Function to calculate distance penalty parameter (
for random genomic windows. Used to choose
distance_parameter to pass
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A cicero CDS object generated using
Size of the genomic window to query, in base pairs.
Maximum number of iterations for distance_parameter estimation.
Power law value. See details for more information.
Number of random windows to calculate
Maximum distance of expected connections. Must be
Convergence step size for
Maximum number of elements per window allowed. Prevents very large models from slowing performance.
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
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
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.
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
not calculate sample_num distance_parameters - see documentation details"
Generally, this means your
window parameter needs to be larger or
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.
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
Further details are available in the publication that accompanies this
citation("cicero") for publication details.
A list of results of length
sample_num. List members are
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.
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data("cicero_data") data("human.hg19.genome") sample_genome <- subset(human.hg19.genome, V1 == "chr18") sample_genome$V2 <- 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)
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