prepare_data: Prepare data for use in PLN models

In PLN-team/PLNmodels: Poisson Lognormal Models

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",
  call = rlang::caller_env(),
  ...
)

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).
call	Optional. The execution environment in which to set the local error call.
...	Additional parameters passed on to compute_offset()

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")

Note

User supplied offsets should be either vectors/column-matrices or have the same number of column as the original count matrix and either (i) dimension names or (ii) the same dimensions as the count matrix. Samples are trimmed in exactly the same way to remove empty samples.

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 \Sexpr[results=rd]{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 \Sexpr[results=rd]{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 \Sexpr[results=rd]{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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/gb-2010-11-3-r25")}

See Also

compute_offset() for details on the different normalization schemes

Examples

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

PLN-team/PLNmodels documentation built on Sept. 11, 2024, 4:08 p.m.