DeconRNASeq {DeconRNASeq} | R Documentation |
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 .
DeconRNASeq(datasets, signatures, proportions = NULL, checksig = FALSE, known.prop = FALSE, use.scale = TRUE, fig = TRUE)
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 |
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.
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 |
Ting Gong tinggong@gmail.com Joseph D. Szustakowski joseph.szustakowski@novartis.com
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.
## 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) #