unmix {DESeq2} | R Documentation

## Unmix samples using loss in a variance stabilized space

### Description

Unmixes samples in `x` according to `pure` components, using numerical optimization. The components in `pure` are added on the scale of gene expression (either normalized counts, or TPMs). The loss function when comparing fitted expression to the samples in `x` occurs in a variance stabilized space. This task is sometimes referred to as "deconvolution", and can be used, for example, to identify contributions from various tissues. Note: some groups have found that the mixing contributions may be more accurate if very lowly expressed genes across `x` and `pure` are first removed. We have not explored this fully. Note: if the `pbapply` package is installed a progress bar will be displayed while mixing components are fit.

### Usage

    unmix(x, pure, alpha, shift, power = 1, format = "matrix", quiet = FALSE)

### Arguments

`x` | normalized counts or TPMs of the samples to be unmixed
`pure` | normalized counts or TPMs of the "pure" samples
`alpha` | for normalized counts, the dispersion of the data when a negative binomial model is fit. this can be found by examining the asymptotic value of `dispersionFunction(dds)`, when using `fitType="parametric"` or the mean value when using `fitType="mean"`.
`shift` | for TPMs, the shift which approximately stabilizes the variance of log shifted TPMs. Can be assessed with `vsn::meanSdPlot`.
`power` | either 1 (for L1) or 2 (for squared) loss function. Default is 1.
`format` | `"matrix"` or `"list"`, default is `"matrix"`. whether to output just the matrix of mixture components, or a list (see Value).
`quiet` | suppress progress bar. default is FALSE, show progress bar if pbapply is installed.

### Value

a matrix, the mixture components for each sample in `x` (rows). The "pure" samples make up the columns, and so each row sums to 1. If colnames existed on the input matrices they will be propagated to the output matrix. If `format="list"`, then a list, containing as elements: (1) the matrix of mixture components, (2) the correlations in the variance stabilized space of the fitted samples to the samples in `x`, and (3) the fitted samples as a matrix with the same dimension as `x`.

### Examples

    # some artificial data
    cts <- matrix(c(80,50,1,100,
                    1,1,60,100,
                    0,50,60,100), ncol=4, byrow=TRUE)
    # make a DESeqDataSet
    dds <- DESeqDataSetFromMatrix(cts,
      data.frame(row.names=seq_len(ncol(cts))), ~1)
    colnames(dds) <- paste0("sample",1:4)

    # note! here you would instead use
    # estimateSizeFactors() to do actual normalization
    sizeFactors(dds) <- rep(1, ncol(dds))

    norm.cts <- counts(dds, normalized=TRUE)

    # 'pure' should also have normalized counts...
    pure <- matrix(c(10,0,0,
                     0,0,10,
                     0,10,0), ncol=3, byrow=TRUE)
    colnames(pure) <- letters[1:3]

    # for real data, you need to find alpha after fitting estimateDispersions()
    mix <- unmix(norm.cts, pure, alpha=0.01)

[Package DESeq2 version 1.32.0 Index]