`normalize.features.Rd`

This function performs feature normalization according to user- specified parameters.

```
normalize.features(siamcat,
norm.method = c("rank.unit", "rank.std",
"log.std", "log.unit", "log.clr", "std", "pass"),
norm.param = list(log.n0 = 1e-06, sd.min.q = 0.1,
n.p = 2, norm.margin = 1),
feature.type='filtered',
verbose = 1)
```

siamcat | an object of class siamcat-class |
---|---|

norm.method | string, normalization method, can be one of these:
' |

norm.param | list, specifying the parameters of the different normalization methods, see details for more information |

feature.type | string, on which type of features should the function
work? Can be either |

verbose | integer, control output: |

an object of class siamcat-class with normalized features

There are seven different normalization methods available:

`'rank.unit'`

- converts features to ranks and normalizes each column (=sample) by the square root of the sum of ranks`'rank.std'`

- converts features to ranks and applies z-score standardization`'log.clr'`

- centered log-ratio transformation (with the addition of pseudocounts)`'log.std'`

- log-transforms features (after addition of pseudocounts) and applies z-score standardization`'log.unit'`

- log-transforms features (after addition of pseudocounts) and normalizes by features or samples with different norms`'std'`

- z-score standardization without any other transformation`'pass'`

- pass-through normalization will not change the features

The list entries in `'norm.param'`

specify the normalzation parameters,
which are dependant on the normalization method of choice:

`'rank.unit' or 'pass'`

does not require any other parameters`'rank.std' and 'std'`

requires`sd.min.q`

, quantile of the distribution of standard deviations of all features that will be added to the denominator during standardization in order to avoid underestimation of the standard deviation, defaults to 0.1`'log.clr'`

requires`log.n0`

, which is the pseudocount to be added before log-transformation, defaults to`NULL`

leading to the estimation of`log.n0`

from the data`'log.std'`

requires both`log.n0`

and`sd.min.q`

, using the same default values`'log.unit'`

requires next to`log.n0`

also the parameters`n.p`

and`norm.margin`

.`n.p`

specifies the vector norm to be used, can be either`1`

for`x/sum(x)`

or`2`

for`x/sqrt(sum(x^2))`

. The parameter`norm.margin`

specifies the margin over which to normalize, similarly to the`apply`

-syntax: Allowed values are`1`

for normalization over features,`2`

over samples, and`3`

for normalization by the global maximum.

The function additionally allows to perform a frozen normalization on a
different dataset. After normalizing the first dataset, the `norm_feat`

slot in the siamcat object contains all parameters of the normalization,
which you can access via the norm_params accessor.

In order to perform a frozen normalization of a new dataset, you can run the
function supplying the normalization parameters as argument to
`norm.param`

:
`norm.param=norm_params(siamcat_reference)`

. See also the example below.

```
# Example data
data(siamcat_example)
# Simple example
siamcat_norm <- normalize.features(siamcat_example,
norm.method='rank.unit')
#> Features normalized successfully.
# log.unit example
siamcat_norm <- normalize.features(siamcat_example,
norm.method='log.unit',
norm.param=list(log.n0=1e-05, n.p=1, norm.margin=1))
#> Features normalized successfully.
# log.std example
siamcat_norm <- normalize.features(siamcat_example,
norm.method='log.std',
norm.param=list(log.n0=1e-05, sd.min.q=.1))
#> Features normalized successfully.
# Frozen normalization
siamcat_norm <- normalize.features(siamcat,
norm.param=norm_params(siamcat_reference))
#> Error in (function (classes, fdef, mtable) { methods <- .findInheritedMethods(classes, fdef, mtable) if (length(methods) == 1L) return(methods[[1L]]) else if (length(methods) == 0L) { cnames <- paste0("\"", vapply(classes, as.character, ""), "\"", collapse = ", ") stop(gettextf("unable to find an inherited method for function %s for signature %s", sQuote(fdef@generic), sQuote(cnames)), domain = NA) } else stop("Internal error in finding inherited methods; didn't return a unique method", domain = NA)})(list("function"), new("standardGeneric", .Data = function (siamcat, verbose = 1) standardGeneric("filt_feat"), generic = structure("filt_feat", package = "SIAMCAT"), package = "SIAMCAT", group = list(), valueClass = character(0), signature = c("siamcat", "verbose"), default = NULL, skeleton = (function (siamcat, verbose = 1) stop("invalid call in method dispatch to 'filt_feat' (no default method)", domain = NA))(siamcat, verbose)), <environment>) unable to find an inherited method for function ‘filt_feat’ for signature ‘"function"’
```