deconv: Deconvolution

In yunzhang813/simDeNet-R-Package: Simulation study on deconvolution for co-expression network

Description

An integrated function for deconvolution of mixed cell-type samples, based on the ISOpureR package.

Usage

deconv(mixed, ref, seed=123, ...)

Arguments

mixed	data matrix (at the log2-transformed scale) for mixed samples, with genes in rows and samples in columns.
ref	data matrix (at the log2-transformed scale) for reference samples, with genes in rows and samples in columns.
seed	random seed for reproducible result.
...	arguments passed to ISOpure.step1.CPE().

Value

A list of the following components:

expr.deconv	estimated expression profiles (at the log2-transformed scale) for the targeted pure samples after deconvolution.
est.prop	estimated mixing proportions for the targeted pure samples.

Warning

Use warnings() to see warnings. Warnings are expected in ISOpureR from the optimization calculations.

Note

The original functions in the ISOpureR package input data at the raw scale (i.e. 2^x) and return the estimated profiles also at the raw scale. Our integrated function takes care the data transformation internally, so that it inputs and returns data at the log2-transformed scale, keeping consistancy with other functions in our package.

References

G Quon, S Haider, AG Deshwar, A Cui, PC Boutros, QD Morris. Computational purification of individual tumor gene expression profiles. Genome Medicine (2013) 5:29, http://genomemedicine.com/content/5/3/29.

G Quon, QD Morris. ISOLATE: a computational strategy for identifying the primary origin of cancers using high-thoroughput sequencing. Bioinformatics 2009, 25:2882-2889 http://bioinformatics.oxfordjournals.org/content/25/21/2882.

See Also

ISOpure.step1.CPE and ISOpure.step2.PPE from the ISOpureR package.

Examples

## Firstly, let's generate some data.
set.seed(999)
data("celltype")
mu.T <- expr[,ctab$Fastq_file_name[which(ctab$X3_letter_code=="ASM")]]
mu.N <- expr[,ctab$Fastq_file_name[which(ctab$X3_letter_code=="AEC")]]
## number of samples to simulate
n.samp <- 5
## parameters for correlation design of cell type T
rho <- c(0.9,0.8,0.7)
block.size <- c(5,10,15)
str.type <- c("interchangeable","decaying","star")
## one-step simulation
out.oneStepSim <- oneStepSim(n.samp, mu.T, mu.N, rho=rho, block.size=block.size, str.type=str.type)

## Deconvolution. Pure samples for cell type N is used as reference.
## Not run: 
out.deconv <- deconv(mixed=out.oneStepSim$expr.mixed, ref=out.oneStepSim$expr.pure.N)

## End(Not run)

yunzhang813/simDeNet-R-Package documentation built on Dec. 24, 2019, 3:02 p.m.