Calculate GenesRanking

Description

Calculates the genes ranking and/or plots the posterior probability of the genes ordered by class ranking.

Usage

calculateGenesRanking(eset=NULL, sampleLabels=NULL, 
numGenesPlot=1000, plotTitle="Significant genes", plotLp=TRUE, 
lpThreshold = 0.95, numSignificantGenesType="ranked", 
returnRanking="full", nullHiphothesisFilter=0.95,  nGenesExprDiff=1000, 
geneLabels=NULL, precalcGenesRanking=NULL, IQRfilterPercentage= 0, 
verbose=TRUE)

Arguments

Details

Significant genes: Genes with posterior probability over 'lpThreshold'.
More significant genes may mean:

• Very different class

• More systemic disease

Plot lines represet the posterior probability of genes, sorted by rank from left to right.

In order to find genes that diferentiate the classes from each other, the function ranks the genes bassed on their posterior probability for each class.
The posterior probability represents how well a gene differentiates samples from a class, from samples from other classes. Therefore, Genes with high posterior probability are good to differentiate a class from all the others.
This posterior probability is calculated by emfit (pkg:EBarrays), an expectation-maximization (EM) algorithm for gene expression mixture model.

Value

• GenesRanking Optional. Requested genes ranking.

• Plot Optional. Plot of the posterior probability of the top genes.

See Also

plot.GenesRanking is a shortcut to plotting a previusly calculated genes ranking.
i.e. plot(genesRanking)

Examples

# Load an expressionSet:
library(leukemiasEset)
data(leukemiasEset)

# Select the train samples: 
trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) 
# summary(leukemiasEset$LeukemiaType[trainSamples])

## Not run: 
######
# Calculate/plot the significant genes (+ info) of a dataset 
# without training classifier/calculating network
######
# Return only significant genes ranking (default)
signGenesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], 
    sampleLabels="LeukemiaType")
numGenes(signGenesRanking)

# Return the full genes ranking:
fullRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], 
    sampleLabels="LeukemiaType", returnRanking="full")
numGenes(fullRanking)
numSignificantGenes(fullRanking)
# The significant genes can then be extracted from it:
signGenesRanking2  <- getTopRanking(fullRanking, 
    numGenesClass=numSignificantGenes(fullRanking))
numGenes(signGenesRanking2)

# Changing the posterior probability required to consider genes significant:
signGenesRanking90 <- calculateGenesRanking(leukemiasEset[,trainSamples], 
    sampleLabels="LeukemiaType", lpThreshold=0.9)
numGenes(signGenesRanking90)

## End(Not run)
######
# Ploting previously calculated rankings:
######
# Load or calculate a ranking (or a classifier with geNetClassifier)
data(leukemiasClassifier) # Sample trained classifier, @genesRanking

# Default plot:
# - equivalent to plot(leukemiasClassifier@genesRanking)
# - in this case, the previously calculated 'fullRanking' 
#   is equivalent to 'leukemiasClassifier@genesRanking'
calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking)

# Changing arguments:
calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking, 
    numGenesPlot=5000, plotTitle="Leukemias", lpThreshold=0.9)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(geNetClassifier)
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, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, 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, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

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

Loading required package: EBarrays
Loading required package: lattice
Loading required package: minet
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/geNetClassifier/calculateGenesRanking.Rd_%03d_medium.png", width=480, height=480)
> ### Name: calculateGenesRanking
> ### Title: Calculate GenesRanking
> ### Aliases: calculateGenesRanking
> ### Keywords: classif
> 
> ### ** Examples
> 
> 
> # Load an expressionSet:
> library(leukemiasEset)
> data(leukemiasEset)
> 
> # Select the train samples: 
> trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) 
> # summary(leukemiasEset$LeukemiaType[trainSamples])
> 
> ## Not run: 
> ##D ######
> ##D # Calculate/plot the significant genes (+ info) of a dataset 
> ##D # without training classifier/calculating network
> ##D ######
> ##D # Return only significant genes ranking (default)
> ##D signGenesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], 
> ##D     sampleLabels="LeukemiaType")
> ##D numGenes(signGenesRanking)
> ##D 
> ##D # Return the full genes ranking:
> ##D fullRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], 
> ##D     sampleLabels="LeukemiaType", returnRanking="full")
> ##D numGenes(fullRanking)
> ##D numSignificantGenes(fullRanking)
> ##D # The significant genes can then be extracted from it:
> ##D signGenesRanking2  <- getTopRanking(fullRanking, 
> ##D     numGenesClass=numSignificantGenes(fullRanking))
> ##D numGenes(signGenesRanking2)
> ##D 
> ##D # Changing the posterior probability required to consider genes significant:
> ##D signGenesRanking90 <- calculateGenesRanking(leukemiasEset[,trainSamples], 
> ##D     sampleLabels="LeukemiaType", lpThreshold=0.9)
> ##D numGenes(signGenesRanking90)
> ## End(Not run)
> ######
> # Ploting previously calculated rankings:
> ######
> # Load or calculate a ranking (or a classifier with geNetClassifier)
> data(leukemiasClassifier) # Sample trained classifier, @genesRanking
> 
> # Default plot:
> # - equivalent to plot(leukemiasClassifier@genesRanking)
> # - in this case, the previously calculated 'fullRanking' 
> #   is equivalent to 'leukemiasClassifier@genesRanking'
> calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking)
> 
> # Changing arguments:
> calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking, 
+     numGenesPlot=5000, plotTitle="Leukemias", lpThreshold=0.9)
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>