Description Usage Arguments Details Value Examples
Detect differences between two jointly normalized HiC datasets. OLD METHOD; USE hic_compare() instead
1 2 3 
hic.table 
A hic.table or list of hic.tables output from the

diff.thresh 
Fold change threshold desired to call a detected difference significant. Set to 'auto' by default to indicate that the difference threshold will be automatically calculated as 2 standard deviations of all the adjusted M values. For no pvalue adjustment set diff.thresh = NA. To set your own threshold enter a numeric value i.e. diff.thresh = 1. If set to 'auto' or a numeric value, a check will be made as follows: if permutation pvalue < 0.05 AND M < diff.thresh (the log2 fold change for the difference between IF1 and IF2) then the pvalue will be set to 0.5. 
iterations 
Number of iterations for the permuation test. 
Plot 
Logical, should the MD plot showing before/after loess normalization be output? 
Plot.smooth 
Logical, defaults to TRUE indicating the MD plot will be a smooth scatter plot. Set to FALSE for a scatter plot with discrete points. 
parallel 
Logical, set to TRUE to utilize the 
BP_param 
Parameters for BiocParallel. Defaults to bpparam(), see help for BiocParallel for more information http://bioconductor.org/packages/release/bioc/vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf 
This is the old method for detecting difference. The function is left in for
legacy reasons and
it is recommended to use the new function, hic_compare(), instead.
The function takes in a hic.table or a list of hic.table objects created
with the hic_loess
function. If you wish to perform difference
detection on HiC data for multiple chromosomes use a list of hic.tables. The process
can be parallelized using the parallel
setting. The adjusted IF and adjusted M calculated from hic_loess
are used for
difference detection. A permutation test is performed to test
the significance of the difference between each IF of the two datasets. Permutations
are broken in blocks for each unit distance. See methods section
of Stansfield & Dozmorov 2017 for more details.
A hic.table with additional columns containing a pvalue for the significance of the difference and the raw fold change between the IFs of the two datasets.
1 2 3 4 5 6 7 8 9  # Create hic.table object using included HiC data in sparse upper triangular
# matrix format
data('HMEC.chr22')
data('NHEK.chr22')
hic.table < create.hic.table(HMEC.chr22, NHEK.chr22, chr = 'chr22')
# Plug hic.table into hic_loess()
result < hic_loess(hic.table, Plot = TRUE)
# perform difference detection
diff.result < hic_diff(result, diff.thresh = 'auto', Plot = TRUE)

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