batch.normalize {DOQTL} | R Documentation

## Batch normalize the X & Y intensity data.

### Description

This function batch normalizes the X & Y intensity data by subtracting batch medians from the X & Y intensities.

### Usage

      batch.normalize(path = ".", snps)
      quantilenorm(x1, y1, x2, y2)

### Arguments

`path` | Character, the full path to the input files, which must be either "x.txt" and "y.txt" or "x.filt.txt" and "y.filt.txt".
`snps` | Data.frame, with three columns containing SNP ID, chromosome and Mb location in that order. May be obtained from ftp://ftp.jax.org/MUGA.
`x1` | Numeric matrix containing X intensities for batch 1 containing samples in rows and markers in columns. Number of samples should be larger than x2.
`y1` | Numeric matrix containing Y intensities for batch 1 containing samples in rows and markers in columns. Number of samples should be larger than y2.
`x2` | Numeric matrix containing X intensities for batch 2 containing samples in rows and markers in columns.
`y2` | Numeric matrix containing Y intensities for batch 2 containing samples in rows and markers in columns.

### Details

quantile.norm adjusts the intensities of samples in batch 2 to those of batch 1. The number of samples in batch 1 should be greater than the number of samples in batch 2. At each SNP, we form quantiles of the X1 (or Y1) intensity distribution, discarding the upper and lower 0.01

### Value

FALSEor batch.normalize: returns value is returned. The batch normalized intensities are written to "x.filt.batch.norm.txt" and "y.filt.batch.norm.txt".

FALSEor quantilenorm: returns normalized X and Y values for batch 2.

### Note

FALSEuture releases may include more sophisticated normalization algorithms.

### Author(s)

Daniel Gatti

### See Also

`extract.raw.data`, `filter.samples`

### Examples

      ## Not run:
        load(url("ftp://ftp.jax.org.MUGA/muga_snps.Rdata"))
        batch.normalize(path = "/demo/MUGA/", snps = muga_snps)

    ## End(Not run)

[Package DOQTL version 1.19.0 Index]