exp_preprocess: Preprocess expression data for network reconstruction

In almeidasilvaf/BioNERO: Biological Network Reconstruction Omnibus

Preprocess expression data for network reconstruction

Description

Preprocess expression data for network reconstruction

Usage

exp_preprocess(
  exp,
  NA_rm = TRUE,
  replaceby = 0,
  Zk_filtering = TRUE,
  zk = -2,
  cor_method = "spearman",
  remove_nonexpressed = TRUE,
  method = "median",
  min_exp = 1,
  min_percentage_samples = 0.25,
  remove_confounders = TRUE,
  variance_filter = FALSE,
  n = NULL,
  percentile = NULL,
  vstransform = FALSE
)

Arguments

exp	A gene expression data frame with genes in row names and samples in column names or a 'SummarizedExperiment' object.
NA_rm	Logical. It specifies whether to remove missing values from the expression data frame or not. Default = TRUE.
replaceby	If NA_rm is TRUE, what to use instead of NAs. One of 0 or 'mean'. Default is 0.
Zk_filtering	Logical. It specifies whether to filter outlying samples by Zk or not. Default: TRUE.
zk	If Zk_filtering is TRUE, the standardized connectivity threshold. Samples below this threshold will be considered outliers. Default is -2.
cor_method	If Zk_filtering is TRUE, the correlation method to use. One of 'spearman', 'bicor', or 'pearson'. Default is 'spearman'.
remove_nonexpressed	Logical. It specifies whether non-expressed genes should be removed or not. Default is TRUE.
method	If remove_nonexpressed is TRUE, the criterion to filter non-expressed genes out. One of "mean", "median", "percentage", or "allsamples". Default is 'median'.
min_exp	If method is 'mean', 'median', or 'allsamples', the minimum value for a gene to be considered expressed. If method is 'percentage', the minimum value each gene must have in at least n percent of samples to be considered expressed.
min_percentage_samples	If method is 'percentage', expressed genes must have expression >= min_exp in at least this percentage. Values must range from 0 to 1. Default = 0.25.
remove_confounders	Logical. If TRUE, it removes principal components that add noise to the data.
variance_filter	Logical. If TRUE, it will filter genes by variance. Default is FALSE.
n	If variance_filter is TRUE, the number of most variable genes to keep.
percentile	If variance_filter is TRUE, the percentage of most variable genes to keep.
vstransform	Logical indicating if data should be variance stabilizing transformed. This parameter can only be set to TRUE if data is a matrix of raw read counts.

Value

Processed gene expression data frame with gene IDs in row names and sample names in column names or 'SummarizedExperiment' object.

Author(s)

Fabricio Almeida-Silva

References

Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 1-21.

See Also

varianceStabilizingTransformation

Examples

data(zma.se)
exp <- exp_preprocess(zma.se, variance_filter=TRUE, n=1000)

almeidasilvaf/BioNERO documentation built on Oct. 9, 2024, 1:49 a.m.