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Automatical CoDA transformation

auto_coda.Rd

If the data has more than 50 variables, call transform_alr(). Otherwise, call transform_clr(), following the same strategy in glycowork.

Usage

auto_coda(x, by = NULL, gamma = 0.1, group_scales = NULL)

Arguments

x

Either a glyexp_experiment object or a matrix. If a matrix, rows should be variables and columns should be samples.

by

Either a column name in sample_info (for glyexp_experiment input) or a factor/vector with one value per sample.

gamma

Standard deviation of the scale-uncertainty model on the log2 scale. Default is 0.1. Set to 0 for deterministic transformation.

group_scales

Optional informed group scales. For binary comparisons, this can be a single positive ratio for the second group relative to the first, or two positive scales from which that ratio is derived. For multi-group data, provide a positive vector with one scale per group.

Value

Returns the same type as the input. If x is a glyexp_experiment, returns a glyexp_experiment with a CoDA-transformed expression matrix (ALR if >50 variables, CLR otherwise).

Algorithmic details

The stochastic branch follows glycowork: when gamma > 0, CLR noise is sampled per feature-sample entry rather than once per sample. Without informed scales, this corresponds to jittering around the sample-specific log2 geometric mean of the non-zero components and then returning to ratio space.

The group_scales argument is interpreted in the same way as glycowork's custom_scale. For exactly two groups, provide the total-signal ratio of the second group relative to the first, either directly as a scalar or implicitly through two per-group scales. For multi-group data, provide one positive scale per group; these scales are used only in the stochastic branch.

Motif quantification

If you intend to use ALR/CLR transformation with motif quantification, you should apply the transformation after motif quantification rather than before. In another words, the recommended workflow is:

  1. Perform data preprocessing.

clean_exp <- auto_clean(exp)

  1. Perform motif quantification on the cleaned experiment.

motif_exp <- glydet::quantify_motifs(clean_exp, motifs)

  1. Perform ALR/CLR transformation on the motif-quantified experiment.

coda_motif_exp <- auto_coda(motif_exp, by = "group", gamma = 0.1)

  1. Proceed with downstream analyses on the transformed motif experiment.

dea_res <- glystats::gly_ttest(coda_motif_exp)