score_features: Score features using ezlimma and score_fcn
In jdreyf/PANTS: Pathway analysis via network smoothing

Score features using limma_contrasts or limma_cor and then applying score_fcn to moderated t-statistics. Does not currently accept type=="mediation".

1 2	score_features(object, phenotype = NULL, type = c("contrasts", "correlation"), contrast.v = NULL, design = NULL, score_fcn = abs)

`object`	Matrix-like data object containing log-ratios or log-expression values, with rows corresponding to features (e.g. genes) and columns to samples. Must have rownames that are non-duplicated and non-empty.
`phenotype`	Vector of sample characteristics (correlation: numeric; contrasts: character). Should be same length as `ncol(object)`.
`type`	Type of ezlimma analysis per feauture; must be one of"contrasts" (`limma_contrasts`), "correlation" (`limma_cor`), or "mediation" (`hitman`). You can specify just the initial letter.
`contrast.v`	Named vector of contrasts, passed to `makeContrasts`.
`design`	Design matrix of the experiment, with rows corresponding to samples and columns to coefficients to be estimated.
`score_fcn`	Function that transforms the t-statistics from the contrasts into a non-negative value. Its input must be a vector of same length as number of elements in `contrast.v` (usually one). Its output must be a non-negative scalar. Ignored if `hitman` is `TRUE`.