Description Usage Arguments Value
This function calculates the interquartile logratio transformation. This function first processes the data to handle zeros. It then calculates which features have the variance across samples within the interquartile range after the CLR transformation. It then uses calculate_alr internally after processing the data to handle zeros. Essential zeros (i.e. target IDs with zeros in all samples) are excluded, and round zeros (i.e. target IDs with at least one zero value) are imputed using the mulitplicative method.
1 2 3 | iqlr_transformation(mat, 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 |
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 |
denom_method |
either 'geomean' or 'DESeq2' to
use either the geometric mean of the IQLR features as the
denominator, or the DESeq2-style size factors (focused on the median
among the IQLR features) as the denominator. The IQLR features
are selected using |
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 |
impute_proportion |
percentage of minimum value that
becomes the imputed value. Only used if delta is |
(D - n - z) x M matrix of ALR-transformed values, with n equal to the number of denominator values and z are the number of rows with essential zeros.
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