| Title: | Signature-based Clustering for Diagnostic Purposes |
|---|---|
| Description: | SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. |
| Authors: | Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] |
| Maintainer: | Matteo Ciciani <[email protected]> |
| License: | GPL-3 |
| Version: | 1.23.0 |
| Built: | 2024-11-27 05:12:39 UTC |
| Source: | https://github.com/bioc/rScudo |
SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two sample is computed using a method similar to GSEA. The graph of samples can the be used to perform community clustering or to perform supervised classification of samples in a testing set.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
scudoTrain, scudoNetwork,
scudoClassify
# To learn more about rScudo, start with the vignette: browseVignettes("rScudo")# To learn more about rScudo, start with the vignette: browseVignettes("rScudo")
Performs supervised classification of samples in a testing set using a network of samples generated by SCUDO during a training step.
scudoClassify(trainExpData, testExpData, N, nTop, nBottom, trainGroups, maxDist = 1, weighted = TRUE, complete = FALSE, beta = 1, alpha = 0.1, foldChange = TRUE, featureSel = TRUE, logTransformed = NULL, parametric = FALSE, pAdj = "none", distFun = NULL)scudoClassify(trainExpData, testExpData, N, nTop, nBottom, trainGroups, maxDist = 1, weighted = TRUE, complete = FALSE, beta = 1, alpha = 0.1, foldChange = TRUE, featureSel = TRUE, logTransformed = NULL, parametric = FALSE, pAdj = "none", distFun = NULL)
trainExpData |
either an
|
testExpData |
either an
|
N |
a number between 0 and 1, representing the fraction of the signature-to-signature distances that will be used to draw the graph |
nTop |
number of up-regulated features to include in the signatures |
nBottom |
number of down-regulated features to include in the signatures |
trainGroups |
factor containing group labels for each sample in
|
maxDist |
an integer. Only nodes with a distance from a testing node
less or equal to |
weighted |
logical, whether to consider the distances associated to the edges to compute the scores for the classification. For a description of the classification method, see Details below |
complete |
logical, whether to consider all the nodes in the training
set to perform the classification. If TRUE, the arguments |
beta |
a coefficient used to down-weight the influence of distant nodes on the classification outcome. For a description of the classification method, see Details below |
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 |
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 |
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 |
distFun |
the function used to compute the distance between two
samples. See Details of |
This function performs supervised classification of samples in a testing set,
using the networks similar to the one generated by scudoTrain
and scudoNetwork as a model.
For each sample S in the testing set, a new distance matrix is computed using
the expression profiles in the training set and the expression profile of S.
The distance matrix is computed as described in the Details of
scudoTrain.
If the argument complete is TRUE, the distance matrix is
converted in a similarity score matrix. Then, the similarity scores between S
and all the samples in the training set are aggregated according to groups.
The mean similarity scores are computed for each group and classification
scores are generated dividing them by their sum, obtaining values bewteen 0
and 1.
If the argument complete is FALSE, the distance matrix obtained
form S and the training set is used to generate a network of samples, using
the parameter N as a threshold for edge selection (see Details of
scudoNetwork for a more complete description). Then the
neighbors of S in the network are explored, up to a distance controlled by
the parameter maxDist. If the weighted parameter is
FALSE, the classification scores for each group are computed as the
number of edges connecting S or one of its neighbors to a node of that group.
The scores are than rescaled dividing them by their sum, in order to obtain
values between 0 and 1. If the weighted parameter is TRUE, the
classification scores for each group are computed as the sum of the
similarity scores associated to edges connecting S or one of its neighbors to
nodes of that group. The scores are than rescaled dividing them by their sum,
in order to obtain values between 0 and 1. The parameter beta can be
used to down-weight the contribution to the classification scores of edges
connecting nodes distant form S, both in the weighed and unweighted cases.
The predicted group for each sample is the one with the largest
classification score. Both predictions and classification scores are
returned. Note that if the argument complete is FALSE, the
classification socres for a sample may be all zero, which happens when the
correspoonding node is isolated in the network of samples. In this case the
predicted group is NA.
The tuning of the parameters can be performed automatically using the
train function form the package caret and the
function scudoModel.
A list containing a factor with the predicticted class for
each sample in testExpData and a data.frame of the classification
scores used to generate the predictions.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
expData <- data.frame(a = 1:10, b = 2:11, c = 10:1, d = 11:2, e = c(1:4, 10:5), f = c(7:10, 6:1), g = c(8:4, 1:3, 10, 9), h = c(6:10, 5:1), i = c(5:1, 6:10)) rownames(expData) <- letters[1:10] groups <- factor(c(1,1,1,2,2,2,1,1,1)) inTrain <- 1:5 # perform classification res <- scudoClassify(expData[, inTrain], expData[, -inTrain], 0.9, 3, 3, groups[inTrain], featureSel = FALSE) #explore predictions predictions <- res$predicted scores <- res$scoresexpData <- data.frame(a = 1:10, b = 2:11, c = 10:1, d = 11:2, e = c(1:4, 10:5), f = c(7:10, 6:1), g = c(8:4, 1:3, 10, 9), h = c(6:10, 5:1), i = c(5:1, 6:10)) rownames(expData) <- letters[1:10] groups <- factor(c(1,1,1,2,2,2,1,1,1)) inTrain <- 1:5 # perform classification res <- scudoClassify(expData[, inTrain], expData[, -inTrain], 0.9, 3, 3, groups[inTrain], featureSel = FALSE) #explore predictions predictions <- res$predicted scores <- res$scores
A wrapper to RCy3 function calls to plot the result of
scudoNetwork in Cytoscape 3. Cytoscape must be open before
running this function.
scudoCytoscape(graph, title = "Scudo Graph", collection = "SCUDO")scudoCytoscape(graph, title = "Scudo Graph", collection = "SCUDO")
graph |
object of class |
title |
the title of the network |
collection |
the name of the Cytoscape collection |
The network SUID (an integer).
Matteo Ciciani [email protected], Thomas Cantore [email protected]
# 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 scudoTrain and scudoNetwork res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) col <- c("#FF00FF", "#FF00FF", "#00FF00", "#00FF00") net <- scudoNetwork(res, N = 0.5, colors = col) # run scudoCytoscape (with Cytoscape open) ## Not run: scudoCytoscape(res, title = "scudoCytoscape output")# 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 scudoTrain and scudoNetwork res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) col <- c("#FF00FF", "#FF00FF", "#00FF00", "#00FF00") net <- scudoNetwork(res, N = 0.5, colors = col) # run scudoCytoscape (with Cytoscape open) ## Not run: scudoCytoscape(res, title = "scudoCytoscape output")
caret::train
This function generates a suitable input for the method argument of
the function train from the package caret, that
can be used to perform automatic parameter tuning (e.g. using
cross-validation).
scudoModel(nTop, nBottom, N, maxDist = 1, weighted = TRUE, complete = FALSE, beta = 1, distFun = NULL)scudoModel(nTop, nBottom, N, maxDist = 1, weighted = TRUE, complete = FALSE, beta = 1, distFun = NULL)
nTop |
number of up-regulated features to include in the signatures |
nBottom |
number of down-regulated features to include in the signatures |
N |
a number between 0 and 1, representing the fraction of the signature-to-signature distances that will be used to draw the graph |
maxDist |
an integer. Only nodes with a distance from a testing node
less or equal to |
weighted |
logical, whether to consider the distances associated to the edges to compute the scores for the classification |
complete |
logical, whether to consider all the nodes in the training
set to perform the classification. If TRUE, the arguments |
beta |
a coefficient used to down-weight the influence of distant nodes on the classification outcome |
distFun |
the function used to compute the distance between two samples. See Details for the specification of this function |
This function can be used in conjunction with the functions in the package
caret to tune the parameters of scudoClassify. The input
of this function are vector of parameter values that the tuning procedure
should explore. All possible combination of parameter values are explored by
default. The user can change this using the search argument of the
trainControl function.
The output of this function is a list that represens a classification model
using scudoClassify and that can be used as input for the
method argument of the function train.
A named list
Matteo Ciciani [email protected], Thomas Cantore [email protected]
scudoClassify, train,
trainControl
# Generate example dataset expData <- data.frame(a = 1:10, b = 2:11, c = 10:1, d = 11:2, e = c(1:4, 10:5), f = c(7:10, 6:1), g = c(8:4, 1:3, 10, 9), h = c(6:10, 5:1), i = c(5:1, 6:10)) rownames(expData) <- letters[1:10] groups <- factor(c(1,1,1,2,2,2,1,1,1)) # Run bootstrap. Notice that the dataset is transposed ctrl <- caret::trainControl(method = "boot", number = 5) model <- scudoModel(nTop = 3:5, nBottom = 3:5, N = 0.5, complete = TRUE) set.seed(1) bootRes <- caret::train(x = t(expData), y = groups, method = model, trControl = ctrl)# Generate example dataset expData <- data.frame(a = 1:10, b = 2:11, c = 10:1, d = 11:2, e = c(1:4, 10:5), f = c(7:10, 6:1), g = c(8:4, 1:3, 10, 9), h = c(6:10, 5:1), i = c(5:1, 6:10)) rownames(expData) <- letters[1:10] groups <- factor(c(1,1,1,2,2,2,1,1,1)) # Run bootstrap. Notice that the dataset is transposed ctrl <- caret::trainControl(method = "boot", number = 5) model <- scudoModel(nTop = 3:5, nBottom = 3:5, N = 0.5, complete = TRUE) set.seed(1) bootRes <- caret::train(x = t(expData), y = groups, method = model, trControl = ctrl)
A function to create an igraph
object from a ScudoResults object. In the graph, nodes
are samples and edges quantify the similarity between the nodes.
scudoNetwork(object, N, colors = character())scudoNetwork(object, N, colors = character())
object |
a |
N |
a number between 0 and 1, representing the fraction of the signature-to-signature distances that will be used to draw the graph |
colors |
a character vector of hexadecimal RGB color codes used to color
the nodes of the graph. |
This function uses the distance matrix in the
ScudoResults object to generate an
igraph object, representing a graph
where nodes are samples and edges quantify the similarity between the
signatures of pairs of nodes.
The distance matrix in object is used to generate an unweighted
adjacency matrix, that is then used to generate the graph. The sample
quantile of probability N, computed from all the non-zero distances in the
distance matrix, is used as a threshold to generate the adjacency matrix: all
the distances larger than this quantile are mapped to 0, all the distances
smaller than this quantile are mapped to 1 (with the exception of the
distances of any node from itself, which are equal to 0).
Distances are set as attributes of the edges. Use
igraph::E(igraphObject)$distance to retrieve them, where
igraphObject is the result of scudoNetwork.
The color parameter controls the color of the nodes. It must be a
vector of hexadecimal RGB color codes (like "#FFFFFF"), with length
equal to
the number of samples in object. By default, a different color is
assigned to each group. If no group is specified in object, all nodes
are set to the same color. A vector of node colors can be accessed
with igraph::V(igraphObject)$color. Use
igraph::V(igraphObject)$group to access the group label of each node
(it returns NULL if no group is specified in object).
An object of class igraph.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
scudoCytoscape, ScudoResults,
igraph
# generate dummy dataset and run scudo exprData <- data.frame(a = 11:20, b = 16:25, c = rev(1:10), d = c(1:2, rev(3:10))) rownames(exprData) <- letters[11:20] grps <- as.factor(c("G1", "G1", "G2", "G2")) nTop <- 2 nBottom <- 3 res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) # generate network col <- c("#FF0000", "#FF0000", "#0000FF", "#0000FF") net <- scudoNetwork(res, N = 0.5, colors = col) # retrieve node colors and groups nodes <- igraph::V(net) colors <- nodes$color groups <- nodes$group # retrieve distances from edges edges <- igraph::E(net) dist <- edges$distance # plot the network scudoPlot(net)# generate dummy dataset and run scudo 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 res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) # generate network col <- c("#FF0000", "#FF0000", "#0000FF", "#0000FF") net <- scudoNetwork(res, N = 0.5, colors = col) # retrieve node colors and groups nodes <- igraph::V(net) colors <- nodes$color groups <- nodes$group # retrieve distances from edges edges <- igraph::E(net) dist <- edges$distance # plot the network scudoPlot(net)
A wrapper to plot.igraph and
legend. Can be used to plot the result of
scudoNetwork with a color legend.
scudoPlot(net, x = "bottomright", y = NULL, ...)scudoPlot(net, x = "bottomright", y = NULL, ...)
net |
an |
x, y
|
the x and y coordinates to be used to position the legend. They
can be specified by keyword or in any way which is accepted by
|
... |
arguments to be passed to |
Returns NULL, invisibly.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
# generate dummy dataset, run scudoTrain and scudoNetwork 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 res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) net <- scudoNetwork(res, N = 0.5) # Plot with scudoPlot scudoPlot(net)# generate dummy dataset, run scudoTrain and scudoNetwork 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 res <- scudoTrain(exprData, grps, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) net <- scudoNetwork(res, N = 0.5) # Plot with scudoPlot scudoPlot(net)
This is an S4 class that represents the output of the functions
scudoTrain and scudoTest.
This class provides a structure to represent the results of scudoTrain
and scudoTest. It contains the distance matrix and the gene signatures
generated by the SCUDO analysis. It is possible, although not recommended, to
manually create instances of this class (see Examples below).
distMatrixa symmetric matrix with non-negative numeric elements
upSignaturesa data.frame with the same colnames as distMatrix, representing the up-regualted features in each sample
downSignaturesa data.frame with the same colnames as distMatrix, representing the down-regualted features in each sample
groupsAnnotationa factor that represents the groups used for the computeFC and the feature selection
consensusUpSignaturesa data.frame that contains the consensus signatures of up-regulated features for each group
consensusDownSignaturesa data.frame that contains the consensus signatures of dowm-regulated features for each group
selectedFeaturesa character vector of selected features. If the feature selection was not performed, it contains every feature present in the input of the scudo functions
scudoParamsa list of the parameters used to run the function that created the instance of the class
distMatrixsignature(object = "ScudoResults"):
a method for obtaining the distance matrix.
upSignaturessignature(object = "ScudoResults"): a
method for obtaining the signature of up-regualted features in each
sample.
downSignaturessignature(object =
"ScudoResults"): a method for obtaining the signature of down-regulated
features in each sample.
groupsAnnotationsignature(object = "ScudoResults"):
a method for obtaining the groups used for
computeFC and feature selection.
consensusUpSignaturessignature(object =
"ScudoResults"): a method for obtaining the consensus signatures of
up-regualted features in each group.
consensusDownSignaturessignature(object =
"ScudoResults"): a method for obtaining the consensus signatures of
down-regulated features in each group.
selectedFeaturessignature(object = "ScudoResults"):
a method for obtaining the names of the features seleted. If no feature
selection was performed, the names of every feature are returned.
scudoParamssignature(object = "ScudoResults"): a method
for obtaining the parameters that were used to generate the result.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
# manually generate instance of ScudoResults class m <- matrix(1, ncol = 4, nrow = 4) diag(m) <- 0 rownames(m) <- colnames(m) <- letters[1:4] SigUp <- data.frame(a = letters[1:5], b = letters[6:10], c = letters[11:15], d = letters[16:20], stringsAsFactors = FALSE) SigDown <- data.frame(a = letters[1:10], b = letters[11:20], c = letters[1:10], d = letters[11:20], stringsAsFactors = FALSE) groups <- as.factor(c("G1", "G1", "G2", "G2")) ConsUp <- data.frame(G1 = letters[11:15], G2 = letters[21:25], stringsAsFactors = FALSE) ConsDown <- data.frame(G1 = letters[16:25], G2 = letters[1:10], stringsAsFactors = FALSE) Feats <- letters[1:20] Pars <- list() scudoR <- ScudoResults(distMatrix = m, upSignatures = SigUp, downSignatures = SigDown, groupsAnnotation = groups, consensusUpSignatures = ConsUp, consensusDownSignatures = ConsDown, selectedFeatures = Feats, scudoParams = Pars)# manually generate instance of ScudoResults class m <- matrix(1, ncol = 4, nrow = 4) diag(m) <- 0 rownames(m) <- colnames(m) <- letters[1:4] SigUp <- data.frame(a = letters[1:5], b = letters[6:10], c = letters[11:15], d = letters[16:20], stringsAsFactors = FALSE) SigDown <- data.frame(a = letters[1:10], b = letters[11:20], c = letters[1:10], d = letters[11:20], stringsAsFactors = FALSE) groups <- as.factor(c("G1", "G1", "G2", "G2")) ConsUp <- data.frame(G1 = letters[11:15], G2 = letters[21:25], stringsAsFactors = FALSE) ConsDown <- data.frame(G1 = letters[16:25], G2 = letters[1:10], stringsAsFactors = FALSE) Feats <- letters[1:20] Pars <- list() scudoR <- ScudoResults(distMatrix = m, upSignatures = SigUp, downSignatures = SigDown, groupsAnnotation = groups, consensusUpSignatures = ConsUp, consensusDownSignatures = ConsDown, selectedFeatures = Feats, scudoParams = Pars)
A function to perform the SCUDO analysis on test data, given an object of
class ScudoResults used as training model.
scudoTest(trainScudoRes, testExpData, testGroups = NULL, nTop = NULL, nBottom = NULL, foldChange = TRUE, groupedFoldChange = FALSE, logTransformed = NULL, distFun = NULL)scudoTest(trainScudoRes, testExpData, testGroups = NULL, nTop = NULL, nBottom = NULL, foldChange = TRUE, groupedFoldChange = FALSE, logTransformed = NULL, distFun = NULL)
trainScudoRes |
an object of class |
testExpData |
either an
|
testGroups |
factor containing group labels for each sample in
|
nTop |
number of up-regulated features to include in the signatures. If
NULL, it defaults to the value present in |
nBottom |
number of down-regulated features to include in the
signatures. If NULL, it defaults to the value present in |
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 |
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 |
distFun |
the function used to compute the distance between two
samples. See Details of |
Given an object of class ScudoResults and a set of
expression profiles with unknown classification, scudoTest performs an
analysis similar to scudoTrain, computing a list of signatures
composed of genes over- and under-expressed in each sample, consensus
signatures for each group and a distance matrix that quantifies the
similarity between the signatures of pairs of samples.
scudoTest differs from scudoTrain in the feature selection
step: only the features present in the ScudoResults object taken as
input are considered for the follwing steps. The computation of fold-changes,
the identification of gene signatures and the computation of the distance
matrix are performed as described in the Details of scudoTrain.
If the classification of samples in the testing dataset is provided, it is only used for annotation purposes.
Object of class ScudoResults.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
scudoTrain, scudoNetwork,
ScudoResults, scudoClassify
# generate dummy train dataset exprDataTrain <- data.frame(a = 11:20, b = 16:25, c = rev(1:10), d = c(1:2, rev(3:10))) exprDataTest <- data.frame(e = 1:10, f = 11:20, g = rev(11:20), h = c(1:2, rev(3:10))) rownames(exprDataTrain) <- rownames(exprDataTest) <- letters[11:20] grpsTrain <- as.factor(c("G1", "G1", "G2", "G2")) nTop <- 2 nBottom <- 3 # run scudo res <- scudoTrain(exprDataTrain, grpsTrain, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) show(res) # run scudoTest testRes <- scudoTest(res, exprDataTest, foldChange = FALSE) show(testRes)# generate dummy train dataset exprDataTrain <- data.frame(a = 11:20, b = 16:25, c = rev(1:10), d = c(1:2, rev(3:10))) exprDataTest <- data.frame(e = 1:10, f = 11:20, g = rev(11:20), h = c(1:2, rev(3:10))) rownames(exprDataTrain) <- rownames(exprDataTest) <- letters[11:20] grpsTrain <- as.factor(c("G1", "G1", "G2", "G2")) nTop <- 2 nBottom <- 3 # run scudo res <- scudoTrain(exprDataTrain, grpsTrain, nTop, nBottom, foldChange = FALSE, featureSel = FALSE) show(res) # run scudoTest testRes <- scudoTest(res, exprDataTest, foldChange = FALSE) show(testRes)
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.
scudoTrain(expressionData, groups, nTop, nBottom, alpha = 0.1, foldChange = TRUE, groupedFoldChange = FALSE, featureSel = TRUE, logTransformed = NULL, parametric = FALSE, pAdj = "none", distFun = NULL)scudoTrain(expressionData, groups, nTop, nBottom, alpha = 0.1, foldChange = TRUE, groupedFoldChange = FALSE, featureSel = TRUE, logTransformed = NULL, parametric = FALSE, pAdj = "none", distFun = NULL)
expressionData |
either an
|
groups |
factor containing group labels for each sample in
|
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 |
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 |
distFun |
the function used to compute the distance between two samples. See Details for the specification of this function |
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:
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).
Object of class ScudoResults.
Matteo Ciciani [email protected], Thomas Cantore [email protected]
scudoTest, scudoNetwork,
ScudoResults
# 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)# 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)