exp_preprocess {BioNERO} | R Documentation

## 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)

[Package BioNERO version 1.0.4 Index]