Description Usage Arguments Details Value Author(s) References Examples

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 .

1 | ```
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 [email protected] Joseph D. Szustakowski [email protected]

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.

1 2 3 4 5 6 7 8 9 10 11 | ```
## 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)
#
``` |

```
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
```

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