MAnorm2 (version 1.2.2)

normBioCondBySizeFactors: Normalize bioCond Objects by Their Size Factors

Description

Given a list of bioCond objects, normBioCondBySizeFactors normalizes the signal intensities stored in them based on their respective size factors, so that these bioConds become comparable to each other. Note that the normalization method implemented in this function is most suited to the bioConds comprised of RNA-seq samples. See normBioCond for a more robust method for normalizing the bioConds consisting of ChIP-seq samples.

Usage

normBioCondBySizeFactors(conds, subset = NULL)

Value

A list of bioCond objects with normalized signal intensities, corresponding to the argument conds. To be noted, information about the mean-variance dependence stored in the original

bioCond objects, if any, will be removed from the returned

bioConds. You can re-fit a mean-variance curve for them by, for example, calling fitMeanVarCurve. Note also that the original structure matrices are retained for each bioCond in the returned list (see setWeight for a detailed description of structure matrix).

Besides, an attribute named "size.factor" is added to the returned list, recording the size factor of each bioCond object.

Arguments

conds

A list of bioCond objects to be normalized.

subset

An optional vector specifying the subset of intervals or genes to be used for estimating size factors. Defaults to the intervals/genes occupied by all the bioCond objects. See normalize and bioCond for more information about occupancy states of intervals/genes in a biological condition.

Details

Technically, normBioCondBySizeFactors considers each bioCond object to be a single ChIP-seq/RNA-seq sample. It treats the sample.mean variable of each bioCond as in the scale of log2 read count, and applies the median ratio strategy to estimate their respective size factors (see "References"). Finally, each bioCond object is normalized by subtracting its log2 size factor from each of its samples.

The idea of normBioCondBySizeFactors comes from the principle that the more similar a set of samples are to each other, the fewer biases are expected to introduce when normalizing them. With this function, instead of performing an overall normalization on all the samples involved, you may choose to first normalize the samples within each biological condition, and then perform a normalization between the resulting bioCond objects (see "Examples" below).

References

Anders, S. and W. Huber, Differential expression analysis for sequence count data. Genome Biol, 2010. 11(10): p. R106.

See Also

normalizeBySizeFactors for normalizing ChIP-seq/RNA-seq samples based on their size factors; bioCond for creating a bioCond object; normBioCond for performing an MA normalization on bioCond objects; cmbBioCond for combining a set of bioCond objects into a single one; MAplot.bioCond for creating an MA plot on two normalized bioCond objects; fitMeanVarCurve for modeling the mean-variance dependence across intervals in bioCond objects.

Examples

data(H3K27Ac, package = "MAnorm2")
attr(H3K27Ac, "metaInfo")

## First perform a normalization within each cell line, and then normalize
## across cell lines.
# \donttest{
# Normalize samples separately for each cell line.
norm <- normalizeBySizeFactors(H3K27Ac, 4)
norm <- normalizeBySizeFactors(norm, 5:6,
                               subset = apply(norm[10:11], 1, all))
norm <- normalizeBySizeFactors(norm, 7:8,
                               subset = apply(norm[12:13], 1, all))

# Construct separately a bioCond object for each cell line, and normalize
# the resulting bioConds by their size factors.
conds <- list(GM12890 = bioCond(norm[4], norm[9], name = "GM12890"),
              GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"),
              GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892"))
conds <- normBioCondBySizeFactors(conds)

# Inspect the normalization effects.
attr(conds, "size.factor")
MAplot(conds[[1]], conds[[2]], main = "GM12890 vs. GM12891")
abline(h = 0, lwd = 2, lty = 5)
# }