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

In DeconRNASeq: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data

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 [e-mail] Joseph D. Szustakowski [e-mail]

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)
#

Example output

Loading required package: limSolve
Loading required package: pcaMethods
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'pcaMethods'

The following object is masked from 'package:stats':

    loadings

Loading required package: ggplot2

Attaching package: 'ggplot2'

The following object is masked from 'package:limSolve':

    resolution

Loading required package: grid

DeconRNASeq documentation built on Nov. 8, 2020, 7:51 p.m.