Description Usage Arguments Details Value Author(s) References See Also Examples
For a vector of principal components the Q-residuals of a DNA symbolical sequence are calculated using a PCA model of the numerical DNA motif. First sequences are converted to numerical DNA sequences and then the model is applied. A matrix is returned for each number of PC with the value of the Q-residuals in each position and a label indicating if the sequence belong to a binding site or not
1 |
iicc |
Options described in the MEET function |
TF |
DNA sequences used to construct the motif model |
Alignment method has to be alled in your computer.
As a list, for each nPCs
matriuROC[[nPCs]] |
matrix with two colums, in the first one the Q-residuals for each studied sequence, and the second one indicates if the sequence belong to a TFBS |
Erola Pairo <epairo@ibecbarcelona.eu>
Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4 Stacklies, Wolfram, Redestig, Henning, Scholz, Matthias, Walther, Dirk, and Selbig, Joachim: pcaMethods a bioconductor package providing PCA methods for incomplete data, Bioinformatics 23(9) , 1164 2007
MEET, kfold.Entropy, kfold.MATCH, kfold.Divergence, PCanalysis, llegir_DNA, convertDNA, numericalDNA.
1 2 3 4 5 6 7 8 9 10 11 | data(iicc)
data(TranscriptionFactor)
iicc$method<-"PCA"
#Define the numner of principal components
iicc$vector<-c(1,3,5)
library(seqinr)
#writing sequences to model in fasta format
write.fasta <- get("write.fasta",pos="package:seqinr")
write.fasta(sequences=TranscriptionFactor,names=c(1:length(TranscriptionFactor)),file.out="Sq.fa",open="w")
kfold.PCA(iicc, TF="Sq.fa")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.