bnbc {bnbc} | R Documentation

## Normalize Contact Matrices with BNBC

### Description

Applies BNBC method to normalize contact matrices.

### Usage

    bnbc(cg, batch, threshold = NULL, step = NULL, qn = TRUE, nbands
    = NULL, mod = NULL, mean.only = FALSE, tol = 5, bstart = 2, verbose = TRUE)

### Arguments

`cg` | A ContactGroup object.
`batch` | A single batch indicator variable.
`threshold` | The maximum distance interacting loci are allowed to be separated by.
`step` | The step size, or the number of bases a contact matrix cell represents.
`qn` | Whether to apply quantile normalization on each band matrix. Defaults to TRUE.
`bstart` | The first band to normalize. Defaults to 2.
`nbands` | The last band to normalize. Defaults to `nrow(cg) - 1`.
`mod` | A model matrix specifying which sample information is to be preserved by ComBat. Optional.
`mean.only` | Whether ComBat should not correct for batch effect in the variances of band matrix rows. Defaults to FALSE, which means variances are corrected. Set to TRUE if there is only one observation per batch.
`tol` | The number of significant digits for which the mean value of a band matrix must be greater than 0 to be processed by ComBat.
`verbose` | Should the function print progress?

### Details

Normalization and batch correction is performed in a band-wise manner, correcting all samples' observations of one matrix off-diagonal (which we refer to as a matrix "band") at a time. For each matrix band, we collect all samples' observations into a single matrix. We then apply quantile normalization to ensure distributional similarity across samples. Finally, we perform batch effect correction using ComBat on this matrix. Each samples' matrix band is then replaced with its corrected version. We refer to this process of Band-Wise Normalization and Batch Correction as BNBC.

This function applies BNBC to the set of contact matrices and returns a ContactGroup object with matrix bands `bstart:nbands` corrected. For those rows in the matrix bands which cannot be corrected we set all elements to 0.

Very high bands contain little data in Hi-C experiments, and we don't recommend to analyze those or apply this function to high bands, see the `nbands` argument to the function.

We recommend performing bnbc on contact matrices which have been converted to log-CPM and smoothed, see the example.

### Value

A ContactGroup object for which matrix bands `bstart:nbands` have had BNBC applied.

### References

Johnson, W.E., Li, C. and Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8:118-127. doi: 10.1093/biostatistics/kxj037

Fletez-Brant et al. Distance-dependent between-sample normalization for Hi-C experiments. In preparation.

### See Also

ContactGroup, logCPM, boxSmoother, band

### Examples

    data(cgEx)
    batches <- colData(cgEx)$Batch
    cgEx.cpm <- logCPM(cgEx)
    cgEx.smooth <- boxSmoother(cgEx, 5, mc.cores=1)
    cgEx.bnbc <- bnbc(cgEx.smooth, batches, 1e7, 4e4, bstart=2, nbands=4)

[Package bnbc version 1.6.0 Index]