normalize_data | R Documentation |
hipathia
Transforms the rank of the SummarizedExperiment or matrix of gene expression
to [0,1] in order
to be processed by hipathia
. The transformation may be performed
in two different ways. If percentil = FALSE
, the transformation
is a re-scaling of the rank of the matrix. If percentil = TRUE
,
the transformation is performed assigning to each cell its percentil in
the corresponding distribution. This option is recommended for
distributions with very long tails.
normalize_data( data, sel_assay = 1, by_quantiles = FALSE, by_gene = FALSE, percentil = FALSE, truncation_percentil = NULL )
data |
Either a SummarizedExperiment or a matrix of gene expression. |
sel_assay |
Character or integer, indicating the assay to be normalized in the SummarizedExperiment. Default is 1. |
by_quantiles |
Boolean, whether to normalize the data by quantiles. Default is FALSE. |
by_gene |
Boolean, whether to transform the rank of each row of the matrix to [0,1]. Default is FALSE. |
percentil |
Boolean, whether to take as value the percentil of each sample in the corresponding distribution. |
truncation_percentil |
Real number p in [0,1]. When provided, values beyond percentil p are truncated to the value of percentil p, and values beyond 1-p are truncated to percentil 1-p. By default no truncation is performed. |
This transformation may be applied either to the whole matrix
(by setting by_gene = FALSE
), which we strongly recommend, or to
each of the rows (by setting by_gene = TRUE
), allowing each gene
to have its own scale.
A previous quantiles normalization may be applied by setting
by_quantiles = TRUE
. This is recommended for noisy data.
For distributions with extreme outlayer values, a percentil p
may be given to the parameter truncation_percentil
. When provided,
values beyond percentil p are truncated to the value of percentil p, and
values beyond 1-p are truncated to percentil 1-p. This step is performed
before any other tranformation. By default no truncation is performed.
Matrix of gene expression whose values are in [0,1].
data("brca_data") trans_data <- translate_data(brca_data, "hsa") exp_data <- normalize_data(trans_data) exp_data <- normalize_data(trans_data, by_quantiles = TRUE, truncation_percentil=0.95)
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