compute_offset {PLNmodels}

Compute offsets from a count data using one of several normalization schemes

Description

Computes offsets from the count table using one of several normalization schemes (TSS, CSS, RLE, GMPR, Wrench, TMM, etc) described in the literature.

Usage

compute_offset(
  counts,
  offset = c("TSS", "GMPR", "RLE", "CSS", "Wrench", "TMM", "none"),
  scale = c("none", "count"),
  ...
)

Arguments

counts

Required. An abundance count table, preferably with dimensions names and species as columns.

offset

Optional. Normalization scheme used to compute scaling factors used as offset during PLN inference. Available schemes are "TSS" (Total Sum Scaling, default), "CSS" (Cumulative Sum Scaling, used in metagenomeSeq), "RLE" (Relative Log Expression, used in DESeq2), "GMPR" (Geometric Mean of Pairwise Ratio, introduced in Chen et al., 2018), Wrench (introduced in Kumar et al., 2018) or "none". Alternatively the user can supply its own vector or matrix of offsets (see note for specification of the user-supplied offsets).

scale

Either "none" (default) or "count". Should the offset be normalized to be on the same scale as the counts ?

...

Additional parameters passed on to specific methods (for now CSS and RLE)

Details

RLE has additional pseudocounts and type arguments to add pseudocounts to the observed counts (defaults to 0L) and to compute offsets using only positive counts (if type == "poscounts"). This mimics the behavior of DESeq2::DESeq() when using sfType == "poscounts". CSS has an additional reference argument to choose the location function used to compute the reference quantiles (defaults to median as in the Nature publication but can be set to mean to reproduce behavior of functions cumNormStat* from metagenomeSeq). Wrench has two additional parameters: groups to specify sample groups and type to either reproduce exactly the default Wrench::wrench() behavior (type = "wrench", default) or to use simpler heuristics (type = "simple"). Note that (i) CSS normalization fails when the median absolute deviation around quantiles does not become instable for high quantiles (limited count variations both within and across samples) and/or one sample has less than two positive counts, (ii) RLE fails when there are no common species across all samples (unless type == "poscounts" has been specified) and (iii) GMPR fails if a sample does not share any species with all other samples. TMM code between two libraries is simplified and adapted from M. Robinson (edgeR:::.calcFactorTMM). The final output is however different from the one produced by edgeR:::.calcFactorTMM as they are intended to be used as such in the model (whereas they need to be multiplied by sequencing depths in edgeR)

Value

If offset = "none", NULL else a vector of length nrow(counts) with one offset per sample.

References

Chen, L., Reeve, J., Zhang, L., Huang, S., Wang, X. and Chen, J. (2018) GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ, 6, e4600 doi:10.7717/peerj.4600

Paulson, J. N., Colin Stine, O., Bravo, H. C. and Pop, M. (2013) Differential abundance analysis for microbial marker-gene surveys. Nature Methods, 10, 1200-1202 doi:10.1038/nmeth.2658

Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biology, 11, R106 doi:10.1186/gb-2010-11-10-r106

Kumar, M., Slud, E., Okrah, K. et al. (2018) Analysis and correction of compositional bias in sparse sequencing count data. BMC Genomics 19, 799 doi:10.1186/s12864-018-5160-5

Robinson, M.D., Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11, R25 doi:10.1186/gb-2010-11-3-r25

Examples

data(trichoptera)
counts <- trichoptera$Abundance
compute_offset(counts)
## Other normalization schemes
compute_offset(counts, offset = "RLE", pseudocounts = 1)
compute_offset(counts, offset = "Wrench", groups = trichoptera$Covariate$Group)
compute_offset(counts, offset = "GMPR")
compute_offset(counts, offset = "TMM")
## User supplied offsets
my_offset <- setNames(rep(1, nrow(counts)), rownames(counts))
compute_offset(counts, offset = my_offset)