normalizeAssay {MatrixQCvis} | R Documentation

## Normalize a data sets (reduce technical sample effects)

### Description

The function 'normalizeAssay' performs normalization by sum of the (count/intensity) values per sample or quantile division per sample or by quantile normalization (adjusting the distributions that they become identical in statistical distributions). The divisor for quantile division (e.g., the 75 Quantile normalization is performed by using the 'normalizeQuantiles' function from 'limma'.

### Usage

    normalizeAssay(
      a,
      method = c("none", "sum", "quantile division", "quantile"),
      probs
    )

### Arguments

`a` | 'matrix' with samples in columns and features in rows
`method` | 'character', one of '"none"', '"sum"', '"quantile division"', '"quantile"'
`probs` | 'numeric', ranging between '[0, 1)'. 'probs' is used as the divisor for quantile division in 'method = "quantile division"'

### Details

Internal usage in 'shinyQC'. If 'method' is set to '"none"', the object 'x' is returned as is (pass-through).

### Value

'matrix'

### Examples

    a <- matrix(1:100, nrow = 10, ncol = 10,
            dimnames = list(1:10, paste("sample", 1:10)))
    normalizeAssay(a, "sum")

[Package MatrixQCvis version 1.0.0 Index]