deco-package {deco} | R Documentation

## DECO: DEcomposing heterogeneous Cohorts from Omic profiling.

### Description

DECO integrates a two-step methodology to find out outlier behavior of features which could determine a new subclass of samples. Thus, a subsampling method with LIMMA (Stratified Differential Analysis or RDA) will be applied to a omic matrix data, previously normalized. After that, Non-Symmetrical Correspondence Analysis (NSCA) will be computed on frequency matrix of differential events generated by subsampling.

The feature-sample relationships will define stratified profiles with statistical significance. Any new subclass defined could be associated to hidden phenotypic variables.

### Details

Package: | deco
Type: | Package
Version: | 0.99
Date: | 2018-11-15
License: | GPL 3.0

### Author(s)

Francisco Jose Campos Laborie, Jose Manuel Sanchez Santos and Javier De las Rivas.

Maintainer: Francisco Jose Campos Laborie <fjcamlab@usal.es>, Cancer Research Centre (Salamanca) <jrivas@usal.es>

### References

Campos-Laborie, FJ et al. DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling. (2018)

Scarfo, Irene et al. Identification of a new subclass of ALK negative ALCL expressing aberrant levels of ERBB4 transcripts. Blood (2015). http://dx.doi.org/10.1182/blood-2014-12-614503.

### See Also

`decoRDA`, `decoNSCA`, `decoReport`, `voom`

### Examples

    #### ALCL EXAMPLE (Scarfo et al., 2015. Blood) ####
    ## Group-VS-group comparison

    ########################
    # Loading example data #
    ########################
    # Data from two subtypes (ALK+ and ALK-) of Anaplastic Large Cell Leukemia (ALCL).
    data(ALCLdata)

    ## Classes vector to run a supervised analysis to compare both classes.
    classes.ALCL <- colData(ALCL)[,"Alk.positivity"]
    names(classes.ALCL) <- colnames(ALCL)

    ####################################
    # Parallelization via BiocParallel #
    ####################################
    # Non-parallel computing
    bpparam <- SerialParam()

    # Computing in shared memory
    # all cores by deault
    bpparam <- MulticoreParam()

    #######################################################################
    # RUNNING SUBSAMPLING OF DATA: BINARY design (two classes of samples) #
    #######################################################################
    # if annotation and rm.xy == TRUE, then
    # library(Homo.sapiens)

    # Not run as example
    # sub.ma.3r.1K <- decoRDA(data = assay(ALCL), classes = classes.ALCL, q.val = 0.01,
    #                rm.xy = TRUE, r = NULL, control = "pos", annot = FALSE, bpparam = bpparam,
    #                id.type = "ENSEMBL", iterations = 10000, pack.db = "Homo.sapiens")

    #########################################################################################
    # RUNNING NSCA STEP: Looking for subclasses within a category/class of samples compared #
    #########################################################################################
    # Not run as example
    # deco.results.ma <- decoNSCA(sub = sub.ma.3r.1K, v = 80, method = "ward.D", bpparam = bpparam,
    #                        k.control = 3, k.case = 3, samp.perc = 0.05, rep.thr = 10)

    # Phenotypical data from TCGA RNAseq samples.
    colData(ALCL)

    ########################################################
    # PDF report with feature-sample patterns or subgroups #
    ########################################################
    ## Generate PDF report with relevant information and several plots.

    ## Binary example (ALK+ vs ALK-) -not run as example-
    # decoReport(deco.results.ma, sub.ma.3r.1K,
    #           pdf.file = "report_example_microarray_binary.pdf",
    #           info.sample = as.data.frame(colData(ALCL)[,8:10]),
    #           cex.names = 0.3, print.annot = TRUE)

[Package deco version 1.8.0 Index]