scudoTrain: Performs SCUDO analysis

In Matteo-Ciciani/rScudo: Signature-based Clustering for Diagnostic Purposes

Performs SCUDO analysis

Description

SCUDO (Signature-based ClUstering for DiagnOstic purposes) is a rank-based method for the analysis of gene expression profiles. This function computes gene signatures for each sample and consensus signatures for each group specified. A distance matrix is also computed, that can be used by the function scudoNetwork to generate a graph in which each node is a sample and an edge between two nodes quantitatively represents the similarity between their respective signatures.

Usage

scudoTrain(expressionData, groups, nTop, nBottom, alpha = 0.1,
    foldChange = TRUE, groupedFoldChange = FALSE, featureSel = TRUE,
    logTransformed = NULL, parametric = FALSE, pAdj = "none", distFun = NULL)

Arguments

expressionData	either an ExpressionSet, a SummarizedExperiment, a data.frame or a matrix of gene expression data, with a column for each sample and a row for each feature
groups	factor containing group labels for each sample in expressionData
nTop	number of up-regulated features to include in the signatures
nBottom	number of down-regulated features to include in the signatures
alpha	p-value cutoff for the optional feature selection step. If feature selection is skipped, alpha is ignored
foldChange	logical, whether or not to compute fold-changes from expression data
groupedFoldChange	logical, whether or not to take into account the groups when computing fold-changes. See Details for a description of the computation of fold-changes
featureSel	logical, whether or not to perform a feature selection. Feature selection is performed using one of four tests: Student's t-test, ANOVA, Wilcoxon-Mann-Withney test, or Kruskal-Wallis test. The test used depends on the number of groups and the parametric argument
logTransformed	logical or NULL. It indicates whether the data is log-transformed. If NULL, an attempt is made to guess if the data is log-transformed
parametric	logical, whether to use a parametric or a non-parametric test for the feature selection
pAdj	pAdj method to use to adjust the p-values in the feature selection step. See p.adjust.methods for a list of adjustment methods
distFun	the function used to compute the distance between two samples. See Details for the specification of this function

Details

Given a set of expression profiles with known classification, scudoTrain computes a list of signatures composed of genes over- and under-expressed in each sample. It also compute consensus signatures for each group and uses the signatures to compute a distance matrix that quantifies the similarity between the signatures of pairs of samples.

Before computing the signatures, two optional perprocessing steps are performed. In the first step fold-changes are compured from expression values. If the parameter groupedFoldChange is TRUE, the fold-changes are computed in two steps: first the mean expression value for each feature in each group is computed. Then, the fold-changes for each feature are computed dividing the expression values by the mean of the group means. If the the parameter groupedFoldChange is FALSE, the fold-changes are computed dividing the expression value of each feature by the mean expression value of that feature (regardless of groups). If the expression values are log-transformed, subtraction is used instead of division.

The second optional preprocessing step is a feature selection. This step is performed in order to select relevant features. Feature selection is performed using one of four tests: Student's t-test, ANOVA, Wilcoxon-Mann-Withney test, or Kruskal-Wallis test. The test used depends on the number of groups and the parameter parametric. The parameter pAdj controls the method used to adjust p-values for multiple hypothesis testing. For a list of adjustment methods see p.adjust. Features with an adjusted p-value less than alpha are selected.

After these two optional steps, the signatures for each sample are computed. Selected features are ranked according to the expression values (or the fold-change, if computed). Than the first nTop and the last nBottom features are selected from the ranked list of features. Two data.frames are containing the signatures of up-regulated genes and down-regulated genes for each sample are produced and are contained in the returned object.

Consensus top and bottom signatures are computed for each group. The avreage rank for each gene is computed for each group. Features are then ranked according to the average rank in each group and the first nTop and the last nBottom genes are selected to form the consensus signatures of each group. Two data.frames containing the consensus signatures are produced and are contained in the returned object.

Gene signatures are used to compute an all-to-all distance matrix. The distance between two samples quantifies the degree of similarty between the signatures of the two samples. The default method used to compute the distance between two samples is based on GSEA. Specifically, the distance between two samples A and B is computed in three steps. First the enrichment score (ES) of the signaure of sample A against the whole expression profile of sample B, ES(A, B), is compted. ES(B, A) is also computed. Since a signature is composed of a top and a bottom part, the ES of a signature in a profile is computed as the average of the ES of the top and the bottom signatures. Then, the distance between two samples is computed as the average ES:

d(A,B)=(ES(A,B)+ES(B,A))/2

Finally, a rounded value of the minimum non-zero distance is subtracted from all values; the purpose of this transformation is to expand the dynamic range and increase the relative difference between distance values.

The ES employed by default is also known as the Kolmogorov-Smirnov running sum and is analogous to the ES used in the unweighted early version of GSEA. Alternatively, a user specified function can be used to compute the distance matrix, provided using the parameter distFun. This function should be of the form function(expressionData, nTop, nBottom), where expressionData is a data.frame of expression profiles and nTop and nBottom are the sizes of the signatures. This function should return a symmetric square matrix, with identical names on the rows and the columns, corresponding to the names of the samples in expressionData.

The distance matrix is included in the returned object and can be used to generate a graph of samples using scudoNetwork.

Note that we use the term distance loosely: from a mathematical point of view, our "distance" is actually a semimetric (it does not satisfy the triangle inequality).

Value

Object of class ScudoResults.

Author(s)

Matteo Ciciani [e-mail], Thomas Cantore [e-mail]

See Also

scudoTest, scudoNetwork, ScudoResults

Examples

# generate dummy dataset
exprData <- data.frame(a = 11:20, b = 16:25,
    c = rev(1:10), d = c(1:2, rev(3:10)))
rownames(exprData) <- letters[11:20]
grps <- as.factor(c("G1", "G1", "G2", "G2"))
nTop <- 2
nBottom <- 3

# run scudo
res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE,
    featureSel = FALSE)
show(res)

# examine top signatures and top consensus signatures
upSignatures(res)
consensusUpSignatures(res)

# examine distance matrix
distMatrix(res)

Matteo-Ciciani/rScudo documentation built on Jan. 25, 2024, 8:55 p.m.