An introduction to rScudo
2021-05-19
1 Introduction
This package implements in R the SCUDO rank-based signature identification
method1 Lauria M. Rank-based transcriptional signatures. Systems Biomedicine.
2013; 1(4):228-239.
Lauria M, Moyseos P, Priami
C. SCUDO: a tool for signature-based clustering of expression profiles. Nucleic
Acids Research. 2015; 43(W1):W188-92.. SCUDO (Signature-based Clustering for
Diagnostic Purposes) is a method for the analysis and classification of gene
expression profiles for diagnostic and classification purposes. The rScudo
package implements the very same algorithm that participated in the SBV IMPROVER
Diagnostic Signature Challenge, an open international competition designed to
assess and verify computational approaches for classifying clinical samples
based on gene expression. SCUDO earned second place overall in the competition,
and first in the Multiple Sclerosis sub-challenge, out of 54 submissions2 Tarca
AL, Lauria M, Unger M, Bilal E, Boue S, Kumar Dey K, Hoeng J, Koeppl H, Martin
F, Meyer P, et al. IMPROVER DSC Collaborators. Strengths and limitations of
microarray-based phenotype prediction: lessons learned from the IMPROVER
Diagnostic Signature Challenge. Bioinformatics. 2013; 29:2892–2899..
The method is based on the identification of sample-specific gene signatures and their subsequent analysis using a measure of signature-to-signature similarity. The computation of a similarity matrix is then used to draw a map of the signatures in the form of a graph, where each node corresponds to a sample and a connecting edge, if any, encodes the level of similarity between the connected nodes (short edge = high similarity; no edge = negligible similarity). The expected result is the emergence of a partitioning of the set of samples in separate and homogeneous clusters on the basis of signature similarity (clusters are also sometimes referred to as communities).
The package has been designed with the double purpose of facilitating experimentation on different aspects of the SCUDO approach to classification, and enabling performance comparisons with other methods. Given the novelty of the method, a lot of work remain to be done in order to fully optimize it, and to fully characterize its classification performance. For this purpose the package includes features that allow the user to implement his/her own signature similarity function, and/or clustering and classification methods. It also adds functions to implement steps that were previously performed manually, such as determining optimal signature length and computing classification performance indices, in order to facilitate the application and the evaluation of the method.
2 Method in brief
Starting from gene expression data, the functions scudoTrain and
scudoNetwork perform the basic SCUDO pipeline, which can be
summarized in 4 steps:
First, fold-changes are computed for each gene. Then, a feature selection step is performed. The user can specify whether to use a parametric or a non parametric test. The test used also depends on the number of groups present in the dataset. This step can be optionally skipped.
The subsequent operations include single sample gene ranking and the extraction of signatures formed by up-regulated and down-regulated genes. The length of the signatures are customizable. Consensus signtures are then computed, both for up- and down-regulated genes and for each group. The computation of consensus signatures is performed aggregating the ranks of the genes in each sample and ranking again the genes.
An all-to-all distance matrix is then computed using a distance similar to the GSEA3 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 2005; 102(43):15545-15550. (Gene Set Enrichment Analysis): the distance between two samples is computed as the mean of the enrichment scores (ES) of the signatures of each sample in the expression profile of the other sample. The distance function used is customizable.
Finally, a user-defined threshold N is used to generate a network of samples. The distance matrix is treated as an adjacency matrix, but only the distances that fall below the Nth quantile of distances are used to draw edges in the network. This is performed by the function
scudoNetwork. The network can then be displayed in R or using Cytoscape.
The function scudoTrain returns an object of class scudoResults,
which contains sample-specific gene signatures, consensus gene signatures for
each group and the sample distance matrix.
After the identification of a list of genes that can be used to partition the
samples in separated communities, the same procedure can be applied to a testing
dataset. The function scudoTest performs steps 2 and 3 on a testing
dataset, taking into account only the genes selected in the training phase.
Alteranatively, the function scudoClassify can be used to perform
supervised classification. This function takes as input a training set,
containing samples with known classification, and a testing set of samples with
unknown classification. For each sample in the testing set, the function
computes a network formed by all the samples in the training set and a single
sample from the training set. Then, classification scores are computed for each
sample in the testing set looking at the neighbors of that sample in the
network. See the documentation of the function for a detailed description of the
computation of the classification scores.
3 Example workflow
3.1 Data preparation
In this example we will use the ALL dataset, containing gene
expression data from T- and B-cells acute lymphoblastic leukemia patients. In
this first part, we are interested in distinguishing B-cells and T-cells
samples, based on gene expression profiles. We begin by loading relevant
libraries and subsetting the dataset, dividing it in a training and a testing
set, using the function createDataPartition from the package
caret.
library(rScudo)
library(ALL)
data(ALL)
bt <- as.factor(stringr::str_extract(pData(ALL)$BT, "^."))
set.seed(123)
inTrain <- caret::createDataPartition(bt, list = FALSE)
trainData <- ALL[, inTrain]
testData <- ALL[, -inTrain]3.2 Analysis of the training set
We start by analyzing the training set. We first run scudoTrain,
which returns an object of class ScudoResults. This function computes the
all-to-all distance matrix, which is a potentially computationally intensive
operation, however its implementation has been carefully optimized for speed. As
a result, the function can handle relatively large data sets; the execution of
the code below takes only about 3 seconds on a PC equipped with a Intel Core
i7-8700T 2.40GHz CPU and 16GB of RAM running Windows 10 Pro.
trainRes <- scudoTrain(trainData, groups = bt[inTrain], nTop = 100,
nBottom = 100, alpha = 0.1)
trainRes
#> Object of class ScudoResults
#> Result of scudoTrain
#>
#> Number of samples : 65
#> Number of groups : 2
#> B : 48 samples
#> T : 17 samples
#> upSignatures length : 100
#> downSignatures length : 100
#> Fold-changes : computed
#> grouped : No
#> Feature selection : performed
#> Test : Wilcoxon rank sum test
#> p-value cutoff : 0.1
#> p.adjust method : none
#> Selected features : 4286From this object we can extract the signatures for each sample and the consensus signatures for each group.
upSignatures(trainRes)[1:5,1:5]
#> 04007 04010 04016 06002 08012
#> 1 36638_at 33273_f_at 36575_at 38355_at 38604_at
#> 2 34362_at 33274_f_at 40511_at 37283_at 1857_at
#> 3 37006_at 38514_at 37623_at 40456_at 878_s_at
#> 4 1113_at 39318_at 547_s_at 41273_at 38355_at
#> 5 40367_at 35530_f_at 37187_at 2036_s_at 37921_at
consensusUpSignatures(trainRes)[1:5, ]
#> B T
#> 1 37039_at 38319_at
#> 2 35016_at 33238_at
#> 3 39839_at 38147_at
#> 4 38095_i_at 37078_at
#> 5 38096_f_at 2059_s_atThe function scudoNetwork can be used to generate a network of
samples from the object trainRes. This function returns an
igraph object. The parameter N controls the percentage of edges
to keep in the network. We can plot this network using the function
scudoPlot.
trainNet <- scudoNetwork(trainRes, N = 0.25)
scudoPlot(trainNet, vertex.label = NA)
You can also render the network in Cytoscape, using the function
scudoCytoscape. Note that Cytoscape has to be open when running this
function.
scudoCytoscape(trainNet)Since we obtained a very good separation of the two groups, we proceed to analyze the testing set.
3.3 Analysis of the testing set
We can use a ScudoResults object and the function scudoTest to
analyze the testing set. The feature selection is not performed in the testing
set. Instead, only the features selected in the training step are used in the
analysis of the testing set.
testRes <- scudoTest(trainRes, testData, bt[-inTrain], nTop = 100,
nBottom = 100)
testRes
#> Object of class ScudoResults
#> Result of scudoTest
#>
#> Number of samples : 63
#> Number of groups : 2
#> B : 47 samples
#> T : 16 samples
#> upSignatures length : 100
#> downSignatures length : 100
#> Fold-changes : computed
#> grouped : NoWe can generate a network of samples and plot it.
testNet <- scudoNetwork(testRes, N = 0.25)
scudoPlot(testNet, vertex.label = NA)
We can use a community clustering algorithm to identify clusters of samples. In
the following example we use the function cluster spinglass from
the package igraph to perform clustering of our network. In
Cytoscape we can perform a similar analysis using clustering functions from the
clusterMaker app.
testClust <- igraph::cluster_spinglass(testNet, spins = 2)
plot(testClust, testNet, vertex.label = NA)
3.3.1 Supervised classification
scudoClassify performs supervised classification of sample in a
testing set using a model built from samples in a training set. It uses a method
based on neighbors in the graph to assign a class label to each sample in the
testing set. We suggest to use the same N, nTop, nBottom and alpha that
were used in the training step.
classRes <- scudoClassify(trainData, testData, N = 0.25, nTop = 100,
nBottom = 100, trainGroups = bt[inTrain], alpha = 0.1)Classification performances can be explored using the
confusionMatrix function from caret.
caret::confusionMatrix(classRes$predicted, bt[-inTrain])
#> Confusion Matrix and Statistics
#>
#> Reference
#> Prediction B T
#> B 47 0
#> T 0 16
#>
#> Accuracy : 1
#> 95% CI : (0.9431, 1)
#> No Information Rate : 0.746
#> P-Value [Acc > NIR] : 9.632e-09
#>
#> Kappa : 1
#>
#> Mcnemar's Test P-Value : NA
#>
#> Sensitivity : 1.000
#> Specificity : 1.000
#> Pos Pred Value : 1.000
#> Neg Pred Value : 1.000
#> Prevalence : 0.746
#> Detection Rate : 0.746
#> Detection Prevalence : 0.746
#> Balanced Accuracy : 1.000
#>
#> 'Positive' Class : B
#> 3.4 Example of multigroup analysis
The analysis can also be performed on more than two groups. In this section, we try to predict the stage of B-cells ALL using gene expression data. We focus only on stages B1, B2 and B3, since they have a suitable sample size.
isB <- which(as.character(ALL$BT) %in% c("B1", "B2", "B3"))
ALLB <- ALL[, isB]
stage <- ALLB$BT[, drop = TRUE]
table(stage)
#> stage
#> B1 B2 B3
#> 19 36 23We divide the dataset in a training and a testing set and we apply
scudoTrain, identifying suitable parameter values. Then, we perform
supervised classification of the samples in the testing set using the function
scudoClassify.
inTrain <- as.vector(caret::createDataPartition(stage, p = 0.6, list = FALSE))
stageRes <- scudoTrain(ALLB[, inTrain], stage[inTrain], 100, 100, 0.01)
stageNet <- scudoNetwork(stageRes, 0.2)
scudoPlot(stageNet, vertex.label = NA)
classStage <- scudoClassify(ALLB[, inTrain], ALLB[, -inTrain], 0.25, 100, 100,
stage[inTrain], alpha = 0.01)
caret::confusionMatrix(classStage$predicted, stage[-inTrain])
#> Confusion Matrix and Statistics
#>
#> Reference
#> Prediction B1 B2 B3
#> B1 6 3 1
#> B2 1 10 1
#> B3 0 1 7
#>
#> Overall Statistics
#>
#> Accuracy : 0.7667
#> 95% CI : (0.5772, 0.9007)
#> No Information Rate : 0.4667
#> P-Value [Acc > NIR] : 0.0008038
#>
#> Kappa : 0.6441
#>
#> Mcnemar's Test P-Value : 0.5724067
#>
#> Statistics by Class:
#>
#> Class: B1 Class: B2 Class: B3
#> Sensitivity 0.8571 0.7143 0.7778
#> Specificity 0.8261 0.8750 0.9524
#> Pos Pred Value 0.6000 0.8333 0.8750
#> Neg Pred Value 0.9500 0.7778 0.9091
#> Prevalence 0.2333 0.4667 0.3000
#> Detection Rate 0.2000 0.3333 0.2333
#> Detection Prevalence 0.3333 0.4000 0.2667
#> Balanced Accuracy 0.8416 0.7946 0.86513.5 Increasing performance through parameter tuning
Parameters such as nTop and nBottom can be optimally tuned using techniques
such as cross-validation. The package caret offers a framework to
perform grid search for parameters tuning. Here we report an example of
cross-validation, in the context of the multigroup analysis previously
performed. Since feature selection represents a performance bottleneck, we
perform it before the cross-validation. Notice that we also transpose the
dataset, since functions in caret expect features on columns and
samples on rows.
trainData <- exprs(ALLB[, inTrain])
virtControl <- rowMeans(trainData)
trainDataNorm <- trainData / virtControl
pVals <- apply(trainDataNorm, 1, function(x) {
stats::kruskal.test(x, stage[inTrain])$p.value})
trainDataNorm <- t(trainDataNorm[pVals <= 0.01, ])We use the function scudoModel to generate a suitable input model
for train. scudoModel takes as input the parameter
values that have to be explored and generates all possible parameter
combinations. We then call the function trainControl to specify
control parameters for the training procedure and perform it using
train. Then we run scudoClassify on the testing set
using the best tuning parameters found by the cross-validation. We use
parallelization to speed up the cross-validation.
cl <- parallel::makePSOCKcluster(2)
doParallel::registerDoParallel(cl)
model <- scudoModel(nTop = (2:6)*20, nBottom = (2:6)*20, N = 0.25)
control <- caret::trainControl(method = "cv", number = 5,
summaryFunction = caret::multiClassSummary)
cvRes <- caret::train(x = trainDataNorm, y = stage[inTrain], method = model,
trControl = control)
parallel::stopCluster(cl)
classStage <- scudoClassify(ALLB[, inTrain], ALLB[, -inTrain], 0.25,
cvRes$bestTune$nTop, cvRes$bestTune$nBottom, stage[inTrain], alpha = 0.01)
caret::confusionMatrix(classStage$predicted, stage[-inTrain])
#> Confusion Matrix and Statistics
#>
#> Reference
#> Prediction B1 B2 B3
#> B1 6 4 1
#> B2 1 9 0
#> B3 0 1 8
#>
#> Overall Statistics
#>
#> Accuracy : 0.7667
#> 95% CI : (0.5772, 0.9007)
#> No Information Rate : 0.4667
#> P-Value [Acc > NIR] : 0.0008038
#>
#> Kappa : 0.6512
#>
#> Mcnemar's Test P-Value : 0.2838861
#>
#> Statistics by Class:
#>
#> Class: B1 Class: B2 Class: B3
#> Sensitivity 0.8571 0.6429 0.8889
#> Specificity 0.7826 0.9375 0.9524
#> Pos Pred Value 0.5455 0.9000 0.8889
#> Neg Pred Value 0.9474 0.7500 0.9524
#> Prevalence 0.2333 0.4667 0.3000
#> Detection Rate 0.2000 0.3000 0.2667
#> Detection Prevalence 0.3667 0.3333 0.3000
#> Balanced Accuracy 0.8199 0.7902 0.92064 Session info
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.2 LTS
#>
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#>
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#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] parallel stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] ALL_1.33.0 Biobase_2.52.0 BiocGenerics_0.38.0
#> [4] rScudo_1.8.0 BiocStyle_2.20.0
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