Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.
Citation: Wang A, Du Y, He Q, Zhou C (2013) A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells. PLoS ONE 8(2): e56095. https://doi.org/10.1371/journal.pone.0056095
Editor: Rajasingh Johnson, University of Kansas Medical Center, United States of America
Received: October 12, 2012; Accepted: January 7, 2013; Published: February 13, 2013
Copyright: © 2013 Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: These authors have no support or funding to report.
Competing interests: The authors have declared that no competing interests exist.
Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) provide important resources for regenerative medicine and medical research , , , , . Given the potential of these stem cell lines, an accurate system to discriminate the cell lines is required. However, such a discriminant system remains to be developed.
Traditionally, biomarkers derived from well-characterized individual molecules have been used to distinguish somatic cells (SCs) from pluripotent cells (PCs), including iPSCs and ESCs , . PCR and immunostaining can be used to improve the ability of biomarkers to distinguish SCs from PCs . However, instabilities within inherent multipotent cell lines due to varying conditions may produce inaccurate results . For examples, the OCT4 biomarker, which was once thought to be an excellent marker for discriminating ESCs from SCs, is only transitionally expressed in ESCs and is not consistently expressed in different ESCs, especially in old ESCs . Any single biomarker selected from a very limited number of samples is unlikely to be robust enough to classify novel stem cells when applied alone across different conditions . In addition, most of the current antibody-based biomarkers will fail to detect low abundance protein signals, and thus exhibit low sensitivity.
Discriminating ESCs from iPSCs is challenging due to their similarity. Cluster analysis and meta-analyses of genome-wide gene expression data sets can circumvent sample size limitations and generate the unbiased signatures needed to classify ESCs . A combination of linear models and gene expression profiling can also be used to classify PCs and SCs . However, the gene signatures cannot be used to distinguish iPSCs and ESCs because the gene signatures are not consistently expressed across different cell lines and conditions , . The gene expression profiling of iPSCs could be lab-specific when the batch effect was inappropriately adjusted , . Furthermore, linear models and clustering analyses are associated with a low sensitivity in determining classification. In addition, they are not the optimal data classification mode in the presence of an abnormal distribution and different resources . Thus, the need for a system that overcomes these challenges and is able to discriminate all three cell types remains.
In contrast to gene expression, DNA methylation consistently varies between iPSCs and ESCs under different conditions , , . This suggests that signatures based on DNA methylation could be used as biomarkers to discriminate iPSCs and ESCs. In addition, SCs express distinct DNA methylation patterns compared to PCs . Thus, DNA methylation-based biomarkers could provide a promising manner to discriminate among cell lines.
Applying mathematical models can accurately discriminate biological samples , , . Systems embedded with mathematical models and trained with large sample sizes can predict unknown samples. Among mathematical models, artificial neural network (NNET)  and support vector machines (SVM)  are frequently employed in biological discriminations . NNET is a form of machine learning and non-linear statistical data modeling which processes data using a connectionism approach through an interconnected group of artificial neurons . In most cases, NNET adjusts its structure during the learning phase according to external or internal information flowing through the network . Therefore, NNET is able to cope with noisy and highly dimensional datasets . Similarly, SVM can discriminate complex samples as we previously reported .
In this study we systematically selected biomarkers by an eigengene score , which was calculated from global methylation profiling, allowing us to establish a quantitative system with mathematical models (i.e. NNET and SVM) to discriminate iPSCs, ESCs and SCs.
DNA methylation profiling of iPSCs, ESCs and SCs
To investigate the DNA methylation profiling differentially expressed in iPSCs, ESCs and SCs, we analyzed genome-wide microarray profiling of these three cell types. In order to avoid cell-line-specific DNA methylation signatures and to develop a general system to discriminate the three cell types, we downloaded and analyzed a large set of data that contains as various sources as possible when they are relative to the three cell types (Table S1, materials and methods). A total of 636 microarrays were used in this study, including 55% SCs, 18% iPSCs, and 27% ESCs (Table S1). Various cell sources were included, such as male and female iPSCs, fetal and adult somatic cells, various tissues, fibroblasts, iPSC-derived and ESCs-derived somatic cells, fibroblast-derived iPSCs, ESCs-somatic cell-derived iPSCs, and epithelial cell-derived iPSCs.
Unsupervised cluster analysis and correspondence analysis of these samples revealed that SCs are clearly separated from PCs, including iPSCs and ESCs. In addition, most ESCs can be separated from iPSCs (Figure 1A). While SCs are separated from PCs in the first correspondence component, ESCs were somewhat different from iPSCs in respect to the correspondence components 2 and 3 (Figure 1B and 1C). This is consistent with the recent finding that iPSCs express distinct methylation profiling compared to ESCs  and suggests that DNA methylation could be used as a variable to select biomarkers for distinguishing cell types.
All methylation sites measured by microarray were used to profile the overall methylation patterns of the three cell types, iPSCs, ESCs, and SCs. A, unsupervised clustering analysis revealed that SCs were separated from PCs (iPSCs and ESCs). In the PCs subgroup, most ESCs were separated from iPSCs. B, Correspondence analysis classified three cell types, SCs, iPSCs, and ESCs. SCs and PCs were separated in first component while most of ESCs and iPSCs were separated in second component. C, iPSCs and ESCs were further classified by correspondence analysis in 3D space. For visualization purposes, only one subset of data was shown here.
DNA methylation biomarker selection
To improve the quantitative performance of our system, we selected biomarkers that contribute the most variance in this system. This ensures that selected biomarkers capture the primary features of data. Instead of using traditional approaches based on differential analysis, we employed an eigengene ranking system derived from principal component analysis (PCA) to circumvent the correlations of gene methylations . To be conservative and consistent, we selected biomarkers from one abundant platform (illumina methylation 27K, GPL8490). The data from other platforms, including next-generation sequencing, was used for testing (materials and methods).
We ranked all methylation loci by the eigengene score and selected the top ∼200 methylation sites as biomarkers for each comparison group (Table 1, Table S2, S3). Interestingly, we found two groups of biomarkers for discriminating iPSCs from ESCs. Both groups are important in variance contributions and they are distributed in two separate PCA components. Biologically, one group is located in autosomes and another in the X-chromosome (Table S3, S4).
A quantitative system discriminating iPSCs, ESCs and SCs
To establish a quantitative system for discriminating iPSCs, ESCs and SCs, we employed two types of mathematical models: artificial neural network (NNET) and support vector machines (SVM). We ran the above models with our data filtered by biomarkers. In both models, we measured the percentage of correction rate and kappa coefficient, which is a statistical measure of inter-rater agreement for quantitative items.
To determine the optimal biomarker number for discriminating SCs from PCs, we ran both NNET and SVM by using a series of marker sets, which follow the order listed in table 1 and Table S2. In each marker set, all samples were randomly sampled 200 times. In each run, 70% of random samples worked for training and the remaining 30% for testing  (materials and methods). The accuracy of the 200 runs for each marker set was calculated.
With approximately 20 markers, both NNET and SVM discriminated SCs from PCs with an average percentage and kappa of 1.0 and 1.0, respectively (Figure 2A). Even with 3 markers (cg03273615, cg18201077, cg20217872), both SVM and NNET could successfully and accurately discriminate these two cell types with an average percentage and kappa of approximately 1.0 and 1.0, respectively (Figure 2A). After approximately 30 markers were applied, the system achieved a static state. This stable state suggests that 30 markers might be sufficient to discriminate SCs from PCs.
Accuracy was measured as kappa value and accuracy percentage, shown on the Y-axis. The top panel A represents SCs discriminating from PCs and the bottom panel B represents ESCs from iPSCs. The X-axis represents marker number, from 1 marker to 50 markers in SCs versus PCs panel (top), and from 1 to 200 markers in ESCs versus iPSCs panel (bottom B). Only data for 50 and 200 markers for these two groups are shown because the system became a static state after that level.
Similarly, we also applied the above approach to discriminate ESCs from iPSCs using two group markers, an autosomal group and a X-chromosomal group (Table S3, S4). The autosomal group starts with a 0.75 percentage and 0.4 kappa in both NNET and SVM. A stable state is reached with a 0.95 percentage and 0.9 kappa with approximately 100 markers (Figure 2B). With 75 markers, the system reaches ∼90% accuracy (Figure 2B). The X-chromosomal group begins with 0.6 percentage and 0.1 kappa and requires more than 300 markers to reach 87 percentage and 0.6 kappa value. It seemed that more biomarkers are required to reach higher accuracy and to achieve system stability (Figure S1). Our study indicated that the autosomal group performed better than x-chromosomal group and that SVM and NNET performed similarly in our biomarker sets. Thereafter we used the autosomal group as the analysis in this study. This result indicates that discriminating iPSCs and ESCs requires at least 100 biomarkers. This also suggests that the sample sources of iPSCs and ESCs are very heterogeneous, leading to the consequence that more biomarkers (>100) are required to make the system robust and stable. Together, our system, which includes mathematical models (SVM and NNET) and DNA methylation markers, can successfully discriminate three cell types, SCs, iPSCs, and ESCs. This also suggests that DNA methylation variations exist among the three cell types.
The system can be expended for general methylation measurement
Methylation data measured by traditional experiments like bisulfite conversion counting are usually presented as a discrete percentage. Discrete percentages are highly correlated with beta values that come from microarray data as evidenced by a high correlation in beta values and percentage methylation levels measured by genome-wide bisulfite sequencing of ESCs  (Figure 3A). To make our system more applicable to biological experiments, we converted the beta value to a discrete percentage level as listed in the following pairs, beta value/percentage >0.90/100, 0.9/90, 0.85/80, 0.8/70, 0.75/60, 0.7/40, 0.6/25, 0.55/20, 0.5/10, 0.4/5, 0.3/2, 0.17/1, and <0.17/0.
A, a high correction relationship exists between methylation percentage measured from sequencing and the beta value measured from Illumina microarray. B, Our system discriminates the three cell types with high accuracy with converted data. For visualization purposes, only percentage of NNET was shown here due to its similarity with SVM and the high correlation between accuracy percentage and kappa. This practice was also applied to following figures in this study.
Using the conversion data, our system has a comparable performance with unconverted data, and it reaches 100% and 90% accuracy with 30 markers and 100 markers respectively for discriminating SCs from PCs and iPSCs from ESCs (Figure 3). The high accuracy with converted and unconverted data suggests that our system can be used as a generalized application.
Robustness and validation
To further investigate the robustness of this system, we tested this system using raw data without global normalization (materials and methods). This system surprisingly works similarly to that with normalized data. With 30 and 75 markers for discriminating SCs from PCs and ESCs from iPSCs, our system reaches 100% and 90% accuracy, respectively (Figure 4).
Our system reaches the similar discriminating power as that with normalized data.
We validated this system using different platforms and resources including data from two new platforms, Illumna 450K (GPL13534) (materials and methods) and next-generation sequencing , and data generated by another research group looking at aging whose focus was unrelated to stem cell research . The performance of our system was tested on each platform or resource. In the aging group , only SCs are available. All data was run with at least 20 sets of biomarkers; at least 30 to 50 markers were used for discriminating SCs from PCs and 170 to 200 markers were used for discriminating ESCs from iPSCs (materials and methods). This system can 100% correctly predict SCs from PCs under all conditions, while it discriminates ESCs from iPSCs with ∼90% of accuracy (Table 2). The accuracy rates suggest that this system is very robust and predictive.
Cell subtype discrimination
Distinguished DNA methylation patterns have been observed in subtypes of cells, such as the subtype of fetal and adult somatic cells and the subtype of female and male iPSCs . Correspondence analysis of DNA methylation levels also demonstrated that female iPSCs clearly separate from male iPSCs (Figure 5A) and adult SCs separate from fetal SCs (Figure 5B). This suggests that DNA methylation could be used to select biomarkers for discriminating cell subtypes. We used the same strategy described above to select DNA methylation biomarkers for discriminating two subtypes, iPSCs male and female subtypes and SCs fetal and adult subtypes (Table S5, S6). Mathematical models with these biomarkers demonstrated that the accuracy of discriminating female iPSCs from male iPSCs reaches 100% accuracy when using 2 biomarkers (Figure 5C, Table S5). When discriminating the adult SCs and fetal SCs, 95% accuracy is reached with 80 biomarkers (Figure 5D). The high level of accuracy indicates that our system could be extended to identify the cell subtypes.
A and B denote correspondence analysis to classify subtypes of cells. A, the subtype of female and male iPSCs. B, the subtype of fetal and adult SCs. C and D show the accuracy of discriminating subtypes of cells. C, the subtype of female and male iPSCs. D, the subtype of fetal and adult SCs.
For the first time, this study establishes a general quantitative system based on DNA-methylation markers to discriminate three cell types, iPSCs, ESCs, and SCs. Conventional methods like the OCT4 based method to distinguish ESCs from SCs have limitations and may not be efficient. Currently, there is no way to discriminate iPSCs from ESCs due to their similarity.
SCs are obviously different from PCs, which include iPSCs and ESCs. SCs exhibit DNA methylation patterns that can be distinguished from PCs in all observed conditions thus far , . It is therefore reasonable to use DNA methylation as a variable to discriminate SCs from PCs; however this system does not exist to date. In contrast, iPSCs closely mimic ESCs in many aspects such as colony morphology, even gene expressions and microRNA profiling , , , , , , . Although previous studies showed that iPSCs generated from single cell resources display DNA methylation variations compared with ESCs , , , these variations could be cell-type specific and condition-dependent due to the limitation of its sample-size and the pure sample-resources. Here, we collected a large dataset including various cell line sources and conditions (Table S1) to determine if the DNA methylation pattern varied between iPSCs and ESCs (Figure 1). The DNA methylation pattern revealed that iPSCs generally exhibit certain variations compared to ESCs, regardless of their originality and conditions. Therefore, DNA methylation could be used to select biomarkers for discriminating iPSCs from ESCs.
Biomarker selections should consider two major aspects: generalizability of the sample and method efficiency. Condition-specific samples  like cell-line specific samples  could bias biomarker selections. To make our system generalizable, we minimized the cell-line bias selection and included various cell line sources (Table S1), such as different cell originality and gender. Methods based on differential values are usually employed to select biomarkers; however, these methods focus only on the significant differences between variables and fail to avoid variable correlations and redundant information from the multiple dimensional microarray data. Thus, these conventional approaches could harass biomarker selections . We selected the biomarkers by adopting the unbiased eigengene selection approach as we previously reported  (materials and methods). Eigengene-based selection takes care of the variable correlations and the redundant information of multiple variables, and it selects the independent elements that contribute to most of the variances in the entire dataset. All conditions like cell originality and other conditions have been taken into account and variations of conditions and cell-originalities have been reflected in the variance contributions. Thus, the selected biomarkers should be the most generalizable and the most important ones in this system. A quantitative system based on these selected biomarkers should perform better than that one based on biomarkers selected from differential comparisons. Indeed, while not reported here, we found that a system based on the differential methylation performed poorer than our system reported here in term of discriminant accuracy. Therefore, the way that we employed here to select biomarkers is efficient and the biomarkers selected from general data including various sources should be of general properties.
The sensitivity is of most concern for biomarker system development , , . Conventionally, methods based on PCR or immunochemistry with a single biomarker like OCT4 have been frequently used in medical researches for distinguishing ESCs , , but it is unlikely for these traditional approaches to provide a sensitive system to discriminate all cell types under all conditions given the substantial heterogeneity among the cell types . Clustering analysis based on gene expression signatures was proposed to classify ESCs ; however its use is severely limited by the natural low accuracy associated with cluster analysis and the numerous signatures involved in the clustering. Ideally, a simple system should be developed, including a small panel of biomarkers that are easy measured and a mathematical model that quantitatively performs a sensitive judgment. However, it is challenging to assemble and validate such a biomarker panel. Here, we employed a machine learning system based on NNET and SVM to systematically and quantitatively validate a panel of ∼200 biomarkers for each group (Figure 2). NNET and SVM, combined with dimension-reduced approaches like principal component analysis, are advantageous when handling non-linear functions for nosey multiple dimension data and have been successfully applied in discriminating disease cell lines and molecular complexity , . NNET and SVM with as few as 3 biomarkers for determining SCs from PCs and with 100 markers for determining iPSCs from ESCs can discriminate the three cell types with 100% and 95% accuracy respectively for two groups (Figure 2). This suggests that our system is the most sensitive system to discriminate the three cell types to date.
Robustness and prediction value are also of concern in developing discriminant system , , . Conventional approaches such as PCR, immunostaining and clustering analysis are of low robustness and prediction value. We tested our system with raw data without normalization and with discrete methylation percentage data converted from continuous variables measured from microarray, and we found that our system 100% and 90% correctly discriminates SCs from PCs and iPSCs from ESCs, respectively (Figure 3 and Figure 4). When validated by new samples generated from other independent groups and even from different microarray platform and next-generation high throughput sequencing data, our system continued to correctly predict 100% SCs and 90% of iPSCs from ESCs (Table 2). Thus, this system established here is very robust and can be generally applied to discriminate the three cells types in medical research.
Furthermore, Nazor et al. recently revealed the distinguished DNA methylation patterns existing in the subtypes of cells , such as the subtype of female against male iPSCs, and the subtype of fetal versus adult SCs. This suggested that DNA methylation could be used as a variable to discriminate the subtypes of cells. We extended our system to discriminate the subtypes of cells, and our system reached 100% accuracy in discriminating female and male iPSCs with only 2 markers and it got 95% accuracy in classifying adult and fetal SCs (Figure 5). This indicated that the DNA methylation difference between female and male iPSCs is so obvious that we actually do not need more biomarkers to discriminate them. In contrast, this system requires as many as 80 biomarkers to reach 95% accuracy when discriminating fetal and adult SCs. Concerning the heterogeneity of SCs, which contained various tissues with tissue-specific methylation loci , it is reasonable and very promising (95% accuracy) when discriminating them. Therefore, our system could be reasonably extended to discriminate other subtypes of cells when more data is available.
The methylation data for our biomarkers can be measured using traditional methods, and the measurement is less expensive than microarray and antibody-based immunochemical approach. Therefore, the system developed here is a cost effective, accurate and reliable system to distinguish three cell types. This approach provides a foundation to develop other discriminant systems.
Materials and Methods
DNA methylation data and processing
All 636 methylation microarray data were downloaded from GEO database (www.ncbi.nlm.nih.gov/geo/) (Table S1). The methylation data was preprocessed by GenomeStudio (http://www.illumina.com/gsp/genomestudio_software.ilmn) and then processed using R (http://www.r-project.org/). All methylation values measured by microarray were presented as beta value, ranging from 0 to 1. Normalizations were performed using quantile normalization . Before further analysis, outliers were filtered out by three ways of QC checks, X-chromosome beta value distribution , CpG methylation distribution  and the euclidean distance from samples to the group center. After outliers were filtered, only 312 out of 399 microarrays generated from the platform GPL8490 (Illumina 27k) were available for biomarker selection. Because the microarray data were not generated at the same time, the batch effect needs to be filtered out before combining the microarray datasets. An algorithm called ComBat , which runs in R environment and uses parametric and nonparametric empirical Bayes frameworks to adjust microarray data for batch effects, was used to adjust the final methylation values for all datasets.
Biomarkers were selected based on an eigengene score, which was defined below as we previously reported .
|Cor(xi, E)| is the absolute value of Pearson correlation coefficient, where xi is a vector of methylation of ith node, and E eigenvalue derived from principal component analysis.
Artificial neural networks and support vector machines
Mathematical models from NNET packages in R were used to perform artificial neural network (NNET), with range = 0.1, decay = 5e-4, and maxit = 200. NNET is machine learning mathematical model that is designed to emulate the architecture of the brain . In NNET, data is processed by neurons that are organized in parallel layers: input, hidden, and output. The neurons of the input layer receive data as a methylation value and transmit the input data into the hidden layer neurons that process the data using mathematical functions. The processed results are displayed into the output layer neurons. The output neuron with largest value in output layer will be the group that input neuron (either iPSCs, ESCs, or SCs) should be.
We used SVM as we previously reported . Briefly, SVM classifies datasets based on hyperplanes in which samples can be clustered with the largest separated distances. The R package e1071 was used in this study. Each run, the parameters were optimal. The best gamma and best cost, and radial kernel were finally used for discriminating the test set of samples.
For both NNET and SVM, we randomly sampled 200 times for each biomarker set, from 1 biomarker to 200 biomarkers, and we used 70% of the samples as a training set and the remaining 30% as test data. The accuracy was calculated from the test data set by measuring both average percentage correct rate and kappa value.
Validation and prediction
During validation and prediction, microarray data were calculated from 0 to 1 number in beta value format from Illumina Inc, without removing batch effect and without further normalization. All 27k platform data used for biomarker selections was treated as training data, and the samples from 45k platform (GPL13534), the next-generation sequencing , and the samples from the aging study  were used as separate testing sets. The testing samples were randomly sampled 200 times, each time using 90% of the samples as input for calculating the accuracy. At least 20 biomarker sets were run for each testing set, utilizing 30 to 50 markers to determine SCs from PCs and 170 to 200 markers to determine ESCs from iPSCs. Only markers that overlapped between training and testing data sets were used for each run. In the sequencing data, we used the read counts from methylation sites generated from sequencing data that was further converted them to the range of 0 and 1 in basis of the beta-percentage relation curve present in figure 3A.
Microarray info. All data were downloaded and the detail were shown below.
Biomarkers for SCs and PCs. These biomarkers were selected by score from all filtered array as described in materials and methods.
Biomarkers for ESCs and iPSCs. Please refer to Table S2.
ChrX biomarkers for ESCs and iPSCs. Please refer to Table S2.
Biomarkers for female and male iPSCs. Please refer to Table S2.
Biomarkers for fetal and adult somatic cells. Please refer to Table S2.
We specially thank Drs. Amanda Jackson and Joshua Kilgore for help editing this paper. No competing interest exists in this paper.
Conceived and designed the experiments: AW. Performed the experiments: AW. Analyzed the data: AW. Contributed reagents/materials/analysis tools: AW YD QH CZ. Wrote the paper: AW.
- 1. Yamanaka S (2009) Shinya (2009) A Fresh Look at iPS Cells. Cell 137: 13–17.
- 2. Yamanaka S (2009) Elite and stochastic models for induced pluripotent stem cell generation. Nature 460: 49–52.
- 3. Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126: 663–676.
- 4. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, et al. (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131: 861–872.
- 5. Yu J, Vodyanik MA, Smuga-Otto K, Antosiewicz-Bourget J, Frane JL, et al. (2007) Induced pluripotent stem cell lines derived from human somatic cells. Science 318: 1917–1920.
- 6. Carpenter MK, Rosler E, Rao MS (2003) Characterization and differentiation of human embryonic stem cells. Cloning Stem Cells 5: 79–88.
- 7. Goldman B (2008) Magic marker myths. Nature Reports Stem Cells .
- 8. Muller FJ, Laurent LC, Kostka D, Ulitsky I, Williams R, et al. (2008) Regulatory networks define phenotypic classes of human stem cell lines. Nature 455: 401–405.
- 9. Muller FJ, Schuldt BM, Williams R, Mason D, Altun G, et al. (2011) A bioinformatic assay for pluripotency in human cells. Nat Methods 8: 315–317.
- 10. Newman AM, Cooper JB (2010) Lab-specific gene expression signatures in pluripotent stem cells. Cell Stem Cell 7: 258–262.
- 11. Guenther MG, Frampton GM, Soldner F, Hockemeyer D, Mitalipova M, et al. (2010) Chromatin structure and gene expression programs of human embryonic and induced pluripotent stem cells. Cell Stem Cell 7: 249–257.
- 12. Wang A, Huang K, Shen Y, Xue Z, Cai C, et al. (2011) Functional modules distinguish human induced pluripotent stem cells from embryonic stem cells. Stem Cells Dev 20: 1937–1950.
- 13. Goldstein DR GD, Conlon EM (2002) Statistical issues in the clustering of gene expression data. Statist Sinica 12: 219–240.
- 14. Kim K, Doi A, Wen B, Ng K, Zhao R, et al. (2010) Epigenetic memory in induced pluripotent stem cells. Nature 467: 285–290.
- 15. Polo JM, Liu S, Figueroa ME, Kulalert W, Eminli S, et al. (2010) Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nat Biotechnol 28: 848–855.
- 16. Lister R, Pelizzola M, Kida YS, Hawkins RD, Nery JR, et al. (2011) Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471: 68–73.
- 17. Nazor KL, Altun G, Lynch C, Tran H, Harness JV, et al. (2012) Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives. Cell Stem Cell 10: 620–634.
- 18. Lancashire LJ, Lemetre C, Ball GR (2009) An introduction to artificial neural networks in bioinformatics–application to complex microarray and mass spectrometry datasets in cancer studies. Brief Bioinform 10: 315–329.
- 19. Oshima Y, Shinzawa H, Takenaka T, Furihata C, Sato H (2010) Discrimination analysis of human lung cancer cells associated with histological type and malignancy using Raman spectroscopy. J Biomed Opt 15: 017009.
- 20. Han M, Dai J, Zhang Y, Lin Q, Jiang M, et al. (2012) Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer. Oncol Rep 28 (6) 2233–2238.
- 21. Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, et al. (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462: 315–322.
- 22. Bork S, Pfister S, Witt H, Horn P, Korn B, et al. (2010) DNA methylation pattern changes upon long-term culture and aging of human mesenchymal stromal cells. Aging Cell 9: 54–63.
- 23. Doi A, Park IH, Wen B, Murakami P, Aryee MJ, et al. (2009) Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat Genet 41: 1350–1353.
- 24. Chin MH, Mason MJ, Xie W, Volinia S, Singer M, et al. (2009) Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 5: 111–123.
- 25. Sawyers CL (2008) The cancer biomarker problem. Nature 452: 548–552.
- 26. Shen R, Ghosh D, Chinnaiyan A, Meng Z (2006) Eigengene-based linear discriminant model for tumor classification using gene expression microarray data. Bioinformatics 22: 2635–2642.
- 27. Jones R (2010) Biomarkers: casting the net wide. Nature 466: S11–S12.
- 28. Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24: 1547–1548.
- 29. Davis S, Du P, Bilke S, Triche T Jr, Bootwalla M (2012) methylumi: Handle Illumina methylation data. R package version 220
- 30. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8: 118–127.
- 31. Rolston DW (1988) Principles of Artificial Intelligence and Expert Systems Development. New York: McGraw-Hill Book Company.