Parallelized Hierarchical Spectral Clustering of TADs — SpectralTAD_Par • SpectralTAD

# Parallelized Hierarchical Spectral Clustering of TADs

Source: `R/SpectralTAD_Par.R`
`SpectralTAD_Par.Rd`

Parallelized Hierarchical Spectral Clustering of TADs

    SpectralTAD_Par(
      cont_list,
      chr,
      levels = 1,
      qual_filter = FALSE,
      z_clust = FALSE,
      eigenvalues = 2,
      min_size = 5,
      window_size = 25,
      resolution = "auto",
      grange = FALSE,
      gap_threshold = 1,
      cores = "auto",
      labels = NULL
    )

## Arguments

cont_list

  List of contact matrices where each is in either sparse 3 column, n x n or n x (n+3) form, where the first 3 columns are chromosome, start and end coordinates of the regions. If an x n matrix is used, the column names must correspond to the start point of the corresponding bin. Required.

chr

  Vector of chromosomes in the same order as their corresponding contact matrices. Must be same length as cont_list. Required.

levels

  The number of levels of the TAD hierarchy to be calculated. The default setting is 1.

qual_filter

  Option to turn on quality filtering which removes TADs with negative silhouette scores (poorly organized TADs). Default is FALSE.

z_clust

  Option to filter sub-TADs based on the z-score of their eigenvector gaps. Default is TRUE.

eigenvalues

  The number of eigenvectors to be calculated. The default and suggested setting is 2.

min_size

  The minimum allowable TAD size measured in bins. Default is 5.

window_size

  The size of the sliding window for calculating TADs. Smaller window sizes correspond to less noise from long-range contacts but limit the possible size of TADs

resolution

  The resolution of the contact matrix. If none selected, the resolution is estimated by taking the most common distance between bins. For n x (n+3) contact matrices, this value is automatically calculated from the first 3 columns.

grange

  Parameter to determine whether the result should be a GRangeList object. Defaults to FALSE

gap_threshold

  Corresponds to the percentage of zeros allowed before a column/row is removed from analysis. 1=100%, .7 = 70%, etc. Default is 1.

cores

  Number of cores to use. Defaults to total available cores minus one.

labels

  Vector of labels used to name each contact matrix. Must be same length as cont_list. Default is NULL.

## Value

List of lists where each entry is a list of data frames or GRanges in BED format corresponding to TADs seperated by hierarchies

## Details

This is the parallelized version of the SpectralTAD() function. Given a sparse 3 column, an n x n contact matrix, or n x (n+3) contact matrix, SpectralTAD returns a list of TAD coordinates in BED format. SpectralTAD works by using a sliding window that moves along the diagonal of the contact matrix. By default we use the biologically relevant maximum TAD size of 2Mb and minimum size of 5 bins to determine the size of this window. Within each window, we calculate a Laplacian matrix and determine the location of TAD boundaries based on gaps between eigenvectors calculated from this matrix. The number of TADs in a given window is calculated by finding the number that maximize the silhouette score. A hierarchy of TADs is created by iteratively applying the function to sub-TADs. The number of levels in each hierarchy is determined by the user.

## Examples

    #Read in data
    data("rao_chr20_25_rep")
    #Make a list of matrices
    mat_list = list(rao_chr20_25_rep, rao_chr20_25_rep)
    #Make a vector of chromosomes
    chr = c("chr20", "chr20")
    #Make a vector of labels
    labels = c("run1", "run2")
    spec_table <- SpectralTAD_Par(mat_list, chr= chr, labels = labels, cores = 2)
    #> Converting to n x n matrix
    #> Matrix dimensions: 2517x2517
    #> Estimating resolution
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Converting to n x n matrix
    #> Matrix dimensions: 2517x2517
    #> Estimating resolution
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.
    #> Warning: Unknown or uninitialised column: `Group`.