prepare_data: Prepare data for use in PLN models

Description

Prepare data in proper format for use in PLN model and its variants. The function (i) merges a count table and a covariate data frame in the most comprehensive way and (ii) computes offsets from the count table using one of several normalization schemes (TSS, CSS, RLE, GMPR, Wrench, etc). The function fails with informative messages when the heuristics used for sample matching fail.

Usage

prepare_data(counts, covariates, offset = "TSS", ...)

Value

A data.frame suited for use in PLN() and its variants with two specials components: an abundance count matrix (in component "Abundance") and an offset vector/matrix (in component "Offset", only if offset is not set to "none")

Arguments

counts: Required. An abundance count table, preferably with dimensions names and species as columns.
covariates: Required. A covariates data frame, preferably with row names.
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).
...: Additional parameters passed on to compute_offset()

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 tools:::Rd_expr_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 tools:::Rd_expr_doi("10.1038/nmeth.2658")

Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biology, 11, R106 tools:::Rd_expr_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 tools:::Rd_expr_doi("10.1186/s12864-018-5160-5")

Examples

Run this code

data(trichoptera)
proper_data <- prepare_data(
 counts     = trichoptera$Abundance,
 covariates = trichoptera$Covariate,
 offset     = "TSS"
)
proper_data$Abundance
proper_data$Offset