettes, two types of data are needed:

• the collection of all distances between objects. These distances are obtained from application of dist function on the coordinates of the elements in mat with argument method.

• the partition obtained by the application of a clustering technique. In sil.score context, the partition is obtained from the Kmeans function (amap package) with argument method which indicates the cluster to which each element is assigned.

For each element, a silhouette value is calculated and evaluates the degree of confidence in the assignment of the element:

• well-clustered elements have a score near 1,

• poorly-clustered elements have a score near -1.

Thus, silhouettes indicates the objects that are well or poorly clustered. To summarize the results, for each cluster, the silhouettes values can be displayed as an average silhouette width, which is the mean of silhouettes for all the elements assigned to this cluster. Finally, the overall average silhouette width is the mean of average silhouette widths of the different clusters.

Silhouette values offer the advantage that they depend only on the partition of the elements. As a consequence, silhouettes can be used to compare the output of the same clustering algorithm applied to the same data but for different numbers of clusters. A range of numbers of clusters can be tested, with the nb.clus argument. The optimal number of clusters is reached for the maximum of the overall silhouette width. This means that the clustering algorithm reaches a strong clustering structure. However, for a given number of clusters, the cluster assignment obtained by different K-means runs can be different because the K-means procedure assigns random initial centroids for each run. It may be necessary to run the K-means procedure several times, with the nb.run argument, to evaluate the uncertainty of the results. In that case, for each number of clusters, the mean of the overall average silhouettes for nb.run K-means runs is calculated. The maximum of this core gives the optimal number of clusters.

Value

A named numeric vector representing the silhouette scores for each number of clusters.

Note

sil.score requires Kmeans and silhouette functions from amap and cluster packages, respectively.

Author(s)

Julien Pele

References

Rousseeuw PJ (1987) Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, 20:53-65.

Lovmar L, Ahlford A, Jonsson M and Syvanen AC (2005) Silhouette scores for assessment of SNP genotype clusters. BMC Genomics, 6:35.

Guy B, Vasyl P, Susmita D and Somnath D (2008) clValid: An R Package for Cluster Validation. Journal of Statistical Software, 25.

See Also

connectivity and dunn functions from clValid package.
silhouette function from cluster package.

Examples

# calculating silhouette scores for K-means clustering of human GPCRs:
data(gpcr)
active <- gpcr$dif$sapiens.sapiens
mds <- mmds(active)
sil.score1 <- sil.score(mds$coord, nb.clus = c(2:10),
 nb.run = 100, iter.max = 100)
barplot(sil.score1)

Amino acid substitution matrices

Description

Contains eight amino acid substitution matrices, imported from version 9.1 of the aaindex2 database and PAM matrix calculator of Wageningen Bioinformatics Webportal.

Usage

data(sub.mat)

Format

A named list with eight elements corresponding to a 20 by 20 named matrix. Rows and columns names correspond to the twenty strict amino acids.

Details

PAM40

matrix was produced by "pam" Version 1.0.7

PAM120

matrix was produced by "pam" Version 1.0.7

PAM160

matrix was produced by "pam" Version 1.0.7

PAM250

log odds matrix for 250 PAMs (Dayhoff et al., 1978)

BLOSUM30

substitution matrix (Henikoff-Henikoff, 1992)

BLOSUM45

substitution matrix (Henikoff-Henikoff, 1992)

BLOSUM62

substitution matrix (Henikoff-Henikoff, 1992)

BLOSUM80

substitution matrix (Henikoff-Henikoff, 1992)

GONNET

substitution matrix (GONNET et al., 1992)

JTT

substitution matrix (Jones et al., 1992)

JTT_TM

transmembrane protein exchange matrix (Jones et al., 1994)

PHAT

substitution matrix built from hydrophobic and transmembrane regions of the Blocks database (Ng et al., 2000)

Source

The matrices were downloaded from the AAindex database at http://www.genome.jp/aaindex or were calculated on the PAM server at http://www.bioinformatics.nl/tools/pam.html.

References

Kawashima S and Kanehisa M (2000) AAindex: amino acid index database. Nucleic Acids Res 28:374.

Dayhoff MO, Schwartz R and Orcutt BC (1978) A model of Evolutionary Change in Proteins. Atlas of protein sequence and structure (volume 5, supplement 3 ed.). Nat. Biomed. Res. Found.. pp. 345-358.

Henikoff S and Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89:10915-9.

Jones DT, Taylor WR and Thornton JM (1992) The rapid generation of mutation data matrices from protein sequences. Comput Appl. Biosci 8:275-282.

Jones DT, Taylor WR and Thornton JM (1994) A mutation data matrix for transmembrane proteins. FEBS Lett 339:269-75.

Ng PC, Henikoff JG and Henikoff S (2000) PHAT: a transmembrane-specific substitution matrix. Predicted hydrophobic and transmembrane. Bioinformatics 16:760-6.

Gonnet GH, Cohen MA and Benner SA (1992) Exhaustive matching of the entire protein sequence database. Science 256:1443-1445.

Examples

# loading
data(sub.mat)

# displaying PAM40:
sub.mat$PAM40

Writes PDB file

Description

Writes MDS coordinates in the Protein Data Bank format for visualization with a molecular graphics viewer.

Usage

write.mmds.pdb(x,project = NULL, axis = c(1, 2, 3), file.pdb = "R.pdb", file.pml=NULL)

Arguments

Details

The elements can be visualized in three dimensions (3D) with a molecular viewer as Pymol or Rasmol. If x contains active and supplementary elements, the active and supplementary elements are numbered from 1 and from 5001, respectively. If group is not NULL, the assignment of an element to a group is indicated by the chain name from A for the first group to Z when the maximum number of groups, 26, is reached.

The pml file allows a fancy visualization of the PDB file with the Pymol molecular viewer. The user must first open the PDB file with Pymol, then run the pml file. The active and inactive elements will be displayed as spheres and crosses, respectively, with coloring based on the user-provided colors with the col.group function.

Value

Produces a PDB file from the MDS coordinates, with the elements numbered in the order of the MSA file and the groups corresponding to the chain numbers. Optionnaly, produces a pml file to add color and group selection in pymol with the pdb file.

Author(s)

Julien Pele and Jean-Michel Becu

References

http://www.wwpdb.org/docs.html

See Also

write.pdb function from bio3d package.

Examples

# writing the first three MDS coordinates of human GPCRs in a PDB file 
wd <- tempdir()
#wd <- getwd()
file1 <- file.path(wd,"sapiens.pdb")
file2 <- file.path(wd,"sapiens.pml")
data(gpcr)
write.mmds.pdb(gpcr$mmds$sapiens.active,file.pdb=file1,file.pml=file2)