Description Usage Arguments Value
This helps you create a function that can be input as a transformation function for 'sleuth_prep'.
1 2 3 | get_lr_functions(type = "alr", denom_name = NULL, lr_method = "both",
denom_method = "geomean", impute_method = "multiplicative",
delta = NULL, impute_proportion = 0.65, base = "e")
|
type |
either "alr", "clr", or "iqlr" (also accepts "ALR" / "CLR" / "IQLR") |
denom_name |
a character vector of target IDs that will be the denominator. More than one is acceptable, and you can also input a numeric vector of row numbers instead. Note that this will be ignored if 'clr' or 'iqlr' is used. |
lr_method |
the choice of how to conduct compositional normalization. "both" provides a compositional normalization function and a compositional transformation functions; "transform" provides a compositional transformation function that also does the normalization |
denom_method |
the choice of what kind of compositional normalization to do when more than one feature is used. "geomean" takes the geometric of all features within a sample as the size factor, and "DESeq2" takes the median ratio of a feature to its geometric mean across all 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 |
impute_proportion |
percentage of minimum value that
becomes the imputed value. Only used if delta is |
base |
the base used for the logarithm. Currently only supports "e" or "2" (can also specify the number 2). |
a list with two items:
n_functhe normalization function to be used with the 'norm_fun_counts' and 'norm_fun_tpm' options in sleuth
t_functhe transformation function to be used with the 'transform_fun_counts' and 'transform_fun_tpm' options in sleuth
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