normalize: Perform MA Normalization on a Set of ChIP-seq Samples
In MAnorm2: Tools for Normalizing and Comparing ChIP-seq Samples

normalize

R Documentation

Perform MA Normalization on a Set of ChIP-seq Samples

Description

Given read counts from a set of ChIP-seq samples in a set of genomic intervals as well as the signal enrichment states of these intervals in each of the samples, this function converts the read counts into signal intensities more of a continuous variable, and normalizes these signal intensities through linear transformations so that the normalized signal intensities in each genomic interval are comparable across samples.

Usage

normalize(
  x,
  count,
  occupancy,
  baseline = NULL,
  subset = NULL,
  interval.size = FALSE,
  offset = 0.5,
  convert = NULL,
  common.peak.regions = NULL
)

Arguments

`x`	A data frame containing the read count and occupancy indicator variables. Each row should represent a genomic interval. Objects of other types are coerced to a data frame.
`count`	Either an integer vector or a character vector that indexes the read count variables in `x` to be normalized. Each of these variables represents a ChIP-seq sample. Elements of `count` must be unique.
`occupancy`	Either an integer or character vector indexing occupancy indicator variables in `x`. Must correspond to `count` one by one with the same order. These variables are interpreted as logical, where `TRUE` indicates being occupied by peaks (i.e., showing an enrichment for reads) of the corresponding ChIP-seq sample.
`baseline`	Either an integer scalar or a character scalar referring to the baseline sample. Must be an element of `count` if specified. By default, the baseline is automatically selected by the function (see "Details"). A special option for this argument is `"pseudo-reference"`, in which case the function constructs a pseudo ChIP-seq sample as baseline by "averaging" the samples to be normalized (see "Details"). This option is especially recommended when the number of samples to be normalized is large (e.g., >5).
`subset`	An optional vector specifying the subset of intervals to be used for estimating size factors and selecting the baseline (see "Details" and `estimateSizeFactors`). Defaults to the intervals occupied by all the samples. Ignored if `baseline` is specified.
`interval.size`	A numeric vector of interval sizes or a logical scalar to specify whether to use interval sizes for converting read counts into signal intensities (see "Details"). If set to `TRUE`, the function will look for the `"start"` and `"end"` variables in `x`, and use them to calculate interval sizes. By default, interval sizes are not used.
`offset`	The offset value used for converting read counts into signal intensities (see "Details").
`convert`	An optional function specifying the way that read counts are converted into signal intensities. It should accept a vector of read counts and return the corresponding signal intensities. If set, `interval.size` and `offset` are ignored.
`common.peak.regions`	An optional logical vector specifying the intervals that could possibly be common peak regions for each pair of samples. By default, for each pair of samples, all the intervals occupied by both samples are considered as their common peak regions. See "Details" for an application of this argument.

Details

The function first determines a baseline ChIP-seq sample from the given set. The baseline could either be specified by the user or automatically selected by the function. In the latter case, the function deduces the size factor of each sample using estimateSizeFactors, and selects the sample as baseline whose log2 size factor is closest to 0 (with the exception that, if there are only two samples to be normalized, the function will always use the sample with the smaller size factor as baseline, for avoiding potential uncertainty in selection results due to limited numerical precision). A special case is setting the baseline argument to "pseudo-reference", in which case the function constructs a pseudo ChIP-seq sample as baseline. Technically, for an individual genomic interval in the pseudo sample, the function derives its signal intensity (rather than read count; see below) by taking the average across those samples that occupy it, and it is considered to be a peak region as long as it is occupied by any of the samples to be normalized. We don't need to care about the signal intensities of those intervals that are not occupied by any sample, since they are never used in the normalization process (see below). Using such a pseudo sample as baseline is especially recommended when the number of samples to be normalized is large, for avoiding computation artifacts resulting from an arbitrary selection of baseline sample.

Then, the function converts each read count into a signal intensity through the equation log2(count + offset), or log2(count / intervalSize + offset) if sizes of the genomic intervals are provided. To be noted, while the interval sizes (either specified by users or calculated from the data frame) are considered as number of base pairs, the intervalSize variable used in the latter equation has a unit of kilo base pairs (so that 0.5 still serves as a generally appropriate offset).

In most cases, simply using the former equation is recommended. You may, however, want to involve the interval sizes when ChIP-seq samples to be classified into the same biological condition are associated with a large variation (e.g., when they are from different individuals; see also bioCond). Besides, the goodness of fit of mean-variance curve (see also fitMeanVarCurve) could serve as one of the principles for selecting an appropriate converting equation.

The convert argument serves as an optional function for converting read counts into signal intensities. The function is expected to operate on the read count vector of each sample, and should return the converted signal intensities. convert is barely used, exceptions including applying a variance stabilizing transformation or shrinking potential outlier counts.

Finally, the function normalizes each ChIP-seq sample to the baseline. Basically, it applies a linear transformation to the signal intensities of each non-baseline sample, so that M and A values calculated from common peak regions (the genomic intervals occupied by both the sample to be normalized and the baseline) are not correlated. The argument common.peak.regions can be used to narrow down the set of intervals that could possibly be considered as common peak regions. You may, for example, use it to remove the intervals located on sex chromosomes from common peak regions when the involved ChIP-seq samples come from different genders (see also "Examples" below).

Value

normalize returns the provided data frame, with the read counts replaced by the corresponding normalized signal intensities. Besides, the following attributes are added to the data frame:

size.factor: Size factors of the specified read count variables. Only present when baseline is not explicitly specified by the user.
baseline: Name of the read count variable used as the baseline sample or "pseudo-reference" if the baseline argument is specified so.
norm.coef: A data frame recording the linear transformation coefficients of each sample as well as the number of common peak regions between each sample and the baseline.
MA.cor: A real matrix recording the Pearson correlation coefficient between M & A values calculated from common peak regions of each pair of samples. The upper and lower triangles of this matrix are deduced from raw and normalized signal intensities, respectively. Note that M values are always calculated as the column sample minus the row sample.

References

Tu, S., et al., MAnorm2 for quantitatively comparing groups of ChIP-seq samples. Genome Res, 2021. 31(1): p. 131-145.

Examples

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

## Perform MA normalization on the whole set of ChIP-seq samples once for
## all.

# Exclude the genomic intervals in sex chromosomes from the common peak
# regions, since the ChIP-seq samples to be normalized are associated with
# different genders.
autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY"))
norm <- normalize(H3K27Ac, 4:8, 9:13, common.peak.regions = autosome)

# Inspect the normalization effects.
attributes(norm)[5:8]
plot(attr(norm, "MA.cor"), symbreaks = TRUE, margins = c(8, 8))
MAplot(norm[[4]], norm[[5]], norm[[9]], norm[[10]],
       main = "GM12890_rep1 vs. GM12891_rep1")
abline(h = 0, lwd = 2, lty = 5)

## Alternatively, apply MA normalization first within each cell line, and
## then normalize across cell lines. In practice, this strategy is more
## recommended than the aforementioned one.

# Normalize samples separately for each cell line.
norm <- normalize(H3K27Ac, 4, 9)
norm <- normalize(norm, 5:6, 10:11)
norm <- normalize(norm, 7:8, 12:13)

# Construct separately a bioCond object for each cell line, and perform MA
# normalization on the resulting bioConds. Genomic intervals in sex
# chromosomes are not allowed to be common peak regions, since the cell
# lines are from different genders.
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"))
autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY"))
conds <- normBioCond(conds, common.peak.regions = autosome)

# Inspect the normalization effects.
attributes(conds)
plot(attr(conds, "MA.cor"), symbreaks = TRUE, margins = c(8, 8))
MAplot(conds[[1]], conds[[2]], main = "GM12890 vs. GM12891")
abline(h = 0, lwd = 2, lty = 5)

MAnorm2 documentation built on Oct. 29, 2022, 1:12 a.m.