alr_transformation: Calculate ALR
In warrenmcg/sleuth-ALR: Compositional Approach to Sleuth Data Analysis

This function calculates the additive logratio transformation. This function uses calculate_alr internally after processing the data to handle zeros. Essential zeros (i.e. target IDs with zeros in all samples) can be optionally excluded, and all remaining zeros, including rounded zeros (i.e. target IDs with at least one zero value) are always imputed using the hidden impute_zeros function.

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alr_transformation(mat, denom_name, base = "e", remove_zeros = FALSE,
  denom_method = "geomean", impute_method = "multiplicative",
  delta = NULL, impute_proportion = 0.65)

`mat`	an D x M matrix of D target IDs and M samples
`denom_name`	a character vector of target IDs or an integer vector of row numbers. If there is more than one, the geometric mean of the denominator values within one sample is used as the denominator.
`base`	what should the base of the logarithm be? currently only supports base "e" and base 2.
`remove_zeros`	boolean to see if this function should remove essential zeros (features with zeros in all samples). The default is `FALSE` to be compatible with sleuth, as its default filter removes essential zeros.
`denom_method`	either 'geomean' or 'DESeq2' to use either the geometric mean of the features as the denominator, or the DESeq2-style size factors as the denominator. If there is only one feature, 'geomean' uses the feature's value itself, whereas 'DESeq2' will use the ratio of the feature to the geometric mean across samples.
`impute_method`	which method to use for imputing zeros. 'multiplicative' (default) sets all values smaller than a imputation value 'delta' (determined by delta or impute_proportion) to that imputation value, and reduces all other values by the amount X * (1 - delta*num_zero_values / sum_constraint). 'additive' is similar to most other tools, and just adds the imputation value to all entries ('delta' must be specified)
`delta`	a number that is the imputed value. If `NULL`, delta = impute_proportion * (minimum value in sample)
`impute_proportion`	percentage of minimum value that becomes the imputed value. Only used if delta is `NULL`