DeconRNASeq {DeconRNASeq} | R Documentation

## Function for Deconvolution of Complex Samples from RNA-Seq.

### Description

This function predicts proportions of constituting cell types from gene expression data generated from RNA-Seq data. Perform nonnegative quadratic programming to get per-sample based globally optimized solutions for constituting cell types .

### Usage

    DeconRNASeq(datasets, signatures, proportions = NULL, checksig = FALSE, known.prop = FALSE, use.scale = TRUE, fig = TRUE)

### Arguments

`datasets` | measured mixture data matrix, genes (transcripts) e.g. gene counts by samples, . The user can choose the appropriate counts, RPKM, FPKM etc..
`signatures` | signature matrix from different tissue/cell types, genes (transcripts) by cell types. For gene counts, the user can choose the appropriate counts, RPKM, FPKM etc..
`proportions` | proportion matrix from different tissue/cell types.
`checksig` | whether the condition number of signature matrix should be checked, efault = FALSE
`known.prop` | whether the proportions of cell types have been known in advanced for proof of concept, default = FALSE
`use.scale` | whether the data should be centered or scaled, default = TRUE
`fig` | whether to generate the scatter plots of the estimated cell fractions vs. the true proportions of cell types, default = TRUE

### Details

Data in the originally measured mixuture sample matrix: datasets and reference matrix: signatures, need to be non-negative. We recommend to deconvolute without log-scale.

### Value

Function DeconRNA-Seq returns a list of results

`out.all` | estimated cell type fraction matrix for all the mixture samples
`out.pca` | svd calculated PCA on the mixture samples to estimate the number of pure sources according to the cumulative R2
`out.rmse` | averaged root mean square error (RMSE)) measuring the differences between fractions predicted by our model and the truth fraction matrix for all the tissue types

### Author(s)

Ting Gong tinggong@gmail.com Joseph D. Szustakowski joseph.szustakowski@novartis.com

### References

Gong, T., et al. (2011) Optimal Deconvolution of Transcriptional Profiling Data Using Quadratic Programming with Application to Complex Clinical Blood Samples, PLoS One, 6, e27156.

### Examples

    ## Please refer our demo
    ##source("DeconRNASeq.R")
    ### multi_tissue: expression profiles for 10 mixing samples from multiple tissues
    #data(multi_tissue.rda)

    #datasets <- x.data[,2:11]
    #signatures <- x.signature.filtered.optimal[,2:6]
    #proportions <- fraction

    #DeconRNASeq(datasets, signatures, proportions, checksig=FALSE, known.prop = TRUE, use.scale = TRUE)
    #

[Package DeconRNASeq version 1.32.0 Index]