View source: R/calculateAutocor.R
calculateAutocor | R Documentation |
This function calculates autocorrelation descriptors:
moran
- moran autocorrelation,
(Dim: length(target.props) * nlag
).
geary
- geary autocorrelation,
(Dim: length(target.props) * nlag
).
moreaubroto
- moreau-broto autocorrelation,
(Dim: length(target.props) * nlag
).
calculateAutocor(
x,
target.props = c("CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102",
"CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"),
nlag = 30L,
type = c("moran", "geary", "moreaubroto")
)
x |
A data.frame containing gene/protein names and their fasta sequences. |
target.props |
A character vector, specifying the accession number of the target properties. 8 properties are used by default, as listed below:
|
nlag |
Maximum value of the lag parameter. Default is |
type |
The autocorrelation type:
|
calculateAutocor
A length nlag
named vector for data input.
Matineh Rahmatbakhsh <matinerb.94@gmail.com>, Nan Xiao
AAindex: Amino acid index database. http://www.genome.ad.jp/dbget/aaindex.html
Feng, Z.P. and Zhang, C.T. (2000) Prediction of membrane protein types based on the hydrophobic index of amino acids. Journal of Protein Chemistry, 19, 269-275.
Horne, D.S. (1988) Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers, 27, 451-477.
Sokal, R.R. and Thomson, B.A. (2006) Population structure inferred by local spatial autocorrelation: an Usage from an Amerindian tribal population. American Journal of Physical Anthropology, 129, 121-131.
data(UP000464024_df)
x_df <- calculateAutocor(UP000464024_df,type = 'moran')
head(x_df, n = 2L)
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