getNormFactors : estimate normalization factors from genomic signal files
getNormFactors.RdEstimates normalization factors for a set of samples (i.e. bam/bigwig files).
Usage
getNormFactors(
x,
method = c("background", "SES", "enriched", "top", "MAnorm", "S3norm", "2cLinear"),
wsize = 10L,
nwind = 20000L,
peaks = NULL,
trim = 0.02,
useSeqLevels = NULL,
paired = NULL,
...,
verbose = TRUE
)
bwNormFactors(x, ...)Arguments
- x
A vector of paths to bigwig files, to bam files, or alternatively a list of coverages in RleList format. (Mixed formats are not supported)
- method
Normalization method (see details below).
- wsize
The size of the random windows. If any of the bigwig files records point events (e.g. insertions) at high resolution (e.g. nucleotide), use a lot (e.g. >10k) of small windows (e.g. `wsize=10`), as per default settings. Otherwise the process can be lightened by using fewer bigger windows.
- nwind
The number of random windows
- peaks
A list of peaks (GRanges) for each experiment in `x`, or a vector of paths to such files, or a single GRanges of unified peaks to use (e.g. for top/MAnorm).
- trim
Amount of trimming when calculating means.
Value
A vector of normalization factors, or for the 'S3norm' and '2cLinear' methods, a numeric matrix with a number of rows equal to the length of `x`, and two columns indicating the alpha and beta terms.
A vector of normalization factors or, for methods 'S3norm' and '2cLinear', a matrix of per-sample normalization parameters.
Details
The function computes per-sample (bigwig or bam file) normalization factors using one of the following methods: * The 'background' or 'SES' normalization method (they are synonyms here) (Diaz et al., Stat Appl Gen et Mol Biol, 2012) assumes that the background noise should on average be the same across experiments, an assumption that works well in practice when there are not very large differences in signal-to-noise ratio. The implementation uses the trimmed mean number of reads in random windows with non-zero counts. * The 'MAnorm' approach (Shao et al., Genome Biology 2012) assumes that regions that are commonly enriched (i.e. common peaks) in two experiments should on average have the same signal in the two experiments. If the data is strictly positive (e.g. counts or CPMs), TMM normalization (Robinson et al., Genome Biology 2010) is then employed on the common peaks. When done on more than two samples, the sample with the highest number of overlaps with other samples is used as a reference. * The 'enriched' approach assumes that enriched regions are on average similarly enriched across samples. Contrarily to 'MAnorm', these regions do not need to be in common across samples/experiments. Note that trimming via the `trim` parameter is performed before calculating means. * The 'top' approach assumes that the maximum enrichment (after trimming according to the `trim` parameter) in peaks is the same across samples/experiments. * The 'S3norm' (Xiang et al., NAR 2020) and '2cLinear' methods try to normalize both enrichment and background simultaneously. S3norm does this in a log-linear fashion (as in the publication), while '2cLinear' does it on the original scale.
Several of the methods rely on random regions to estimate background levels. This means that they will not be entirely deterministic, although normally quite reproducible with experiments that are not very sparse. For fully reproducible results, be sure to set the random seed.
When the data is very sparse (e.g. low sequencing depth, or compiling single nucleotide hits rather than fragment coverage), it might be necessary to increase the `wsize` and `nwind` to get more robust estimates.
Examples
# we get an example bigwig file, and use it twice:
bw <- system.file("extdata/example_atac.bw", package="epiwraps")
bw <- c(sample1=bw, sample2=bw)
# we indicate to use only chr1, because it's the only one in the example file
getNormFactors(bw, useSeqLevels="1")
#> Comparing coverage in random regions...
#> Warning: Some samples have less than 50 non-zero windows. Consider increasing the window size or (better) the number of windows.
#> sample1 sample2
#> 1 1