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**|**BMC Bioinformatics**|**v.12; 2011**|**PMC3166941

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- Results and Discussion
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BMC Bioinformatics. 2011; 12: 312.

Published online 2011 July 29. doi: 10.1186/1471-2105-12-312

PMCID: PMC3166941

Hui Wang: ude.drofnats@iugnawh; Mark J van der Laan: ude.yelekreb@naal

Received 2011 June 2; Accepted 2011 July 29.

Copyright ©2011 Wang and van der Laan; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been cited by other articles in PMC.

When a large number of candidate variables are present, a dimension reduction procedure is usually conducted to reduce the variable space before the subsequent analysis is carried out. The goal of dimension reduction is to find a list of candidate genes with a more operable length ideally including all the relevant genes. Leaving many uninformative genes in the analysis can lead to biased estimates and reduced power. Therefore, dimension reduction is often considered a necessary predecessor of the analysis because it can not only reduce the cost of handling numerous variables, but also has the potential to improve the performance of the downstream analysis algorithms.

We propose a TMLE-VIM dimension reduction procedure based on the variable importance measurement (VIM) in the frame work of targeted maximum likelihood estimation (TMLE). TMLE is an extension of maximum likelihood estimation targeting the parameter of interest. TMLE-VIM is a two-stage procedure. The first stage resorts to a machine learning algorithm, and the second step improves the first stage estimation with respect to the parameter of interest.

We demonstrate with simulations and data analyses that our approach not only enjoys the prediction power of machine learning algorithms, but also accounts for the correlation structures among variables and therefore produces better variable rankings. When utilized in dimension reduction, TMLE-VIM can help to obtain the shortest possible list with the most truly associated variables.

Gene expression microarray data are typically characterized by large quantities of variables with unknown correlation structures [1,2]. This high dimensionality has presented us challenges in analyzing the data, especially when correlations among variables are complex. Including many variables in standard statistical analyses can easily cause problems such as singularity and overfitting, and sometimes is not even doable. To manage this problem, the dimensionality of the data will often be reduced in the first step. There are multiple ways to achieve this goal. One is to select a subset of genes based on certain criteria such that this subset of genes is believed to best predict the outcome. This gene selection strategy is typically based on some univariate measurement related to the outcome, such as t-test and rank test [3,4]. Another strategy is to use a weighted combination of genes of lower dimension to represent the total variation of the data. Representative approaches are principle component analysis (PCA) [5] and partial least squares (SLR) [6-9]. Machine learning algorithms such as LASSO [10,11] and Random Forest [12] have embedded capacity to select variables while simultaneously making predictions, and can be used to accommodate high dimensional microarray data.

As always, there is no one-size-fits-all solution to this problem, and one often needs to resort to a mix-and-match strategy. The univariate-measurement based gene selection is a very popular approach in the field. It is fast and scales easily to the dimension of the data. The output is usually stable and easy to understand, and fulfills the objectives of the biologists to directly pursue interesting findings. However, it often relies on over-simplified models. For instance, the univariate analysis evaluates every gene in isolation of others, with the unrealistic assumption of independence among genes. As a result, it carries a lot of noise and the selected genes are often highly correlated, which themselves create problems in subsequent analysis. Also, due to the practical limit of the size of the gene subset, real informative genes with weaker signals will be left out. In contrast, PCA/PLS constructs a few gene components as linear combinations of all genes in a dataset. This "Super Gene" approach assumes that the majority of the variation in the dataset can be explained by a small number of underlying variables. One then uses these gene components to predict the outcome. These approaches can better handle the dependent structure of genes and their performances are quite acceptable [13]. But it is harder to interpret gene components biologically, and to assess the effect of individual genes one needs to look at the weight coefficients of the linear combination. Machine learning algorithms are very attractive variable selection tools to deal with large quantities of genes. They are prediction algorithms with embedded abilities to select gene subsets. However, whether or not a gene is chosen by a learning algorithm may not be the best measurement of its importance. Machine learning algorithms are constructed to achieve an optimal prediction accuracy, which often overlooks the importance of each variable. Consequently, small changes in data or tuning parameters may result in big changes in variable rankings and the the selected gene subsets are instable. For example, Random Forest, a tree-based non-parametric method, has a variable importance measurement that greatly contributes to its popularity. This measurement is sensitive to the parameter choices of trees in the presence of high correlations among variables, because different sets of variables can produce nearly unchanged prediction accuracy [14,15]. Another example is LASSO -- one of the most popular regularization algorithms. Assuming a sparse signal, LASSO handles the high dimensionality problem by shrinking the coefficients of most variables towards zero [16]. A recent implementation of LASSO is in the GLMNET R package [17]. The package uses a coordinate descent algorithm and can finish an analysis of 20,000 variables within a few seconds. To us, its result is somewhat sensitive to the choice of the penalizing parameter *λ*. Different *λ*s may result in gene subsets with little overlapping. In the mean time, variable importance measurements are not readily available in LASSO. One can simply rank genes by their coefficients, but this can be quite subtle. Although permutation tests may be used to derive p-values, how to perform the permutation is a tricky matter due to selection of tuning parameters. For small p-values, it is still computationally infeasible. In this paper, inspired by concepts of counterfactual effects from the causal inference literature, we propose a targeted variable importance measurement [18,19] to rank genes and reduce the dimensionality of the dataset. Counterfactuals are usually defined in the context of treatment to disease. It is the outcome a patient would have had a treatment been assigned differently, with everything else held the same. Hence counterfactuals are "counter"-fact and apparently impossible to be observed. But it can be estimated statistically. Suppose that we have an outcome *Y*, a binary treatment *A*, and the confounding variables *W *of *A*, and we have worked out correctly an estimate of the conditional expectation of *Y *given *A *and *W*. A common way to estimate the counterfactual effect of *A *is to compute the difference between the and the for every observation and then average over all observations, referred to as the G-computation method [20]. Although counterfactuals may not be completely relevant to gene microarray data, thinking about the data in this way is very helpful for us to assess the importance of a gene. Our VIM definition uses the concepts of counterfactuals and the estimation framework is built on the methodology of targeted maximum likelihood estimation (TMLE) [21]. By tailoring this recently developed technique specifically to gene expression data, we hope to introduce to the community an alternative strategy to carry out gene selection in addition to current methods. Our approach takes the advantage of prediction power of learning algorithms while targeting at the individual importance of each variable. Its mathematical property has been studied in [22], and we will focus on its application. In brief, our approach consists of two-stages. In the first stage, we predict the outcome given all genes. In the second stage, we improve the first stage by modeling the mechanism between an individual gene and its confounding variables. Both stages can be very flexible ranging from using univariate analysis to refined learning algorithms. When machine learning algorithms are used, we have the flexibility to determine how to make predictions without restricting ourselves to explicit models and distributions. In the meanwhile, as in the case of the univariate analysis, we return to a simple and well interpretable measure of the importance of each gene. This importance measurement is derived in the presence of the confounding variables of a gene, and hence can help to exploit the redundant information among correlated genes. It is generally also more stable than the variable importance produced by machine learning algorithms. In addition, our approach provides a simple way for statistical inference based on asymptotic theories, and is well suited for the exploratory analysis of microarray data.

Suppose the observed data are i.i.d. *O _{i }*= (

Consider the semiparametric regression model:

where *f*(*W*) is a function of *W*. With this parameterization, we have Ψ(*a*) = *βa*. We can then view *β *as an index of the VIM of *A*. In the above model, the only assumption we make is the linearity of *A*. The definition of the VIM of *A *is closely related to the definition of the counterfactual effect in causal inference [23]. Although *β *can not be directly interpreted as an causal effect without proper assumptions [19], it serves well as a surrogate of the magnitude of the causal relationship between the outcome and a gene. The motivation of this parameterization is that by selecting more causally related genes, the resulting prediction function will be better generalized to new experiments with the same causal relation between the outcome *Y *and *A*, but a different joint distribution of *W*. If in a next experiment, the technology or the sampling population is somewhat different, but the causal mechanism is still the same, then a prediction function that uses the correlates of the true causal variables will perform poorly while a prediction function using the true causal variables will still perform nicely. This idea will be illustrated in our simulations.

Our goal is to estimate *β*. In [22], this estimation problem was addressed in the framework of targeted maximum likelihood estimation (TMLE). TMLE is an estimating equation and efficient estimation theory based methodology [24], and is particularly useful when it comes to semiparametric models. Estimators from the traditional method such as MLE perform well for parametric models, however, they are generally biased relative to their variances especially when the model space is large. This is because the MLE focuses on doing a good job on the estimation of the whole density rather than on the parameter itself. TMLE is designed to achieve an optimal trade-off between the bias and the variance of the estimator. It uses an MLE framework, but instead of estimating the overall density, TMLE targets on the parameter of interest and produces estimators minimally affected by changes of the nuisance parameters in a model. In Additional File 1 we provide a brief overview of this methodology with a demo simulation example. The formal mathematical formulation of TMLE can be found in the original paper by van der Laan and Rubin [21]. The implementation of TMLE to estimate *β *is fairly simple and consists of two stages. First, we estimate *E*(*Y *|*A*, *W*) without any parametric restriction. We then regress the residual of *Y *and *E*(*Y *|*A *= 0, *W*) onto *βA *to conform with our semiparametric regression model. This will yield an initial estimator of *β *and fitted values of *E*(*Y *|*A*, *W*), denoted by and the . In the second stage, we update these initial estimates in a direction targeted at *β*. This involves regressing the residuals of *Y *and the fitted on the clever covariate *A - E*(*A*|*W*). The *E*(*A*|*W*) evaluates the confounding of *A *with *W*, and we name it the "gene confounding mechanism". It needs to be estimated if unknown. Let us denote the coefficient before the clever covariate as *ε*. The updated TMLE estimate of *β *is , where *ε _{n }*is the estimated value of

1. Obtain the initial estimator and . Use your favorite algorithm here, for example, linear regression, LASSO, Random Forest, etc.

2. Obtain the *g _{n}*(

3. Compute the "clever covariate":

4. Fit regression .

5. Update the initial estimate with

and update the initial fitted values with

6. Compute the variance estimate for according to its efficient influence curve:

where *i *indexes the *i*-th observation.

7. Construct the test statistic:

*T*(*A*) follows the standard Gaussian distribution under the null hypothesis *β *= 0 when the sample size *n *goes to infinity.

The TMLE estimator is a consistent estimator of *β *when either the or the *g _{n}*(

In the application to dimension reduction, for each variable in the dataset, we compute a TMLE-VIM p-value. We then reduce our variable space based on these p-values. There are two notions. First, in principle, a separate initial estimator should be fitted for every gene *A *by forcing *A *as a term in the algorithm used. This can become quite time consuming. To solve the problem, instead of estimating *E*(*Y *|*A*, *W*) for each *A*, we obtain a grand estimate *G _{n}*(

We performed two sets of simulations. The first set of simulations investigates how TMLE-VIM responds to changes in the number of confounding variables, the correlation level among variables, and the noise levels. The second set studies the TMLE-VIM with more complex correlation structures and model misspecification. The performance of the dimension reduction procedure was primarily evaluated by the achieved prediction accuracy using a prediction algorithm on the reduced sets of variables, illustrated in the following analysis flow:

Two prediction algorithms, LASSO and D/S/A (Deletion/Substitution/Addition) [25], were used. D/S/A searches through the variable space and selects the best subset of covariates by minimizing the cross validated residual sum of squares. In our simulations, LASSO and D/S/A predictions are often similar. We used D/S/A in simulation I as it provides convenience to count what variables are included in the prediction model. LASSO was used in simulation II for its faster speed. We also used multivariate linear regression (MVR) as a comparison to machine learning algorithms when applicable.

In simulation I, we varied the number of non-causal variables (*W*), the correlation coefficient *ρ *among variables, and the noise level to see how TMLE-VIM responds to them. For each simulated observation *O _{i }*= (

where *j *indexes the *j*-th *A*, and *e _{i }*is a normal error with mean 0 and variance . Each

Simulations were run for combinations of:

• *m *= (10, 20) corresponding to *m _{w }*= (250, 500);

• *σ _{e }*= (1, 5, 10);

• and *ρ *= (0.1, 0.3, 0.5, 0.7, 0.9).

For each combination, we simulated a training set of 500 data points and a testing set of 5000 data points. The training set was used to obtain the prediction model while the testing set was used to calculate the L2 risk. We also calculated a cross-examined L2 risk using a testing set with a *ρ *other than that of the training set. This is to demonstrate that by identifying more causally related variables, TMLE-VIM is robust to the change of the joint distribution among the covariates *A*s and *W*s. In specific, for each prediction model obtained from a training set, we calculated the *L*2 risk on the testing set generated with *ρ *= 0.1 regardless of what *ρ *was used to generate the training set. As a benchmark, we also used univariate regression in parallel with TMLE-VIM to reduce the dimensionality of the dataset, denoted with UR-VIM. Once the variable importance was calculated, we cut short the variable list using a p-value threshold 0.05. Each combination was replicated 10 times and results took the average.

TMLE-VIM used LASSO to obtain both the initial estimator and the gene confounding mechanism estimator *g _{n}*(

• *R _{r }*= (UR-VIM risk

•*R _{A }*= TMLE-VIM

•*R _{W }*= TMLE-VIM

•*RR*_{DSA }= TMLE-VIM *P _{A}/*UR-VIM

The *R _{r }*was calculated on two different testing sets. One is the testing set generated with the same

• The proportion of the risk reduction (*R _{r}*) of the TMLE-VIM relative to the UR-VIM is typically more than 20% for the MVR prediction and 10% for the D/S/A prediction. In some cases, the risk reduction of the MVR can be very significant. For example, when

• Most *R _{A }*values are slightly higher than 1 while the

• As to the number of *A*s that are finally made into the D/S/A prediction model, the TMLE-VIM in most cases displays a slight advantage over the UR-VIM. A closer look reveals that the variables included in the D/S/A model only differs by one or two between the TMLE-VIM and the UR-VIM. But the prediction risk has a measurable difference. This probably implies that every single variable counts in making good predictions in these simulations.

• When *ρ *= 0.9, the situation seems to be losing its track. The TMLE-VIM did worse than the UR-VIM in terms of correctly identified variables as well as the prediction risk of the testing set (a). Considering the high correlations among variables, this could possibly be attributed to the overfitting in the *g _{n}*(

Figure Figure11 presents a graphical representation of a typical example in simulation I with (*σ _{e }*= 5,

Simulation II examines the TMLE-VIM on larger-scale datasets with much more complex correlation structures. The simulation consists of 500 samples and 1000 variables. We used a correlation matrix derived from the top 800 genes in a real dataset published in [26]. For these genes, the median absolute correlation coefficient was centered at 0.26, the 1st/3rd quartile being 0.16/0.37, and the maximum as high as 0.9977. Hence, simulation II tried to mimic the correlation structure in this real data set. The outcome *Y *was generated from two different models using 20 *A*s. One is a linear model, and the other is polynomial.

Details of this simulation is provided in the Additional File 1. A test dataset of 5000 points were simulated to assess the L2 prediction risk. We repeated the simulation for 10 times and results took the average. In TMLE-VIM, we tried two different initial estimators. One is the univariate regression as simple as *Y ~ A*, and the other is the LASSO estimator. LASSO was also used to get the *g _{n}*(

The numbers in Table Table22 were based on candidate lists that were cut short with a p-value threshold of 0.05. In Table Table3,3, we provide the results based on the top 100 ranked genes. The numbers of UR-VIM and the are less satisfying than those in Table Table2,2, while the achieved comparable results. This suggests that the p-values of *A*s are among the smallest ones, and shortening the length of the list does not affect the final result. Regardless of the weakened results, The still displays a non-ignorable advantage over the UR-VIM with respect to the prediction accuracy, while the number of correctly identified *A*s is slightly smaller than that of the UR-VIM. We then looked at the correlation matrix among the top 100 selected genes, and it occurs that the correlation among them is the least for the , the most for the UR-VIM, and the lies in between. This could explain why the does a better job in prediction regardless of less *A*s.

We also carried out the TMLE-VIM(*λ*) procedure with LASSO as the initial estimator, allowing the data select the correlation cutoff for variables to be adjusted in the *g _{n}*(

Breast cancer patients are often put on chemotherapy after the surgical removal of the tumor. However not all patients will respond to chemotherapy, and proper guidance for selecting the optimal regimen is needed. Gene expression data have the potential for such predictions, as studied in [26]. The dataset from [26] contains the gene expression profiling on 22283 genes for 133 breast cancer patients. The outcome is the pathological complete response (pCR). This is a binary response associated with long-term cancer free survival. There are also 13 clinical variables collected in the dataset including the ER (estrogen receptor) status, which is a very significant clinical indicator for chemotherapy response.

The goal of the study is to select a set of genes that best predict the clinical response pCR. The first step is to reduce the number of genes worth of consideration, and we applied both UR-VIM and TMLE-VIM (with *Q*^{(0) }= *UR *and *Q*^{(0) }= LASSO) for this purpose. For the TMLE-VIM(*Q*^{(0) }= LASSO), the was estimated by LASSO using the top 5000 ranked genes. We then took all the genes with the FDR-adjusted p-values less than 0.005 [27], as suggested in the original paper, and upon them we built a predictor using the Random Forest (tuning parameters mtry = number of variables/3, ntree = 3000 and nodesize = 1). The clinical covariates were treated in the same way as genes. To prevent the algorithm from breaking down, we only adjusted for the confounder with correlation coefficients less than 0.7 with *A *in the *g _{n}*(

Analysis results are tabulated in Table Table4.4. The UR-VIM produced a candidate list of 326 genes and one clinical variable the "ER status", while the list of the TMLE-VIM(*Q*^{(0) }= UR) consists of 660 genes and TMLE-VIM(*Q*^{(0) }= LASSO) 818 genes. The TMLE-VIM identified many more genes than the UR-VIM. Among all the identified genes, 429 overlap between the and , 15 overlap between the UR-VIM and TMLE-VIM(*Q*^{(0) }= UR), 10 overlap between the UR-VIM and TMLE-VIM(*Q*^{(0) }= LASSO), and only 4 genes are shared among all three (please see Figure Figure2).2). The TMLE-VIM appeared to have selected almost a different set of genes than the UR-VIM.

The TMLE-VIM(*Q*^{(0) }= UR) and the TMLE-VIM(*Q*^{(0) }= LASSO) results are quite similar to each other regardless of the adequate difference between the initial estimators. It seems the modeling of the *g _{n}*(

In summary, the UR-VIM and RF-VIM seemed to have identified genes that are strong predictors of the clinical variable ER status. The ER status is a strong indicator of the outcome pCR. Hence, the final prediction accuracy still seems quite good. The TMLE-VIM has identified a list of genes of which a small proportion is strong predictors of ER status and others are not associated with the ER status. Its prediction accuracy is slightly better than that of the UR-VIM and RF-VIM.

We have shown in this paper with extensive simulations that the TMLE based variable importance measurement can be incorporated into a dimension reduction procedure to improve the quality of the list of the candidate variables. It requires an initial estimator and a gene confounding mechanism estimate *g _{n}*(

A popular dimension reduction approach is the principle component analysis (PCA). The PCA computation does not involve the outcome, and so it could be less powerful when prediction is the primary goal. Its output is a linear combination of all the genes. Though not a gene selection approach, we still carried it out on our simulation I data as an interesting comparison to our approach. PCA demonstrates an intermediate performance with respect to the UR-VIM and the TMLE-VIM on small p-value cutoffs. This means a few top components carry all the prediction power. When the p-value cutoff is increased, and more components enter the candidate list, its results became quite unsatisfying. When the correlation structure changes among the genes, PCA has done a poor predicting job. The PCA results are contained in Additional File 3.

Usually, the reduced set of variables will serve as the input of a prediction algorithm to build a model. Such algorithms used in this article include MVR, LASSO, and D/S/A. We have noticed that in most of our simulations, the MVR prediction often achieves a similar risk as LASSO and D/S/A on the TMLE-VIM reduced set of variables. It suggests that further variable selection may not be necessary for the TMLE-VIM candidate list, and we can use simpler algorithms to get a good prediction. In fact, the TMLE-VIM can go beyond the scope of dimension reduction. It can be iteratively applied to the data until it converges to a list of several variables that are most likely to be causal to the outcome. In this case, one may want to use the Super Learner [28] as the prediction algorithm, which works more effectively with the TMLE-VIM. The Super Learner is an ensemble learner that combines predictions from multiple candidate learners with optimal weights. It has been shown in [29] that the Super Learner performs asymptotically equal to or better than any of its candidate learners. The Super Learner allows the data to objectively blend results from different algorithms rather than relying on a single algorithm chosen subjectively by an analyst. Hence it enjoys a greater flexibility to explore the model space and usually produces reasonable predictions consistently across a wide variety of datasets, and serves as a very good prediction algorithm for the TMLE-VIM. On the other hand, it is also more computationally demanding.

TMLE-VIM is a quite general approach. Besides gene expression data, TMLE-VIM can also be applied to genetic mapping problems. The genome-wide association studies (GWAS) can involve more than a million of genetic markers. In this case, only the univariate analysis seems to be feasible of ranking every marker. With the TMLE-VIM procedure, we can run more complex algorithms on a subset of top ranked markers, taking it as the initial estimator, and then evaluate every single marker. The variable importance of each marker is thus obtained through a multi-marker approach and being adjusted for its confounder. However, the GWAS in human beings is usually case-control data, and the current TMLE-VIM needs to be extended to accommodate such outcomes.

The authors declare that they have no competing interests.

MvdL conceived the project and designed the algorithm. HW implemented the algorithm, designed the simulation studies, and collected and analyzed the data. All authors participated in drafting the manuscript.

**More detailed descriptions of the TMLE methodology and the conducted simulations**.

Click here for file^{(136K, PDF)}

**The additional materials of the conducted simulations**.

Click here for file^{(500K, PDF)}

The authors want to thank Cathy Tugulus for sharing her codes and her helpful comments on this work. The authors also thank the reviewers for their precious appraisal of the earlier version of this manuscript. This work was by NIH R01 AI074345. The authors declare no conflicts of interest.

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