filter.dccm {bio3d} | R Documentation

## Filter for Cross-correlation Matrices (Cij)

### Description

This function builds various cij matrix for correlation network analysis

### Usage

      filter.dccm(x, cutoff.cij = 0.4, cmap = NULL, xyz = NULL, fac = NULL,
          cutoff.sims = NULL, collapse = TRUE, extra.filter = NULL, ...)

### Arguments

`x` | A matrix (nXn), a numeric array with 3 dimensions (nXnXm), a list with m cells each containing nXn matrix, or a list with ‘all.dccm’ component, containing atomic correlation values, where "n" is the number of residues and "m" the number of calculations. The matrix elements should be in between -1 and 1. See ‘dccm’ function in bio3d package for further details.
`cutoff.cij` | Threshold for each individual correlation value. See below for details.
`cmap` | logical or numerical matrix indicating the contact map. If logical and TRUE, contact map will be calculated with input `xyz`.
`xyz` | XYZ coordinates for distance matrix calculation.
`fac` | factor indicating distinct categories of input correlation matrices.
`cutoff.sims` | Threshold for the number of simulations with observed correlation value above `cutoff.cij` for the same residue/atomic pairs. See below for details.
`collapse` | logical, if TRUE the mean matrix will be returned.
`extra.filter` | Filter to apply in addition to the model chosen.
`...` | extra arguments passed to function `cmap`.

### Details

If cmap is TRUE or provided a numerical matrix, the function inspects a set of cross-correlation matrices, or DCCM, and decides edges for correlation network analysis based on:

1. min(abs(cij)) >= cutoff.cij, or 2. max(abs(cij)) >= cutoff.cij && residues contact each other based on results from `cmap`.

Otherwise, the function filters DCCMs with `cutoff.cij` and return the mean of correlations present in at least `cutoff.sims` calculated matrices.

### Value

Returns a matrix of class "dccm" or a 3D array of filtered cross-correlations.

### Author(s)

Xin-Qiu Yao, Guido Scarabelli & Barry Grant

### References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.

### See Also

`cna`, `dccm`, `dccm.nma`, `dccm.xyz`, `cmap`, `plot.dccm`

### Examples

    ## Not run:

    # Example of transducin
    attach(transducin)

    gaps.pos <- gap.inspect(pdbs$xyz)
    modes <- nma.pdbs(pdbs, ncore=NULL)
    dccms <- dccm.enma(modes, ncore=NULL)

    cij <- filter.dccm(dccms, xyz=pdbs)

    # Example protein kinase
    # Select Protein Kinase PDB IDs
    ids <- c("4b7t_A", "2exm_A", "1opj_A", "4jaj_A", "1a9u_A",
                     "1tki_A", "1csn_A", "1lp4_A")

    # Download and split by chain ID
    files <- get.pdb(ids, path = "raw_pdbs", split=TRUE)

    # Alignment of structures
    pdbs <- pdbaln(files) # Sequence identity
    summary(c(seqidentity(pdbs)))

    # NMA on all structures
    modes <- nma.pdbs(pdbs, ncore=NULL)

    # Calculate correlation matrices for each structure
    cij <- dccm(modes)

    # Set DCCM plot panel names for combined figure
    dimnames(cij$all.dccm) = list(NULL, NULL, ids)
    plot.dccm(cij$all.dccm)

    # Filter to display only correlations present in all structures
    cij.all <- filter.dccm(cij, cutoff.sims = 8, cutoff.cij = 0)
    plot.dccm(cij.all, main = "Consensus Residue Cross Correlation")

    detach(transducin)

    ## End(Not run)

[Package bio3d version 2.3-4 Index]