ACC: Auto Cross Covariance (ACC) for Generating Scales-Based...

In nanxstats/protr: Generating Various Numerical Representation Schemes for Protein Sequences

Auto Cross Covariance (ACC) for Generating Scales-Based Descriptors of the Same Length

Description

This function calculates the auto covariance and auto cross covariance for generating scale-based descriptors of the same length.

Usage

acc(mat, lag)

Arguments

mat	A p * n matrix. Each row represents one scale (total p scales), each column represents one amino acid position (total n amino acids).
lag	The lag parameter. Must be less than the amino acids.

Value

A length lag * p^2 named vector, the element names are constructed by: the scales index (crossed scales index) and lag index.

Note

Please see the references for details about auto cross covariance.

Author(s)

Nan Xiao <https://nanx.me>

References

Wold, S., Jonsson, J., Sjorstrom, M., Sandberg, M., & Rannar, S. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica chimica acta, 277(2), 239–253.

Sjostrom, M., Rannar, S., & Wieslander, A. (1995). Polypeptide sequence property relationships in Escherichia coli based on auto cross covariances. Chemometrics and intelligent laboratory systems, 29(2), 295–305.

See Also

See extractScales for scales-based descriptors. For more details, see extractDescScales and extractProtFP.

Examples

p <- 8 # p is the scales number
n <- 200 # n is the amino acid number
lag <- 7 # the lag paramter
mat <- matrix(rnorm(p * n), nrow = p, ncol = n)
acc(mat, lag)

nanxstats/protr documentation built on April 24, 2024, 7:32 a.m.