DeconRNASeq: Function for Deconvolution of Complex Samples from RNA-Seq.

Description Usage Arguments Details Value Author(s) References Examples

View source: R/DeconRNASeq.R

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

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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 [email protected] Joseph D. Szustakowski [email protected]

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

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## 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)
#

DeconRNASeq documentation built on Nov. 17, 2017, 11:10 a.m.