HDMD: Statistical Analysis Tools for High Dimension Molecular Data (HDMD)

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High Dimensional Molecular Data (HDMD) typically have many more variables or dimensions than observations or replicates (D>>N). This can cause many statistical procedures to fail, become intractable, or produce misleading results. This package provides several tools to reduce dimensionality and analyze biological data for meaningful interpretation of results. Factor Analysis (FA), Principal Components Analysis (PCA) and Discriminant Analysis (DA) are frequently used multivariate techniques. However, PCA methods prcomp and princomp do not reflect the proportion of total variation of each principal component. Loadings.variation displays the relative and cumulative contribution of variation for each component by accounting for all variability in data. When D>>N, the maximum likelihood method cannot be applied in FA and the the principal axes method must be used instead, as in factor.pa of the psych package. The factor.pa.ginv function in this package further allows for a singular covariance matrix by applying a general inverse method to estimate factor scores. Moreover, factor.pa.ginv removes and warns of any variables that are constant, which would otherwise create an invalid covariance matrix. Promax.only further allows users to define rotation parameters during factor estimation. Similar to the Euclidean distance, the Mahalanobis distance estimates the relationship among groups. pairwise.mahalanobis computes all such pairwise Mahalanobis distances among groups and is useful for quantifying the separation of groups in DA. Genetic sequences are composed of discrete alphabetic characters, which makes estimates of variability difficult. MolecularEntropy and MolecularMI calculate the entropy and mutual information to estimate variability and covariability, respectively, of DNA or Amino Acid sequences. Functional grouping of amino acids (Atchley et al 1999) is also available for entropy and mutual information estimation. Mutual information values can be normalized by NMI to account for the background distribution arising from the stochastic pairing of independent, random sites. Alternatively, discrete alphabetic sequences can be transformed into biologically informative metrics to be used in various multivariate procedures. FactorTransform converts amino acid sequences using the amino acid indices determined by Atchley et al 2005.

Author
Lisa McFerrin
Date of publication
2013-02-27 07:31:03
Maintainer
Lisa McFerrin <lgmcferr@ncsu.edu>
License
GPL (>= 2)
Version
1.2

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Man pages

AA54
Normalized Amino Acid Indices quantifying 54 various...
AAMetric
Amino Acid Metric Solution using R (Atchley et al 2005)
AAMetric.Atchley
Amino Acid Metric Solution (Atchley et al 2005)
AminoAcids
Amino Acid Properties
bHLH288
Alignment of basic Helix Loop Helix (bHLH) domain data
factor.pa.ginv
Principal Axis Factor Analysis when D>>N
FactorTransform
Metric Solution for Amino Acid characters
HDMD-package
Structural Analysis Tools for High Dimensional Molecular Data
Loadings.variation
Proportional and Cumulative Variation of Loading Components
MolecularEntropy
Molecular Entropy for DNA or Amino Acid Sequences
MolecularMI
Molecular Mutual Information
NMI
Normalized Mutual Information
pairwise.mahalanobis
Mahalanobis distances for grouped data
Promax.only
Promax rotation (without prior Varimax rotation)

Files in this package

HDMD
HDMD/MD5
HDMD/R
HDMD/R/HDMD_package.R
HDMD/R/bHLH288.R
HDMD/R/AA54.R
HDMD/NAMESPACE
HDMD/man
HDMD/man/Promax.only.Rd
HDMD/man/pairwise.mahalanobis.Rd
HDMD/man/NMI.Rd
HDMD/man/MolecularMI.Rd
HDMD/man/MolecularEntropy.Rd
HDMD/man/Loadings.variation.Rd
HDMD/man/HDMD-package.Rd
HDMD/man/FactorTransform.Rd
HDMD/man/factor.pa.ginv.Rd
HDMD/man/bHLH288.Rd
HDMD/man/AminoAcids.Rd
HDMD/man/AAMetric.Rd
HDMD/man/AAMetric.Atchley.Rd
HDMD/man/AA54.Rd
HDMD/DESCRIPTION