estimate_distance_parameter {cicero} | R Documentation

## Calculate distance penalty parameter

### Description

Function to calculate distance penalty parameter (`distance_parameter`) for random genomic windows. Used to choose `distance_parameter` to pass to `generate_cicero_models`.

### Usage

    estimate_distance_parameter(cds, window = 5e+05, maxit = 100,
      s = 0.75, sample_num = 100, distance_constraint = 250000,
      distance_parameter_convergence = 1e-22, max_elements = 200,
      genomic_coords = cicero::human.hg19.genome)

### Arguments

`cds` | A cicero CDS object generated using `make_cicero_cds`.
`window` | Size of the genomic window to query, in base pairs.
`maxit` | Maximum number of iterations for distance_parameter estimation.
`s` | Power law value. See details for more information.
`sample_num` | Number of random windows to calculate `distance_parameter` for.
`distance_constraint` | Maximum distance of expected connections. Must be smaller than `window`.
`distance_parameter_convergence` | Convergence step size for `distance_parameter` calculation.
`max_elements` | Maximum number of elements per window allowed. Prevents very large models from slowing performance.
`genomic_coords` | Either a data frame or a path (character) to a file with chromosome lengths. The file should have two columns, the first is the chromosome name (ex. "chr1") and the second is the chromosome length in base pairs. See `data(human.hg19.genome)` for an example. If a file, should be tab-separated and without header.

### Details

The purpose of this function is to calculate the distance scaling parameter used to adjust the distance-based penalty function used in Cicero's model calculation. The scaling parameter, in combination with the power law value `s` determines the distance-based penalty.

This function chooses random windows of the genome and calculates a `distance_parameter`. The function returns a vector of values calculated on these random windows. We recommend using the mean value of this vector moving forward with Cicero analysis.

The function works by finding the minimum distance scaling parameter such that no more than 5 `distance_constraint` have non-zero entries after graphical lasso regularization and such that fewer than 80 nonzero.

If the chosen random window has fewer than 2 or greater than `max_elements` sites, the window is skipped. In addition, the random window will be skipped if there are insufficient long-range comparisons (see below) to be made. The `max_elements` parameter exist to prevent very dense windows from slowing the calculation. If you expect that your data may regularly have this many sites in a window, you will need to raise this parameter.

Calculating the `distance_parameter` in a sample window requires peaks in that window that are at a distance greater than the `distance_constraint` parameter. If there are not enough examples at high distance, the function will return the warning `"Warning: could not calculate sample_num distance_parameters - see documentation details"` Generally, this means your `window` parameter needs to be larger or your `distance_constraint` parameter needs to be smaller. A less likely possibility is that your `max_elements` parameter needs to be larger. This would occur if your data is particularly dense.

The parameter `s` is a constant that captures the power-law distribution of contact frequencies between different locations in the genome as a function of their linear distance. For a complete discussion of the various polymer models of DNA packed into the nucleus and of justifiable values for s, we refer readers to (Dekker et al., 2013) for a discussion of justifiable values for s. We use a value of 0.75 by default in Cicero, which corresponds to the “tension globule” polymer model of DNA (Sanborn et al., 2015). This parameter must be the same as the s parameter for generate_cicero_models.

Further details are available in the publication that accompanies this package. Run `citation("cicero")` for publication details.

### Value

A list of results of length `sample_num`. List members are numeric `distance_parameter` values.

### References

- Dekker, J., Marti-Renom, M.A., and Mirny, L.A. (2013). Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390–403.

- Sanborn, A.L., Rao, S.S.P., Huang, S.-C., Durand, N.C., Huntley, M.H., Jewett, A.I., Bochkov, I.D., Chinnappan, D., Cutkosky, A., Li, J., et al. (2015). Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl. Acad. Sci. U. S. A. 112, E6456–E6465.

### See Also

`generate_cicero_models`

### Examples

      data("cicero_data")
      data("human.hg19.genome")
      sample_genome <- subset(human.hg19.genome, V1 == "chr18")
      sample_genome$V2[1] <- 100000
      input_cds <- make_atac_cds(cicero_data, binarize = TRUE)
      input_cds <- reduceDimension(input_cds, max_components = 2, num_dim=6,
                                   reduction_method = 'tSNE',
                                   norm_method = "none")
      tsne_coords <- t(reducedDimA(input_cds))
      row.names(tsne_coords) <- row.names(pData(input_cds))
      cicero_cds <- make_cicero_cds(input_cds, reduced_coordinates = tsne_coords)
      distance_parameters <- estimate_distance_parameter(cicero_cds,
                                                         sample_num=5,
                                                         genomic_coords = sample_genome)

[Package cicero version 1.2.0 Index]