2013, 10(3): 667-690. doi: 10.3934/mbe.2013.10.667

On the sensitivity of feature ranked lists for large-scale biological data

1. 

Silesian University of Technology, Institute of Automatic Control, Akademicka 16, 44-100 Gliwice, Poland, Poland

Received  June 2012 Revised  January 2013 Published  April 2013

The problem of feature selection for large-scale genomic data, for example from DNA microarray experiments, is one of the fundamental and well-investigated problems in modern computational biology. From the computational point of view, a selected gene list should be characterized by good predictive power and should be understood and well explained from the biological point of view. Recently, another feature of selected gene lists is increasingly investigated, namely their stability which measures how the content and/or the gene order change when the data are perturbed. In this paper we propose a new approach to analysis of gene list stability, termed the sensitivity index, that does not require any data perturbation and allows the gene list that is most reliable in a biological sense to be chosen.
Citation: Danuta Gaweł, Krzysztof Fujarewicz. On the sensitivity of feature ranked lists for large-scale biological data. Mathematical Biosciences & Engineering, 2013, 10 (3) : 667-690. doi: 10.3934/mbe.2013.10.667
References:
[1]

C. Alvarez-Baron, P. Jonsson, C. Thomas, S. Dryer and C. Williams, The two-pore domain potassium channel KCNK5: Induction by estrogen receptor alpha and role in proliferation of breast cancer cells,, Molecular Endocrinology, 25 (2011), 1326. Google Scholar

[2]

N. Ballatori, N. Li, F. Fang, J. Boyer, W. Christian and C. Hammond, OST alpha-OST beta: A key membrane transporter of bile acids and conjugated steroids,, Frontiers in Bioscience, 14 (2009), 2829. doi: 10.2741/3416. Google Scholar

[3]

A. Boulesteix and M. Slawski, Stability and aggregation of ranked gene lists,, Briefings in Bioinformatics, 10 (2009), 556. doi: 10.1093/bib/bbp034. Google Scholar

[4]

R. Buckanovich, D. Sasaroli, A. O'Brien-Jenkins, J. Botbyl, R. Hammond, D. Katsaros, R. Sandaltzopoulos, L. Liotta, P. Gimotty and G. Coukos, Tumor vascular proteins as biomarkers in ovarian cancer,, Journal of Clinical Oncology, 25 (2007), 852. doi: 10.1200/JCO.2006.08.8583. Google Scholar

[5]

V. Catalán, J. Gómez-Ambrosi, A. Rodríguez, B. Ramírez, F. Rotellar, V. Valentí, C. Silva, M. Gil, J. Salvador and G. Frühbeck, Up-regulation of the novel proinflammatory adipokines lipocalin-2, chitinase-3 like-1 and osteopontin as well as angiogenic-related factors in visceral adipose tissue of patients with colon cancer,, The Journal of Nutritional Biochemistry, 22 (2011), 634. Google Scholar

[6]

F. Coffman, Chitinase 3-Like-1 (CHI3L1): A putative disease marker at the interface of proteomics and glycomics,, Critical Reviews in Clinical Laboratory Sciences, 45 (2008), 531. doi: 10.1080/10408360802334743. Google Scholar

[7]

X. Deng, J. Xu, J. Hui and C. Wang, Probability fold change: A robust computational approach for identifying differentially expressed gene lists,, Computer Methods and Programs in Biomedicine, 93 (2009), 124. doi: 10.1016/j.cmpb.2008.07.013. Google Scholar

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S. Dudoit, J. Fridlyand and T. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data,, Journal of the American Statistical Association, 97 (2002), 77. doi: 10.1198/016214502753479248. Google Scholar

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T. J. Farr, S. J. Coddington-Lawson, P. M. Snyder and F. J. McDonald, Human Nedd4 interacts with the human epithelial Na+ channel: WW3 but not WW1 binds to Na+-channel subunits,, The Biochemical Journal, 345 (2000), 503. doi: 10.1042/0264-6021:3450503. Google Scholar

[11]

K. Fujarewicz, et al, A multigene approach to differentiate papillary thyroid carcinoma from benign lesions: Gene selection using bootstrap-based support vector machines,, Endocrine - Related Cancer, 14 (2007), 809. Google Scholar

[12]

K. Fujarewicz, M. Kimmel and J. Rzeszowska-Wolny, Improved classification of microarray gene expression data using support vector machines,, Journal of Medical Informatics and Technologies, 2 (2001). Google Scholar

[13]

K. Fujarewicz and M. Wiench, Selecting differencially expressed genes for colon tumor classification,, International Journal of Applied Mathematics and Computer Science, 13 (2003), 327. Google Scholar

[14]

J. Harvey, A. Gannon, Z. Li, C. Beard and C. Burgess, Identification of a novel methylation marker, SCNN1B,, AACR Meeting Abstracts, (2005). Google Scholar

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T. Hastie, R. Tibshirani and J. Friedman, "The Elements of Statistical Learning. Data Mining, Inference, and Prediction,", 2nd edition, (2009). doi: 10.1007/978-0-387-84858-7. Google Scholar

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M. Irigoyen, M. Pajares, J. Agorreta, M. Ponz-Sarvisé E. Salvo, M. Lozano, R. Pío, I. Gil-Bazo and A. Rouzaut, TGFBI expression is associated with a better response to chemotherapy in NSCLC,, Molecular Cancer, 9 (2010). doi: 10.1186/1476-4598-9-130. Google Scholar

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[18]

G. Jurman, S. Merler, A. Barla, S. Paoli, A. Galea and C. Furlanello, Algebraic stability indicators for ranked lists in molecular profiling,, Bioinformatics, 24 (2008), 258. doi: 10.1093/bioinformatics/btm550. Google Scholar

[19]

M. Kawada, H. Seno, K. Kanda, Y. Nakanishi, R. Akitake, H. Komekado, K. Kawada, Y. Sakai, E. Mizoguchi and T. Chiba, Chitinase 3-like 1 promotes macrophage recruitment and angiogenesis in colorectal cancer,, Oncogene, 31 (2012), 3111. doi: 10.1038/onc.2011.498. Google Scholar

[20]

C. Lai, M. Reinders, L. Veer and L. Wessels, A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets,, BMC Bioinformatics, 7 (2006), 235. Google Scholar

[21]

C. Ma, Y. Rong, D. Radiloff, M. Datto, B. Centeno, S. Bao, A. Cheng, F. Lin, S. Jiang, T. Yeatman and X Wang, Extracellular matrix protein betaig-h3/TGFBI promotes metastasis of colon cancer by enhancing cell extravasation,, Genes Dev., 22 (2008), 308. Google Scholar

[22]

L. Melchor, L. Saucedo-Cuevas, I. Munoz-Repeto, S. Rodrģuez-Pinilla, E. Honrado, A. Campoverde, J. Palacios, K. Nathanson, M. García and J. Benítez, Comprehensive characterization of the DNA amplification at 13q34 in human breast cancer reveals TFDP1 and CUL4A as likely candidate target genes,, Breast Cancer Research, 11 (2009). doi: 10.1186/bcr2456. Google Scholar

[23]

C. Palena, D. Polev, K. Tsang, et al., The human T-box mesodermal transcription factor Brachyury is a candidate target for T-cell-mediated cancer immunotherapy,, Clin. Cancer Res., 13 (2007), 2471. doi: 10.1158/1078-0432.CCR-06-2353. Google Scholar

[24]

T. Palma, A. Conti, T. Cristofaro, S. Scala, L. Nitsch and M. Zannini, Identification of novel Pax8 targets in FRTL-5 thyroid cells by gene silencing and expression microarray analysis,, PLoS ONE, 6 (2011), 1. Google Scholar

[25]

M. Palma, L. Lopez, M. García, N. de Roja, T. Ruiz, J. García, E. Rosell, C. Vela, P. Rueda and M. Rodriguez, Detection of collagen triple helix repeat containing-1 and nuclear factor (erythroid-derived 2)-like 3 in colorectal cancer,, BMC Clinical Pathology, 12 (2012), 2. doi: 10.1186/1472-6890-12-2. Google Scholar

[26]

M. Sabatino, M. Marabese, M. Ganzinelli, E. Caiola, C. Geroni and M. Broggini, Down-regulation of the nucleotide excision repair gene XPG as a new mechanism of drug resistance in human and murine cancer cells,, Molecular Cancer, 9 (2010). doi: 10.1186/1476-4598-9-259. Google Scholar

[27]

S. Scholzel, W. Zimmermann, G. Schwarzkopf, F. Grunert, B. Rogaczewski and J. Thompson, Carcinoembryonic antigen family members CEACAM6 and CEACAM7 are differentially expressed in normal tissues and oppositely deregulated in hyperplastic colorectal polyps and early adenomas,, Am. J. Pathol., 156 (2000), 595. doi: 10.1016/S0002-9440(10)64764-5. Google Scholar

[28]

E. Y. Song, H. G. Lee, Y. II Yeom, N. Y. Ji, J. W. Kim, S. Y. Kim, M. S. Won, K. S. Chung, Y. H. Kim, H. K. Chun and J. H. Kim, Diagnostic kit of colon cancer using colon cancer related marker, and diagnostic method therof,, 2010, (). Google Scholar

[29]

G. Stiglic and P. Kokol, Stability of ranked gene lists in large microarray analysis studies,, Journal of Biomedicine and Biotechnology, 2010 (2010), 556. doi: 10.1155/2010/616358. Google Scholar

[30]

S. Student and K. Fujarewicz, Stable feature selection and classification algorithms for multiclass microarray data,, Biology Direct, 7 (2012). doi: 10.1186/1745-6150-7-33. Google Scholar

[31]

J. Thompson, M. Seitz, E. Chastre, M. Ditter, C. Aldrian, C. Gespach and W. Zimmermann, Down-regulation of carcinoembryonic antigen family member 2 expression is an early event in colorectal tumorigenesis,, Cancer Research, 57 (1997), 1776. Google Scholar

[32]

V. G. Tusher, R. Tibshirani and G. Chu, Significance analysis of microarrays applied to the ionizing radiation response,, Proceedings of the National Academy of Sciences of the United States of America, 98 (2001), 5116. doi: 10.1073/pnas.091062498. Google Scholar

[33]

A. Wali, P. Morin, C. Hough, F. Lonardo, T. Seya, M. Carbone and H. Pass, Identification of intelectin overexpression in malignant pleural mesothelioma by serial analysis of gene expression (SAGE),, Lung Cancer, 48 (2005), 19. doi: 10.1016/j.lungcan.2004.10.011. Google Scholar

[34]

C. Walsh, S. Ogawa, H. Karahashi, D. Scoles, J. Pavelka, H. Tran, C. Miller, N. Kawamata, C. Ginther, J. Dering, M. Sanada, Y. Nannya, D. Slamon, P. Koeffler and B. Karlan, ERCC5 is a novel biomarker of ovarian cancer prognosis,, Journal of Clinical Oncology, 26 (2008), 2952. doi: 10.1200/JCO.2007.13.5806. Google Scholar

[35]

D. Witten and R. Tibshirani, A comparison of fold-change and the t-statistic for microarray data analysis,, Stanford University, (2007), 1. Google Scholar

[36]

J. Zhou, L. Zhang, Y. Gu, K. Li, Y. Nie, D. Fan and Y. Feng, Dynamic expression of CEACAM7 in precursor lesions of gastric carcinoma and its prognostic value in combination with CEA,, World Journal of Surgical Oncology, 9 (2011). doi: 10.1186/1477-7819-9-172. Google Scholar

[37]

, "GEDI (Genetic Diseases/Gene Discovery),", Available from: , (). Google Scholar

[38]

, "GeneCards (Human Gene Compendium),", Available from: , (). Google Scholar

[39]

, "MalaCards,", Available from: , (). Google Scholar

[40]

, " NCBI (National Center for Biotechnology Information) Gene Database,", Available from: , (). Google Scholar

[41]

, "OMIM (Online Mendelian Inheritance in Man),", Available from: , (). Google Scholar

[42]

, "The Clinical Correlation Between Scin, Cdkl1, Cugbp1, Slc16a7 With Colorectal Cancer Liver Metastasis,", 2012. Available from: , (). Google Scholar

[43]

, "USGENE BLAST Search Portal,", Available from: , (). Google Scholar

[44]

, "WikiGenes,", Available from: , (). Google Scholar

show all references

References:
[1]

C. Alvarez-Baron, P. Jonsson, C. Thomas, S. Dryer and C. Williams, The two-pore domain potassium channel KCNK5: Induction by estrogen receptor alpha and role in proliferation of breast cancer cells,, Molecular Endocrinology, 25 (2011), 1326. Google Scholar

[2]

N. Ballatori, N. Li, F. Fang, J. Boyer, W. Christian and C. Hammond, OST alpha-OST beta: A key membrane transporter of bile acids and conjugated steroids,, Frontiers in Bioscience, 14 (2009), 2829. doi: 10.2741/3416. Google Scholar

[3]

A. Boulesteix and M. Slawski, Stability and aggregation of ranked gene lists,, Briefings in Bioinformatics, 10 (2009), 556. doi: 10.1093/bib/bbp034. Google Scholar

[4]

R. Buckanovich, D. Sasaroli, A. O'Brien-Jenkins, J. Botbyl, R. Hammond, D. Katsaros, R. Sandaltzopoulos, L. Liotta, P. Gimotty and G. Coukos, Tumor vascular proteins as biomarkers in ovarian cancer,, Journal of Clinical Oncology, 25 (2007), 852. doi: 10.1200/JCO.2006.08.8583. Google Scholar

[5]

V. Catalán, J. Gómez-Ambrosi, A. Rodríguez, B. Ramírez, F. Rotellar, V. Valentí, C. Silva, M. Gil, J. Salvador and G. Frühbeck, Up-regulation of the novel proinflammatory adipokines lipocalin-2, chitinase-3 like-1 and osteopontin as well as angiogenic-related factors in visceral adipose tissue of patients with colon cancer,, The Journal of Nutritional Biochemistry, 22 (2011), 634. Google Scholar

[6]

F. Coffman, Chitinase 3-Like-1 (CHI3L1): A putative disease marker at the interface of proteomics and glycomics,, Critical Reviews in Clinical Laboratory Sciences, 45 (2008), 531. doi: 10.1080/10408360802334743. Google Scholar

[7]

X. Deng, J. Xu, J. Hui and C. Wang, Probability fold change: A robust computational approach for identifying differentially expressed gene lists,, Computer Methods and Programs in Biomedicine, 93 (2009), 124. doi: 10.1016/j.cmpb.2008.07.013. Google Scholar

[8]

S. Dudoit, J. Fridlyand and T. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data,, Journal of the American Statistical Association, 97 (2002), 77. doi: 10.1198/016214502753479248. Google Scholar

[9]

S. Dudoit and R. Gentleman, "Bioconductor Short Course,", 2003. Available from: , (). Google Scholar

[10]

T. J. Farr, S. J. Coddington-Lawson, P. M. Snyder and F. J. McDonald, Human Nedd4 interacts with the human epithelial Na+ channel: WW3 but not WW1 binds to Na+-channel subunits,, The Biochemical Journal, 345 (2000), 503. doi: 10.1042/0264-6021:3450503. Google Scholar

[11]

K. Fujarewicz, et al, A multigene approach to differentiate papillary thyroid carcinoma from benign lesions: Gene selection using bootstrap-based support vector machines,, Endocrine - Related Cancer, 14 (2007), 809. Google Scholar

[12]

K. Fujarewicz, M. Kimmel and J. Rzeszowska-Wolny, Improved classification of microarray gene expression data using support vector machines,, Journal of Medical Informatics and Technologies, 2 (2001). Google Scholar

[13]

K. Fujarewicz and M. Wiench, Selecting differencially expressed genes for colon tumor classification,, International Journal of Applied Mathematics and Computer Science, 13 (2003), 327. Google Scholar

[14]

J. Harvey, A. Gannon, Z. Li, C. Beard and C. Burgess, Identification of a novel methylation marker, SCNN1B,, AACR Meeting Abstracts, (2005). Google Scholar

[15]

T. Hastie, R. Tibshirani and J. Friedman, "The Elements of Statistical Learning. Data Mining, Inference, and Prediction,", 2nd edition, (2009). doi: 10.1007/978-0-387-84858-7. Google Scholar

[16]

M. Irigoyen, M. Pajares, J. Agorreta, M. Ponz-Sarvisé E. Salvo, M. Lozano, R. Pío, I. Gil-Bazo and A. Rouzaut, TGFBI expression is associated with a better response to chemotherapy in NSCLC,, Molecular Cancer, 9 (2010). doi: 10.1186/1476-4598-9-130. Google Scholar

[17]

B. Jarzçab, M. Wiench, K. Fujarewicz, K. Simek, M. Jarzçab, M. Oczko-Wojciechowska, J. Włoch, A. Czarniecki, E. Chmielik, D. Lange, A. Pawlaczek, S. Szpak, E. Gubała and A. Świerniak, Gene expression profile of papillary thyroid Ccncer: sources of variability and diagnostic implications,, Cancer Research, 65 (2005), 1587. Google Scholar

[18]

G. Jurman, S. Merler, A. Barla, S. Paoli, A. Galea and C. Furlanello, Algebraic stability indicators for ranked lists in molecular profiling,, Bioinformatics, 24 (2008), 258. doi: 10.1093/bioinformatics/btm550. Google Scholar

[19]

M. Kawada, H. Seno, K. Kanda, Y. Nakanishi, R. Akitake, H. Komekado, K. Kawada, Y. Sakai, E. Mizoguchi and T. Chiba, Chitinase 3-like 1 promotes macrophage recruitment and angiogenesis in colorectal cancer,, Oncogene, 31 (2012), 3111. doi: 10.1038/onc.2011.498. Google Scholar

[20]

C. Lai, M. Reinders, L. Veer and L. Wessels, A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets,, BMC Bioinformatics, 7 (2006), 235. Google Scholar

[21]

C. Ma, Y. Rong, D. Radiloff, M. Datto, B. Centeno, S. Bao, A. Cheng, F. Lin, S. Jiang, T. Yeatman and X Wang, Extracellular matrix protein betaig-h3/TGFBI promotes metastasis of colon cancer by enhancing cell extravasation,, Genes Dev., 22 (2008), 308. Google Scholar

[22]

L. Melchor, L. Saucedo-Cuevas, I. Munoz-Repeto, S. Rodrģuez-Pinilla, E. Honrado, A. Campoverde, J. Palacios, K. Nathanson, M. García and J. Benítez, Comprehensive characterization of the DNA amplification at 13q34 in human breast cancer reveals TFDP1 and CUL4A as likely candidate target genes,, Breast Cancer Research, 11 (2009). doi: 10.1186/bcr2456. Google Scholar

[23]

C. Palena, D. Polev, K. Tsang, et al., The human T-box mesodermal transcription factor Brachyury is a candidate target for T-cell-mediated cancer immunotherapy,, Clin. Cancer Res., 13 (2007), 2471. doi: 10.1158/1078-0432.CCR-06-2353. Google Scholar

[24]

T. Palma, A. Conti, T. Cristofaro, S. Scala, L. Nitsch and M. Zannini, Identification of novel Pax8 targets in FRTL-5 thyroid cells by gene silencing and expression microarray analysis,, PLoS ONE, 6 (2011), 1. Google Scholar

[25]

M. Palma, L. Lopez, M. García, N. de Roja, T. Ruiz, J. García, E. Rosell, C. Vela, P. Rueda and M. Rodriguez, Detection of collagen triple helix repeat containing-1 and nuclear factor (erythroid-derived 2)-like 3 in colorectal cancer,, BMC Clinical Pathology, 12 (2012), 2. doi: 10.1186/1472-6890-12-2. Google Scholar

[26]

M. Sabatino, M. Marabese, M. Ganzinelli, E. Caiola, C. Geroni and M. Broggini, Down-regulation of the nucleotide excision repair gene XPG as a new mechanism of drug resistance in human and murine cancer cells,, Molecular Cancer, 9 (2010). doi: 10.1186/1476-4598-9-259. Google Scholar

[27]

S. Scholzel, W. Zimmermann, G. Schwarzkopf, F. Grunert, B. Rogaczewski and J. Thompson, Carcinoembryonic antigen family members CEACAM6 and CEACAM7 are differentially expressed in normal tissues and oppositely deregulated in hyperplastic colorectal polyps and early adenomas,, Am. J. Pathol., 156 (2000), 595. doi: 10.1016/S0002-9440(10)64764-5. Google Scholar

[28]

E. Y. Song, H. G. Lee, Y. II Yeom, N. Y. Ji, J. W. Kim, S. Y. Kim, M. S. Won, K. S. Chung, Y. H. Kim, H. K. Chun and J. H. Kim, Diagnostic kit of colon cancer using colon cancer related marker, and diagnostic method therof,, 2010, (). Google Scholar

[29]

G. Stiglic and P. Kokol, Stability of ranked gene lists in large microarray analysis studies,, Journal of Biomedicine and Biotechnology, 2010 (2010), 556. doi: 10.1155/2010/616358. Google Scholar

[30]

S. Student and K. Fujarewicz, Stable feature selection and classification algorithms for multiclass microarray data,, Biology Direct, 7 (2012). doi: 10.1186/1745-6150-7-33. Google Scholar

[31]

J. Thompson, M. Seitz, E. Chastre, M. Ditter, C. Aldrian, C. Gespach and W. Zimmermann, Down-regulation of carcinoembryonic antigen family member 2 expression is an early event in colorectal tumorigenesis,, Cancer Research, 57 (1997), 1776. Google Scholar

[32]

V. G. Tusher, R. Tibshirani and G. Chu, Significance analysis of microarrays applied to the ionizing radiation response,, Proceedings of the National Academy of Sciences of the United States of America, 98 (2001), 5116. doi: 10.1073/pnas.091062498. Google Scholar

[33]

A. Wali, P. Morin, C. Hough, F. Lonardo, T. Seya, M. Carbone and H. Pass, Identification of intelectin overexpression in malignant pleural mesothelioma by serial analysis of gene expression (SAGE),, Lung Cancer, 48 (2005), 19. doi: 10.1016/j.lungcan.2004.10.011. Google Scholar

[34]

C. Walsh, S. Ogawa, H. Karahashi, D. Scoles, J. Pavelka, H. Tran, C. Miller, N. Kawamata, C. Ginther, J. Dering, M. Sanada, Y. Nannya, D. Slamon, P. Koeffler and B. Karlan, ERCC5 is a novel biomarker of ovarian cancer prognosis,, Journal of Clinical Oncology, 26 (2008), 2952. doi: 10.1200/JCO.2007.13.5806. Google Scholar

[35]

D. Witten and R. Tibshirani, A comparison of fold-change and the t-statistic for microarray data analysis,, Stanford University, (2007), 1. Google Scholar

[36]

J. Zhou, L. Zhang, Y. Gu, K. Li, Y. Nie, D. Fan and Y. Feng, Dynamic expression of CEACAM7 in precursor lesions of gastric carcinoma and its prognostic value in combination with CEA,, World Journal of Surgical Oncology, 9 (2011). doi: 10.1186/1477-7819-9-172. Google Scholar

[37]

, "GEDI (Genetic Diseases/Gene Discovery),", Available from: , (). Google Scholar

[38]

, "GeneCards (Human Gene Compendium),", Available from: , (). Google Scholar

[39]

, "MalaCards,", Available from: , (). Google Scholar

[40]

, " NCBI (National Center for Biotechnology Information) Gene Database,", Available from: , (). Google Scholar

[41]

, "OMIM (Online Mendelian Inheritance in Man),", Available from: , (). Google Scholar

[42]

, "The Clinical Correlation Between Scin, Cdkl1, Cugbp1, Slc16a7 With Colorectal Cancer Liver Metastasis,", 2012. Available from: , (). Google Scholar

[43]

, "USGENE BLAST Search Portal,", Available from: , (). Google Scholar

[44]

, "WikiGenes,", Available from: , (). Google Scholar

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