Biomarker Selection from Gene Expression Data for Tumour Categorization Using Bat Algorithm

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1 Receved: March 20, Bomarker Selecton from Gene Expresson Data for Tumour Categorzaton Usng Bat Algorthm Gunavath Chellamuthu 1 *, Premalatha Kandasamy 2, Svasubramanan Kanagaraj 3 1 School of Informaton Technology and Engneerng, Vellore Insttute of Technology Unversty, Vellore, Inda 2 Department of Computer Scence and Engneerng, Bannar Amman Insttute of Technology, Sathyamangalam, Inda 3 Department of Electroncs and Communcaton Engneerng, KS Rangasamy College of Technology, Truchengode, Inda * Correspondng author s Emal: sssguna@gmal.com Abstract: Mcroarray technology s commonly used n the study of dsease dagnoss usng gene expresson levels. The classfcaton of cancer s a foremost area of research n the feld of bonformatcs. Mcroarray technology enables the researcher to nvestgate the expresson levels of thousands of genes n a sngle experment and gves the measurements of ther dfferental expresson. The man drawback of gene expresson data s that t contans thousands of genes and a very few samples. Feature or gene selecton methods are used to successfully extract the genes that drectly nvolved n the classfcaton and to elmnate rrelevant genes. These methods consderably mprove the classfcaton accuracy. The proposed method apples bat algorthm (BA) for feature selecton n tumour classfcaton. Intally, the top-10 genes are selected by T-Statstcs, sgnal-to-nose rato (SNR) and F-Test. The classfer accuracy of k-nearest neghbour (knn) technque s used as the ftness functon for BA. The smulated results are demonstrated and analyzed wth 10 dfferent cancer gene expresson dataset. For Lung Cancer Mchgan and Lung Harvard2 datasets the proposed method gves 100% classfcaton accuracy wth mnmum number of genes. For 5 other datasets, the proposed method gves more than 90% of classfcaton accuracy. The results show the sutablty of the proposed algorthm for feature selecton n cancer classfcaton. Keywords: Mcroarray gene expresson, Tumour categorzaton, Feature selecton, Bat algorthm, K-nearestneghbour. 1. Introducton Cancer s featured by an rregular, uncontrollable growth that may destroy and attack neghbourng healthy body tssues or somewhere else n the body. Gene expresson proflng by mcroarray method has been emerged as an effcent technque for classfcaton and dagnostc predcton of cancer. Cancer classfcaton refers to the process of constructng a model on the mcroarray dataset and then dstngushng one type of samples from other types wth ths nduced model. The raw mcroarray data are mages that are transformed nto gene expresson matrces. The rows n the matrx correspond to genes, and the columns represent samples or expermental condtons. The numbers n each cell denotes the expresson level of partcular gene n a partcular sample or condton [1, 2]. Expresson levels can be absolute or relatve. They are used to smultaneously montor and study the expresson levels of thousands of genes, relatonshp between genes, ther functons and classfyng genes or samples. If two rows are smlar, t mples that the respectve genes are co-regulated and possbly functonally related. By comparng samples, dfferentally expressed genes can be dentfed. The major lmtaton n gene expresson data s ts hgh dmensonalty. It contans more number of genes and a very few samples. Feature or gene selecton methods are needed to fnd the mportant genes that are reason for cancer. Feature selecton

2 Receved: March 20, methods remove rrelevant and redundant features to mprove classfcaton accuracy. A number of gene selecton methods have been ntroduced to select nformatve genes for cancer predcton and dagnoss. Most commonly used gene selecton methods are Relef-F, Mnmal-Redundancy-Maxmal Relevance (MRMR), T-statstc, Informaton Gan and Chsquare statstc [3]. Feature selecton methods can be categorzed nto flter, wrapper, and embedded or hybrd [4]. T-statstcs, Sgnal-to-Nose Rato and F- Test are the feature selecton measures used n the proposed work to fnd the top-10 sgnfcant or nformatve genes. Optmzaton s the act of achevng the best possble result under gven condtons. The objectve of an optmzaton algorthm s to mnmze or maxmze the objectve functon. Bat algorthm (BA) s a novel meta-heurstc optmzaton algorthm based on the echolocaton behavor of mcrobats wth varyng pulse rate of emsson and loudness. BA s very smple to understand. It has only few parameters to adjust. Its convergence speed s fast, and t s easy to mplement. The proposed approach s a hybrd system that uses the BA for feature selecton to classfy the gven samples and the ftness functon of BA s measured by the knn technque. Ths smple model based on statstcal measures and optmzaton technque performs two level of feature selecton to get the most nformatve genes for the classfcaton process. The paper s organzed as follows: Secton 2 descrbes about gene selecton methods such as T-statstcs, Sgnal-to-Nose Rato and F-Test. Secton 3 explans about k-nearest Neghbour Classfcaton algorthm. Secton 4 gves the detals about BA. Secton 5 explans about tumour categorzaton wth BA. Secton 6 presents the expermental results obtaned from the proposed method. 2. Gene selecton methods 2.1 T-statstcs Genes who have sgnfcantly dfferent expressons between normal and tumour tssues or between subtypes of tumour tssues are also canddates for selecton. A smple T-statstc can be used to measure the degree of gene expresson dfference between normal and tumour tssues [5]. The top-10 genes wth the largest T- statstc are selected for ncluson n the dscrmnant analyss. t x1 x2 v1 v2 n1 n2 (1) Here x 1 - Mean of Normal samples x2 - Mean of Tumour samples n1 - Normal Sample sze n2 - Tumour Sample sze v1 - varance of Normal samples v2 - varance of Tumour samples 2.2 Sgnal-to-nose rato A sgnfcant measure used n fndng the mportance of genes s the Pearson Correlaton Coeffcent. It s modfed as follows to emphasze the Sgnal-to-Nose Rato n usng a gene as a predctor [1]. Ths predctor s created wth the purpose of fndng the Predcton Strength of a partcular Gene [6]. The Sgnal-to-Nose rato PS of a gene g s defned as PS( g) x1 x2 s1 s2 Here x 1 Mean of Normal samples x2 - Mean of Tumour samples s1 - Standard Devaton of Normal samples s2 - Standard Devaton of Tumour samples (2) Ths value s used to reflect the dfference between the classes relatve to the standard devaton wthn the classes. Large values of PS(g) ndcate a strong correlaton between the gene expresson and the class dstncton, whle the sgn of PS(g) beng postve or negatve corresponds to g beng more hghly expressed n class 1 or class 2. Genes wth large SNR value are nformatve and are selected for tumour classfcaton. Top-10 genes wth the largest SNR value are selected for ncluson n the dscrmnant analyss. 2.3 F-Test F-Test s generally defned as the rato of the varances of the gven two set of values. The F-test s used to test f the standard devatons of two populatons are equal or f the standard devaton from one populaton s less than that of another populaton. Ths test can be a two-taled test or a onetaled test. The two-taled verson tests aganst the alternatve that the standard devatons are not equal. The one-taled verson only tests n one drecton that s the standard devaton from the frst populaton s ether greater than or less than (but not both) the

3 Receved: March 20, second populaton standard devaton. Top-10 genes wth the smallest F-Test value are selected for ncluson n the dscrmnant analyss. Here F v1 v2 v1 - Varance of Normal Samples v2 - Varance of Tumour Samples 3. K-nearest neghbour algorthm (3) The k-nearest Neghbour algorthm s one of the smplest of all machne learnng algorthms. It s one of the Lazy learners n whch the learner wats untl the last moment before constructng any model for the purpose of classfyng a gven test tuple. When gven a tranng sample, a lazy learner smply stores t and wats untl t s gven a test tuple. It s a method for classfyng objects based on closest tranng examples n the feature space. Here a sample s classfed by a majorty vote of ts neghbours, wth the object beng assgned to the class most common amongst ts k-nearest Neghbours (k s a postve nteger, typcally small) measured by a dstance functon. If k = 1, then the object s smply assgned to the class of ts nearest. Sometmes one mnus correlaton value s also taken as a dstance metrc. For contnuous varables the followng three dstance measures are used. They are Eucldean dstance, Manhattan dstance and Mnkowsk dstance. In ths work Eucldean dstance between two samples s used as the dstance measure. 4. Bat algorthm Bat Algorthm (BA) s a novel meta-heurstc optmzaton algorthm frstly proposed n Xn She Yang n 2010 [7]. Mcrobats are nsectvores. Bats use echolocaton to locate and catch ther prey. Bat echolocaton s a perceptual system where ultrasonc sounds are emtted specfcally to produce echoes. By usng the tme delay between the outgong pulse and the returnng echoes the bran and audtory nervous system of the bat produces a detaled three dmensonal mage of the surroundngs. From ths, bats can detect, localze and even classfy ther prey n complete darkness. When bats fly, they produce a constant stream of hgh-ptched sounds that can be heard only by them. When the sound waves produced by these bats ht an nsect or other anmal, the echoes bounce back to the bats, and gude them to the source [8]. Ther pulses vary n propertes and can be correlated wth ther huntng strateges, dependng on the speces. The rules for Bat algorthm are: 1. All bats utlze echolocaton to sense dstance. They can also dstngush the dfference between food/prey and background barrers n some supernatural way 2. Bats fly randomly wth velocty v at poston X wth a fxed frequency f mn, varyng wavelength λ and loudness A 0 to search for prey. They can automatcally fne-tune the wavelength (or frequency) of ther emtted pulses and adjust the rate of pulse emsson R [0, 1], based on the proxmty of ther target 3. The loudness can vary n many ways. Here, t s assumed that the loudness vares from a large (postve) value A 0 to a mnmum constant value A mn. In the mplementatons, vrtual bats are used naturally. Consder, n an n-dmensonal search space the postons X and veloctes v of the bats are to be updated. The pulse frequency f s calculated by usng Eq. (4). At tme t, the new solutons X (t) and veloctes v (t) are calculated by usng Eqs. (5) and (6). f f mn ( f max f mn ) s X ( t) X ( t 1) v ( t) v ( t) v ( t 1) ( X ( t 1) X*) f 1 (4) (5) (6) Here, S 1 s a random number, S 1 [0,1] whch s drawn from a unform dstrbuton. The frequency f ranges from f mn to f max. Intally all the bats are randomly assgned wth a frequency that s drawn unformly from [f mn, f max]. In ths work, the value of f mn s 0 and f max s 2. X * s the current global best locaton among all the n bats/solutons. For the local search, once a soluton X s chosen from the current best solutons, a new soluton X +1 for each bat s generated locally usng random walk. t X 1 X A (7) Here, η s a random number between [ 1, 1] and A t s the average loudness of all bats at tme step t. As the teratons proceed, the loudness A and rate of pulse emsson R have to be updated. The bats adjust ther pulse emsson rate and loudness dependng on the closeness of the prey/target. Normally the loudness decreases once a bat has found ts prey. The rate of pulse emsson R ncreases as teraton ncreases. The loudness and rate of pulse

4 Receved: March 20, emsson are updated accordng to the Equatons (8) and (9). A ( t 1) A ( t). S 2 R (t 1) R (0)[1-exp(- t)] (8) (9) Here, S 2 and γ are constants, gven that 0 < S 2 < 1 and γ >0. Also A (t) 0, R (t) R (0) as t. The BA proposed by Yang n 2010 s shown n Fg. 1. Algorthm: BA Intalze the bat populaton and ther velocty Defne pulse frequency f at X Intalze pulse rates R and the loudness A whle (t <max number of teratons) Generate new solutons by adjustng frequency, and updatng veloctes and postons (usng equatons 4, 5 & 6) f (rand > R ) Select a soluton among the best soluton Generate a local soluton around the selected best soluton (usng equaton 7) end f Generate a new soluton by flyng randomly f (rand < A & f(x ) < f(x * )) Accept new solutons Reduce A and ncrease R (usng 8 & 9) end f Rank the bats and fnd the current best X* end whle Fgure.1 Pseudo code of the BA 5. Cancer classfcaton usng BA The proposed approach s based on BA wth knn on the selected genes (ndvduals). 5.1 Bat representaton The bat should contan nformaton about the soluton whch t represents. The most used way of encodng s a bnary strng. In the bat representaton bnary code 1 or 0 s used to mark whether a gene s selected or not. So each bat n the populaton s encoded by a strng lke Fnally, the gene subsets are obtaned by choosng the genes that are marked by Ftness functon The ftness functon f(x) of a bat s measured by knn technque [9]. The accuracy of knn classfer s used as the ftness functon. The ftness functon f(x) s defned as Ftness(x)=Accuracy(x) (10) where Accuracy(x) s test accuracy of testng data of the knn classfer bult wth the feature subset selecton of tranng data whch s represented by x. The classfcaton accuracy of knn s gven by the followng formula. Accuracy(x)=(c / t) 100 (11) Here c - Samples that are classfed correctly n test data by knn technque t - Total number of Samples n test data 6. Expermental results The proposed method uses T-statstcs, Sgnal-to- Nose Rato and F-Test to select top-10 genes. These genes alone used for further classfcaton. Bat algorthm s appled on the selected genes. For classfcaton purpose the gven dataset s dvded nto tranng and test samples. Intally the system s traned wth tranng samples. Then the proposed method s tested on test samples. The classfcaton accuracy of knn s used as a ftness functon for BA. The knn wth 5-fold cross valdaton method gves the classfcaton accuracy as output. The BA was confgured to have 10 bats and was run for 100 teratons n each tral. The parameter pulse rate of BA s consdered as 0.5 and the loudness value s taken as 0.25 [7]. The values of S1 and S2 are the random numbers n the range 0 to 1. The value of γ s a constant. In order to assess the performance of the proposed method, 10 datasets were analyzed. These datasets were collected from Kent Rdge Bomedcal Data Repostory [10]. The detals about the datasets are gven n Table1. Table 2 gves the Parameters and ther values used n ths method. g 1 g 2 g 3 g 4 g n-1 g m Fgure.2 Bat representaton

5 Receved: March 20, Table1. Detals of Datasets used n ths method Dataset Name Number of Genes Class1 Class2 Total Samples CNS 7129 Survvors (21) Falures (39) 60 DLBCL Harvard 7129 DLBCL (58) FL (19) 77 DLBCL Outcome 7129 Cured (32) Fatal (26) 58 Lung Cancer Mchgan 7129 Tumour (86) Normal (10) 96 Ovaran Cancer Normal (91) Cancer (162) 253 Prostate Outcome Non-Relapse (13) Relapse (8) 21 AML-ALL 7129 ALL (47) AML (25) 72 Colon Tumour 2000 Tumour (40) Healthy (22) 62 Lung Harvard ADCA (150) Mesotheloma (31) 181 Prostate Normal (59) Tumour (77) 136 Table 2. Parameters and values Parameter Value Loudness (A) 0.25 Pulse rate (R) 0.5 γ 0.5 S 1 rand(0,1) S 2 rand(0,1) Number of bats 10 Number of teratons 100 Dstance Measure n knn Eucldean dstance k-value s knn 3 Table 3. Expermental results T-statstcs SNR F-Test S.No. Dataset Number of Accuracy Number of Accuracy Number Accuracy gene(s) gene(s) of gene(s) 1 CNS % % % 2 DLBCL Harvard 3 78% 6 92% 5 80% 3 DLBCL Outcome % % % 4 Lung Cancer Mchgan % % % 5 Ovaran Cancer % % % 6 Prostate Outcome % % % 7 AML-ALL % % % 8 Colon Tumour 3 75 % 5 95 % 1 75 % 9 Lung Harvard % % % 10 Prostate % % %

6 Receved: March 20, S.No. Table 4. Maxmum accuracy wth mnmum genes Maxmum Accuracy (wth Mnmum Genes) Number of Dataset Name gene(s) Accuracy Gene selecton method 1 CNS % T-statstcs 2 DLBCL Harvard 6 92 % SNR 3 DLBCL outcome % SNR 4 Lung Cancer Mchgan % F-Test 5 Ovaran Cancer % SNR 6 Prostate outcome % SNR 7 AML-ALL % F-Test 8 Colon Tumour 5 95 % SNR 9 Lung Harvard % SNR 10 Prostate % F-Test Fgure.3 Classfcaton accuracy Fgure.4 Maxmum accuracy

7 Receved: March 20, Table 5. Comparson wth other methods Reference / Dataset CNS DLBCL Harvard DLBCL outcome Lung Cancer Mchgan Ovaran Cancer Prostate outcome AML-ALL Colon Tumor Lung Harvard2 Prostate [11] [12] [13] [14] [3] [15] BA Table 3 shows the results obtaned from the proposed method. It gves the Classfcaton accuracy wth mnmum number of genes wth top-10 genes when appled dfferent measures lke Sgnal-to-Nose rato, T-statstcs and F-Test. Table 4 represents the correspondng measure whch gves the maxmum accuracy wth mnmum number of genes among top- 10 genes. Fgure 3 depcts the classfcaton accuracy obtaned from dfferent measures when applyng BA and knn on top-10 genes. Fgure 4 depcts the maxmum accuracy obtaned for dfferent cancer types when applyng BA and knn on top-10 genes. Table 5 dsplays the results obtaned from BA based feature selecton method for each of the dataset and the results are compared wth other exstng methods n the lterature. BA has a capablty of automatcally zoomng nto an area where favourable solutons have been found. Ths zoomng s supplemented by the automatc swtch from exploratve moves to local ntensve explotaton. BA has guaranteed global convergence propertes under the rght condton, and t can also solve large-scale problems effectvely. From the results t s observed that the performance of the BA based feature selecton method s comparable wth other works. It gves maxmum classfcaton accuracy wth mnmum number of genes. on those top genes n ths research work. Here the classfcaton accuracy of knn s consdered as the ftness functon for the BA. The knn classfer s one of the most famous neghbourhood classfer n pattern recognton. The knn wth 5-fold crossvaldaton s appled to avod the over fttng of the data. The performance of hybrd method s tested wth ten dfferent cancer datasets. For Lung Cancer Mchgan and Lung Harvard2 datasets the proposed method gves 100% classfcaton accuracy wth mnmum number of genes. For DLBCL Harvard, Ovaran Cancer, AML-ALL, Colon Tumour and Prostate datasets, the proposed method gves more than 90% of classfcaton accuracy. The results prove that only the nformatve gene selecton leads to mprove the classfcaton accuracy. The above method can be appled to the gene expresson data of any type of cancer, because t was successfully demonstrated wth ten dfferent cancer datasets n ths research work. In ths proposed work, only bnaryclass cancer gene expresson datasets are consdered. Further research may focus on datasets wth multple-class labels. Other statstcal measures such as nformaton gan, ch-square test can also be consdered for gene rankng. Hybrd approaches of optmzaton may be mplemented wth an mproved soluton whch can be suggested to avod premature convergence. 7. Concluson Cancer classfcaton usng gene expresson data s an mportant task for addressng the problem of cancer dagnoss and drug dscovery. T-statstcs, Sgnal-to-Nose Rato and F-Test are the feature selecton methods used to select the mportant genes. Bat Algorthm wth knn Classfer method s appled References [1] T.R. Golub, D.K. Slonm, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesrov, H. Coller, M.L. Loh, J.R. Downng, M.A. Calgur, C.D. Bloomfeld, and E.S. Lander, Molecular Classfcaton of Cancer: Class Dscovery and Class Predcton by Gene Expresson

8 Receved: March 20, Montorng, Scence, Vol.286, No.5439, pp , [2] E. Domany, Cluster Analyss of Gene Expresson Data, Journal of Statstcal Physcs, Vol.110, No.3-6, pp , [3] B. Chandra and M. Gupta, An effcent statstcal feature selecton approach for classfcaton of gene expresson data, Journal of Bomedcal Informatcs, Vol.44, No.4, pp , [4] Y. Saeys, I. Inza, and P. Larranaga, A revew of feature selecton technques n bonformatcs, Bonformatcs, Vol.23, No.19, pp , [5] K. Yendrapall, R. Basnet, S. Mukkamala, and AH. Sung, Gene Selecton for Tumor Classfcaton Usng Mcroarray Gene Expresson Data, In: Proc. of the World Congress on Engneerng, London, UK, Vol.I, pp , [6] M. Xong, W. L, J. Zhao, L. Jn, and E. Boerwnkle, Feature (Gene) Selecton n Gene Expresson-Based Tumor Classfcaton, Journal of Molecular Genetcs and Metabolsm, Vol.73, No.3, pp , [7] X.S. Yang, A New Metaheurstc Bat-Inspred Algorthm, Nature Inspred Cooperatve Strateges for Optmzaton, Eds. J. R. Gonzalez et al, Studes n Computatonal Intellgence, Sprnger Berln, Sprnger, Vol.284, pp.65-74, [8] P. Lauber, Bats: Wngs n the Nght, Random House, New York, [9] MS. Mohamed, S. Ders, and M.R. Othman, Genetc Algorthms wrapper approach to select nformatve genes for gene expresson mcroarray classfcaton usng support vector machnes, In: Proc. of Thrd Internatonal Conf. on Bonformatcs, Auckland, New Zealand, [10] Kent Rdge Bomedcal Data Repostory 2002, Avalable from: < [15 February 2013]. [11] J.M. Arevalllo and H. Navarro, Explorng correlatons n gene expresson mcroarray data for maxmum predctve-mnmum redundancy bomarker selecton and classfcaton, Computers n Bology and Medcne, Vol.43, No.10, pp , [12] G.C.J. Alonso, I.Q.M. Sancho, A.S. Hurtado, and R.V. Arrabal, Mcroarray gene expresson classfcaton wth few genes: crtera to combne attrbute selecton and classfcaton methods, Expert Systems wth Applcatons, Vol.39, No.8, pp , [13] P. Maj, Mutual nformaton-based supervsed attrbute clusterng for mcroarray sample classfcaton, IEEE Transactons on Knowledge and Data Engneerng, Vol.24, No.1, pp , [14] X. Wang and R. Smon, Mcroarray-based cancer predcton usng sngle genes, BMC Bonformatcs, Vol.12, Artcle.391, do: / , [15] H. Lu, L. Lu and H. Zhang, Ensemble gene selecton for cancer classfcaton, Pattern Recognton, Vol.43, No.8, pp , 2010.