Research article

Identifying candidate diagnostic markers for tuberculosis: A critical role of co-expression and pathway analysis

  • Received: 17 April 2018 Accepted: 18 September 2018 Published: 10 January 2019
  • We conducted a systematic bioinformatics analysis to explore an important set of gene expression data with 39 samples infected at different time stages withW-Beijing families of Mycobacterium tuberculosis strains. We took a contrast on the samples at different infection time stages to characterize gene expression features of the THP1 cells to identify sensitive and specific molecular markers for diagnosis. We first confirmed, through the multidimensional scaling unsupervised clustering, that samples were clustered well according to different infection times. Building on this classification result and using the linear modelling and empirical Bayes moderation, we found 287 hits as most significant genes associated with tuberculosis. We generated a gene co-expression network map based on the mutual regulation between the differentially expressed genes. We found that 27 genes are regulatory genes associated with tuberculosis. We constructed 4 gene pathway figures to explain the pathogenicity process that involves 24 key genes. This study implicates that contrast on the gene expression of the classifications in different infection stages provides critical information for the detection of tuberculosis, and our method can be utilized to narrow down the shortlist of disease relevant genes and explore tuberculosis pathogenesis.

    Citation: Xu Zhang, Dongdong Chen, Wenmin Yang, JianhongWu. Identifying candidate diagnostic markers for tuberculosis: A critical role of co-expression and pathway analysis[J]. Mathematical Biosciences and Engineering, 2019, 16(2): 541-552. doi: 10.3934/mbe.2019026

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  • We conducted a systematic bioinformatics analysis to explore an important set of gene expression data with 39 samples infected at different time stages withW-Beijing families of Mycobacterium tuberculosis strains. We took a contrast on the samples at different infection time stages to characterize gene expression features of the THP1 cells to identify sensitive and specific molecular markers for diagnosis. We first confirmed, through the multidimensional scaling unsupervised clustering, that samples were clustered well according to different infection times. Building on this classification result and using the linear modelling and empirical Bayes moderation, we found 287 hits as most significant genes associated with tuberculosis. We generated a gene co-expression network map based on the mutual regulation between the differentially expressed genes. We found that 27 genes are regulatory genes associated with tuberculosis. We constructed 4 gene pathway figures to explain the pathogenicity process that involves 24 key genes. This study implicates that contrast on the gene expression of the classifications in different infection stages provides critical information for the detection of tuberculosis, and our method can be utilized to narrow down the shortlist of disease relevant genes and explore tuberculosis pathogenesis.


    1. Introduction

    Multi-nominal data are common in scientific and engineering research such as biomedical research, customer behavior analysis, network analysis, search engine marketing optimization, web mining etc. When the response variable has more than two levels, the principle of mode-based or distribution-based proportional prediction can be used to construct nonparametric nominal association measure. For example, Goodman and Kruskal [3,4] and others proposed some local-to-global association measures towards optimal predictions. Both Monte Carlo and discrete Markov chain methods are conceptually based on the proportional associations. The association matrix, association vector and association measure were proposed by the thought of proportional associations in [9]. If there is no ordering to the response variable's categories, or the ordering is not of interest, they will be regarded as nominal in the proportional prediction model and the other association statistics.

    But in reality, different categories in the same response variable often are of different values, sometimes much different. When selecting a model or selecting explanatory variables, we want to choose the ones that can enhance the total revenue, not just the accuracy rate. Similarly, when the explanatory variables with cost weight vector, they should be considered in the model too. The association measure in [9], ωY|X, doesn't consider the revenue weight vector in the response variable, nor the cost weight in the explanatory variables, which may lead to less profit in total. Thus certain adjustments must be made for a better decisionning.

    To implement the previous adjustments, we need the following assumptions:

    X and Y are both multi-categorical variables where X is the explanatory variable with domain {1,2,...,α} and Y is the response variable with domain {1,2,...,β} respectively;

    the amount of data collected in this article is large enough to represent the real distribution;

    the model in the article mainly is based on the proportional prediction;

    the relationship between X and Y is asymmetric;

    It needs to be addressed that the second assumption is probably not always the case. The law of large number suggests that the larger the sample size is, the closer the expected value of a distribution is to the real value. The study of this subject has been conducted for hundreds of years including how large the sample size is enough to simulate the real distribution. Yet it is not the major subject of this article. The purpose of this assumption is nothing but a simplification to a more complicated discussion.

    The article is organized as follows. Section 2 discusses the adjustment to the association measure when the response variable has a revenue weight; section 3 considers the case where both the explanatory and the response variable have weights; how the adjusted measure changes the existing feature selection framework is presented in section 4. Conclusion and future works will be briefly discussed in the last section.


    2. Response variable with revenue weight vector

    Let's first recall the association matrix {γs,t(Y|X)} and the association measure ωY|X and τY|X.

    γs,t(Y|X)=E(p(Y=s|X)p(Y=t|X))p(Y=s)=αi=1p(X=i|Y=s)p(Y=t|X=i);s,t=1,2,..,βτY|X=ωY|XEp(Y)1Ep(Y)ωY|X=EX(EY(p(Y|X)))=βs=1αi=1p(Y=s|X=i)2p(X=i)=βs=1γssp(Y=s) (1)

    γst(Y|X) is the (s,t)-entry of the association matrix γ(Y|X) representing the probability of assigning or predicting Y=t while the true value is in fact Y=s. Given a representative train set, the diagonal entries, γss, are the expected accuracy rates while the off-diagonal entries of each row are the expected first type error rates. ωY|X is the association measure from the explanatory variable X to the response variable Y without a standardization. Further discussions to these metrics can be found in [9].

    Our discussion begins with only one response variable with revenue weight and one explanatory variable without cost weight. Let R=(r1,r2,...,rβ) to be the revenue weight vector where rs is the possible revenue for Y=s. A model with highest revenue in total is then the ideal solution in reality, not just a model with highest accuracy. Therefore comes the extended form of ωY|X with weight in Y as in 2:

    Definition 2.1.

    ˆωY|X=βs=1αi=1p(Y=s|X=i)2rsp(X=i)=βs=1γssp(Y=s)rsrs>0,s=1,2,3...,β (2)

    Please note that ωY|X is equivalent to τY|X for given X and Y in a given data set. Thus the statistics of τY|X will not be discussed in this article.

    It is easy to see that ˆωY|X is the expected total revenue for correctly predicting Y. Therefore one explanatory variable X1 with ˆωY|X1 is preferred than another X2 if ˆωY|X1ˆωY|X2. It is worth mentioning that ˆωY|X is asymmetric, i.e., ˆωY|XˆωX|Y and that ωY|X=ˆωY|X if r1=r2=...=rβ=1.

    Example.Consider a simulated data motivated by a real situation. Suppose that variable Y is the response variable indicating the different computer brands that the customers bought; X1, as one explanatory variable, shows the customers' career and X2, as another explanatory variable, tells the customers' age group. We want to find a better explanatory variable to generate higher revenue by correctly predicting the purchased computer's brand. We further assume that X1 and X2 both contain 5 categories, Y has 4 brands and the total number of rows is 9150. The contingency table is presented in 1.

    Table 1. Contingency tables:X1 vs Y and X2 vs Y.
    X1|Y y1 y2 y3 y4 X2|Y y1 y2 y3 y4
    x11 1000 100 500 400 x21 500 300 200 1500
    x12 200 1500 500 300 x22 500 400 400 50
    x13 400 50 500 500 x23 500 500 300 700
    x14 300 700 500 400 x24 500 400 1000 100
    x15 200 500 400 200 x25 200 400 500 200
     | Show Table
    DownLoad: CSV

    Let us first consider the association matrix {γY|X}. Predicting Y with the information of X1, or X2 is given by the association matrix γ(Y|X1), or γ(Y|X2) as in Table 2.

    Table 2. Association matrices:X1 vs Y and X2 vs Y.
    Y|ˆY ^y1|X1 ^y2|X1 ^y3|X1 ^y4|X1 Y|ˆY ^y1|X2 ^y2|X2 ^y3|X2 ^y4X2
    y1 0.34 0.18 0.27 0.22 y1 0.26 0.22 0.27 0.25
    y2 0.13 0.48 0.24 0.15 y2 0.25 0.24 0.29 0.23
    y3 0.24 0.28 0.27 0.21 y3 0.25 0.24 0.36 0.15
    y4 0.25 0.25 0.28 0.22 y4 0.22 0.18 0.14 0.46
     | Show Table
    DownLoad: CSV

    Please note that Y contains the true values while ˆY is the guessed one. One can see from this table that the accuracy rate of predicting y1 and y2 by X1 on the left are larger than that on the right. The cases of y3 and y4, on the other hand, are opposite.

    The correct prediction contingency tables of X1 and Y, denoted as W1, plus that of X2 and Y, denoted as W2, can be simulated through Monte Carlo simulation as in Table 3.

    Table 3. Contingency table for correct predictions: W1 and W2.
    X1|Y y1 y2 y3 y4 X2|Y y1 y2 y3 y4
    x11 471 6 121 83 x21 98 34 19 926
    x12 101 746 159 107 x22 177 114 113 1
    x13 130 1 167 157 x23 114 124 42 256
    x14 44 243 145 85 x24 109 81 489 6
    x15 21 210 114 32 x25 36 119 206 28
     | Show Table
    DownLoad: CSV

    The total number of the correct predictions by X1 is 3142 while it is 3092 by X2, meaning the model with X1 is better than X2 in terms of accurate prediction. But it maybe not the case if each target class has different revenues. Assuming the revenue weight vector of Y is R=(1,1,2,2), we have the association measure of ωY|X, and ˆωY|X as in Table 4:

    Table 4. Association measures: ωY|X, and ˆωY|X.
    X ωY|X ˆωY|X total revenue average revenue
    X1 0.3406 0.456 4313 0.4714
    X2 0.3391 0.564 5178 0.5659
     | Show Table
    DownLoad: CSV

    Given that revenue=i,sWi,skrs,i=1,2,...,α,s=1,2,...,β,k=1,2, we have the revenue for W1 as 4313, and that for W2 as 5178. Divide the revenue by the total sample size, 9150, we can obtain 0.4714 and 0.5659 respectively. Contrasting these to ˆωY|X1 and ˆωY|X2 above, we believe that they are similar, which means then ˆωY|X is truly the expected revenue.

    In summary, it is possible for an explanatory variable X with bigger ˆωY|X, i.e, the larger revenue, but with smaller ωY|X, i.e., the smaller association. When the total revenue is of the interest, it should be the better variable to be selected, not the one with larger association.


    3. Explanatory variable with cost weight and response variable with revenue weight

    Let us further discuss the case with cost weight vector in predictors in addition to the revenue weight vector in the dependent variable. The goal is to find a predictor with bigger profit in total. We hence define the new association measure as in 3.

    Definition 3.1.

    ˉωY|X=αi=1βs=1p(Y=s|X=i)2rscip(X=i) (3)

    ci>0,i=1,2,3,...,α, and rs>0,s=1,2,...,β.

    ci indicates the cost weight of the ith category in the predictor and rs means the same as in the previous section. ˉωY|X is then the expected ratio of revenue and cost, namely RoI. Thus a larger ˉωY|X means a bigger profit in total. A better variable to be selected then is the one with bigger ˉωY|X. We can see that ˉωY|X is an asymmetric measure, meaning ˉωY|XˉωY|X. When c1=c2=...=cα=1, Equation 3 is exactly Equation 2; when c1=c2=...=cα=1 and r1=r2=...=rβ=1, equation 3 becomes the original equation 1.

    Example. We first continue the example in the previous section with new cost weight vectors for X1 and X2 respectively. Assuming C1=(0.5,0.4,0.3,0.2,0.1), C2=(0.1,0.2,0.3,0.4,0.5) and R=(1,1,1,1), we have the associations in Table 5.

    Table 5. Association with/without cost vectors: X1 and X2.
    X ωY|X ˆωY|X ˉωY|X total profit average profit
    X1 0.3406 0.3406 1.3057 12016.17 1.3132
    X2 0.3391 0.3391 1.8546 17072.17 1.8658
     | Show Table
    DownLoad: CSV

    By profit=i,sWi,skrsCki,i=1,2,..,α;s=1,2,..,β and k=1,2 where Wk is the corresponding prediction contingency table, we have the profit for X1 as 12016.17 and that of X2 as 17072.17. When both divided by the total sample size 9150, they change to 1.3132 and 1.8658, similar to ˉω(Y|X1) and ˉω(Y|X2). It indicates that ˉωY|X is the expected RoI. In this example, X2 is the better variable given the cost and the revenue vectors are of interest.

    We then investigate how the change of cost weight affect the result. Suppose the new weight vectors are: R=(1,1,1,1), C1=(0.1,0.2,0.3,0.4,0.5) and C2=(0.5,0.4,0.3,0.2,0.1), we have the new associations in Table 6.

    Table 6. Association with/without new cost vectors: X1 and X2.
    X ωY|X ˆωY|X ˉωY|X total profit average profit
    X1 0.3406 0.3406 1.7420 15938.17 1.7419
    X2 0.3391 0.3391 1.3424 12268.17 1.3408
     | Show Table
    DownLoad: CSV

    Hence ˉωY|X1>ˉωY|X2, on the contrary to the example with the old weight vectors. Thus the right amount of weight is critical to define the better variable regarding the profit in total.


    4. The impact on feature selection

    By the updated association defined in the previous section, we present the feature selection result in this section to a given data set S with explanatory categorical variables V1,V2,..,Vn and a response variable Y. The feature selection steps can be found in [9].

    At first, consider a synthetic data set simulating the contribution factors to the sales of certain commodity. In general, lots of factors could contribute differently to the commodity sales: age, career, time, income, personal preference, credit, etc. Each factor could have different cost vectors, each class in a variable could have different cost as well. For example, collecting income information might be more difficult than to know the customer's career; determining a dinner waitress' purchase preference is easier than that of a high income lawyer. Therefore we just assume that there are four potential predictors, V1,V2,V3,V4 within the data set with a sample size of 10000 and get a feature selection result by monte carlo simulation in Table 7.

    Table 7. Simulated feature selection: one variable.
    X |Dmn(X)| ωY|X ˉωY|X total profit average profit
    V1 7 0.3906 3.5381 35390 3.5390
    V2 4 0.3882 3.8433 38771 3.8771
    V3 4 0.3250 4.8986 48678 4.8678
    V4 8 0.3274 3.7050 36889 3.6889
     | Show Table
    DownLoad: CSV

    The first variable to be selected is V1 using ωY|X as the criteria according to [9]. But it is V3 that needs to be selected as previously discussed if the total profit is of interest. Further we assume that the two variable combinations satisfy the numbers in Table 8 by, again, monte carlo simulation.

    Table 8. Simulated feature selection: two variables.
    X1,X2 |Dmn(X1,X2)| ωY|(X1,X2) ˉωY|(X1,X2) total profit average profit
    V1,V2 28 0.4367 1.8682 18971 1.8971
    V1,V3 28 0.4025 2.1106 20746 2.0746
    V1,V4 56 0.4055 1.8055 17915 1.7915
    V3,V2 16 0.4055 2.3585 24404 2.4404
    V3,V4 32 0.3385 2.0145 19903 1.9903
     | Show Table
    DownLoad: CSV

    As we can see, all ωY|(X1,X2)ωY|X1, but it is not case for ˉωY|(X1,X2) since the cost gets larger with two variables thus the profit drops down. As in one variable scenario, the better two variable combination with respect to ωY|(X1,X2) is (V1,V2) while ˉωY|(X1,X2) suggests (V3, V2) is the better choice.

    In summary, the updated association with cost and revenue vector not only changes the feature selection result by different profit expectations, it also reflects a practical reality that collecting information for more variables costs more thus reduces the overall profit, meaning more variables is not necessarily better on a Return-Over-Invest basis.


    5. Conclusions and remarks

    We propose a new metrics, ¯ωY|X in this article to improve the proportional prediction based association measure, ωY|X, to analyze the cost and revenue factors in the categorical data. It provides a description to the global-to-global association with practical RoI concerns, especially in a case where response variables are multi-categorical.

    The presented framework can also be applied to high dimensional cases as in national survey, misclassification costs, association matrix and association vector [9]. It should be more helpful to identify the predictors' quality with various response variables.

    Given the distinct character of this new statistics, we believe it brings us more opportunities to further studies of finding the better decision for categorical data. We are currently investigating the asymptotic properties of the proposed measures and it also can be extended to symmetrical situation. Of course, the synthetical nature of the experiments in this article brings also the question of how it affects a real data set/application. It is also arguable that the improvements introduced by the new measures probably come from the randomness. Thus we can use k-fold cross-validation method to better support our argument in the future.




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